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Dispatches from PIDapalooza

Last month, California Digital Library, ORCID, Crossref, and Datacite brought together the brightest minds in scholarly infrastructure to do the impossible: make a conference on persistent identifiers fun!

Usually discussions about persistent identifiers (PIDs) and networked research are dry and hard to get through or we find ourselves discussing the basics and never getting to the meat.

We designed PIDapalooza to attract kindred spirits who are passionate about improving interoperability and the overall quality of our scholarly infrastructure. We knew if we built it, they would come!

The results were fantastic and there was a great showing from the University of California community:

• Stephanie Simms from CDL discussed the need for PIDified Data Management Plans.
• Cory Craig from UC Davis proposed ways to tackle the complexity of ORCIDs in hyper-authored articles.
• Tom Gillespie from UCSD outlined the need for PIDs in scientific protocols.
• John Kunze from CDL offered a draft vocabulary for “persistence statements”.

All PIDapalooza presentations are being archived on Figshare: https:/pidapalooza.figshare.com

Take a look and make sure you are following @pidapalooza for word on future PID fun!

There’s a New Dash!

Dash: an open source, community approach to data publication

We have great news! Last week we refreshed our Dash data publication service.  For those of you who don’t know, Dash is an open source, community driven project that takes a unique approach to data publication and digital preservation.

Dash focuses on search, presentation, and discovery and delegates the responsibility for the data preservation function to the underlying repository with which it is integrated. It is a project based at the University of California Curation Center (UC3), a program at California Digital Library (CDL) that aims to develop interdisciplinary research data infrastructure.

Dash employs a multi-tenancy user interface; providing partners with extensive opportunities for local branding and customization, use of existing campus login credentials, and, importantly, offering the Dash service under a tenant-specific URL, an important consideration helping to drive adoption. We welcome collaborations with other organizations wishing to provide a simple, intuitive data publication service on top of more cumbersome legacy systems.

There are currently seven live instances of Dash: – UC Berkeley – UC Irvine – UC Merced – UC Office of the President – UC Riverside – UC Santa Cruz – UC San Francisco – ONEshare (in partnership with DataONE)

Architecture and Implementation

Dash is completely open source. Our code is made publicly available on GitHub (http://cdluc3.github.io/dash/). Dash is based on an underlying Ruby-on-Rails data publication platform called Stash. Stash encompasses three main functional components: Store, Harvest, and Share.

• Store: The Store component is responsible for the selection of datasets; their description in terms of configurable metadata schemas, including specification of ORCID and Fundref identifiers for researcher and funder disambiguation; the assignment of DOIs for stable citation and retrieval; designation of an optional limited time embargo; and packaging and submission to the integrated repository
• Harvest: The Harvest component is responsible for retrieval of descriptive metadata from that repository for inclusion into a Solr search index
• Share: The Share component, based on GeoBlacklight, is responsible for the faceted search and browse interface

Individual dataset landing pages are formatted as an online version of a data paper, presenting all appropriate descriptive and administrative metadata in a form that can be downloaded as an individual PDF file, or as part of the complete dataset download package, incorporating all data files for all versions.

To facilitate flexible configuration and future enhancement, all support for the various external service providers and repository protocols are fully encapsulated into pluggable modules. Metadata modules are available for the DataCite and Dublin Core metadata schemas. Protocol modules are available for the SWORD 2.0 deposit protocol and the OAI-PMH and ResourceSync harvesting protocols. Authentication modules are available for InCommon/Shibboleth and Google/OAuth19 identity providers (IdPs). We welcome collaborations to develop additional modules for additional metadata schemas and repository protocols. Please email UC3 (uc3 at ucop dot edu) or visit GitHub (http://cdluc3.github.io/dash/) for more information.

Features of the newly refreshed Dash service

What are the new features on our refresh of the Dash services?  Take a look.

A little Dash history

The Dash project began as DataShare, a collaboration among UC3, the University of California San Francisco Library and Center for Knowledge Management, and the UCSF Clinical and Translational Science Institute (CTSI). CTSI is part of the Clinical and Translational Science Award program funded by the National Center for Advancing Translational Sciences at the National Institutes of Health. Dash version 2 developed by UC3 and partners with funding from the Alfred P. Sloan Foundation (our funded proposal). Read more about the code, the project, and contributing to development on the Dash GitHub site.

A little Dash future

We will continue the development of the new Dash platform and will keep you posted. Next up: support for timed deposits and embargoes.  Stay tuned!

USING AMAZON S3 AND GLACIER FOR MERRITT- An Update

The integration of the Merritt repository with Amazon’s S3 and Glacier cloud storage services, previously described in an August 16 post on the Data Pub blog, is now mostly complete. The new Amazon storage supplements Merritt’s longstanding reliance on UC private cloud offerings at UCLA and UCSD. Content tagged for public access is now routed to S3 for primary storage, with automatic replication to UCSD and UCLA. Private content is routed first to UCSD, and then replicated to UCLA and Glacier. Content is served for retrieval from the primary storage location; in the unlikely event of a failure, Merritt automatically retries from secondary UCSD or UCLA storage. Glacier, which provides near-line storage with four hour retrieval latency, is not used to respond to user-initiated retrieval requests.

In preparation for this integration, all retrospective public content, over 1.1 million objects and 3 TB, was copied from UCSD to S3, a process that took about six days to complete. A similar move from UCSD to Glacier is now underway for the much larger corpus of private content, 1.5 million objects and 71 TB, which is expected to take about five weeks to complete.

The Merritt-Amazon integration enables more optimized internal workflows and increased levels of reliability and preservation assurance. It also holds the promise of lowering overall storage costs, and thus, the recharge price of Merritt for our campus customers.  Amazon has, for example, recently announced significant price reductions for S3 and Glacier storage capacity, although their transactional fees remain unchanged.  Once the long-term impact of S3 and Glacier pricing on Merritt costs is understood, CDL will be able to revise Merritt pricing appropriately.

CDL is also investigating the possible use of the Oracle archive cloud, as a lower-cost alternative, or supplement, to Glacier for dark archival content hosting.  While offering similar function to Glacier, including four hour retrieval latency, Oracle’s price point is about 1/4th of Glacier’s for storage capacity.

An RDM Model for Researchers: What we’ve learned

Thanks to everyone who gave feedback on our previous blog post describing our data management tool for researchers. We received a great deal of input related to our guide’s use of the term “data sharing” and our guide’s position in relation to other RDM tools as well as quite a few questions about what our guide will include as we develop it further.

As stated in our initial post, we’re building a tool to enable individual researchers to assess the maturity of their data management practices within an institutional or organizational context. To do this, we’ve taken the concept of RDM maturity from in existing tools like the Five Organizational Stages of Digital Preservation, the Scientific Data Management Capability Model, and the Capability Maturity Guide and placed it within a framework familiar to researchers, the research data lifecycle.

Data Sharing

The most immediate feedback we received was about the term “Data Sharing”. Several commenters pointed out the ambiguity of this term in the context of the research data life cycle. In the last iteration of our guide, we intended “Data Sharing” as a shorthand to describe activities related to the communication of data. Such activities may range from describing data in a traditional scholarly publication to depositing a dataset in a public repository or publishing a data paper. Because existing data sharing policies (e.g. PLOS, The Gates Foundation, and The Moore Foundation) refer specifically to the latter over the former, the term is clearly too imprecise for our guide.

Like “Data Sharing”, “Data Publication” is a popular term for describing activities surrounding the communication of data. Even more than “Sharing”, “Publication” relays our desire to advance practices that treat data as a first class research product. Unfortunately the term is simultaneously too precise and too ambiguous it to be useful in our guide. On one hand, the term “Data Publication” can refer specifically to a peer reviewed document that presents a dataset without offering any analysis or conclusion. While data papers may be a straightforward way of inserting datasets into the existing scholarly communication ecosystem, they represent a single point on the continuum of data management maturity. On the other hand, there is currently no clear consensus between researchers about what it means to “publish” data.

For now, we’ve given that portion of our guide the preliminary label of “Data Output”. As the development process proceeds, this row will include a full range of activities- from description of data in traditional scholarly publications (that may or may not include a data availability statement) to depositing data into public repositories and the publication of data papers.

Other Models and Guides

While we correctly identified that there are are range of rubrics, tools, and capability models with similar aims as our guide, we overstated that ours uniquely allows researchers to assess where they are and where they want to be in regards to data management. Several of the tools we cited in our initial post can be applied by researchers to measure the maturity of data management practices within a project or institutional context.

Below we’ve profiled four such tools and indicated how we believe our guide differs from each. In differentiating our guide, we do not mean to position it strictly as an alternative. Rather, we believe that our guide could be used in concert with these other tools.

Collaborative Assessment of Research Data Infrastructure and Objectives (CARDIO)

CARDIO is a benchmarking tool designed to be used by researchers, service providers, and coordinators for collaborative data management strategy development. Designed to be applied at a variety of levels, from entire institutions down to individual research projects, CARDIO enables its users to collaboratively assess data management requirements, activities, and capacities using an online interface. Users of CARDIO rate their data management infrastructure relative to a series of statements concerning their organization, technology, and resources. After completing CARDIO, users are given a comprehensive set of quantitative capability ratings as well as a series of practical recommendations for improvement.

Unlike CARDIO, our guide does not necessarily assume its users are in contact with data-related service providers at their institution. As we stated in our initial blog post, we intend to guide researchers to specialist knowledge without necessarily turning them into specialists. Therefore, we would consider a researcher making contact with their local data management, research IT, or library service providers for the first time as a positive application of our guide.

Community Capability Model Framework (CCMF)

The Community Capability Model Framework is designed to evaluate a community’s readiness to perform data intensive research. Intended to be used by researchers, institutions, and funders to assess current capabilities, identify areas requiring investment, and develop roadmaps for achieving a target state of readiness, the CCMF encompasses eight “capability factors” including openness, skills and training, research culture, and technical infrastructure. When used alongside the Capability Profile Template, the CCMF provides its users with a scorecard containing multiple quantitative scores related to each capability factor.

Unlike the CCMF, our guide does not necessarily assume that its users should all be striving towards the same level of data management maturity. We recognize that data management practices may vary significantly between institutions or research areas and that what works for one researcher may not necessarily work for another. Therefore, we would consider researchers understanding the maturity of their data management practices within their local contexts to be a positive application of our guide.

Data Curation Profiles (DCP) and DMVitals

The Data Curation Profile toolkit is intended to address the needs of an individual researcher or research group with regards to the “primary” data used for a particular project. Taking the form of a structured interview between an information professional and a researcher, a DCP can allow an individual research group to consider their long-term data needs, enable an institution to coordinate their data management services, or facilitate research into broader topics in digital curation and preservation.

DMVitals is a tool designed to take information from a source like a Data Curation Profile and use it to systematically assess a researcher’s data management practices in direct comparison to institutional and domain standards. Using the DMVitals, a consultant matches a list of evaluated data management practices with responses from an interview and ranks the researcher’s current practices by their level of data management “sustainability.” The tool then generates customized and actionable recommendations, which a consultant then provides to the researcher as guidance to improve his or her data management practices.

Unlike DMVitals, our guide does not calculate a quantitative rating to describe the maturity of data management practices. From a measurement perspective, the range of practice maturity may differ between the four stages of our guide (e.g. the “Project Planning” stage could have greater or fewer steps than the “Data Collection” stage), which would significantly complicate the interpretation of any quantitative ratings derived from our guide. We also recognize that data management practices are constantly evolving and likely dependent on disciplinary and institutional context. On the other hand, we also recognize the utility of quantitative ratings for benchmarking. Therefore, if, after assessing the maturity of their data management practices with our guide, a researcher chooses to apply a tool like DMVitals, we would consider that a positive application of our guide.

Our Model (Redux)

Perhaps the biggest takeaway from the response to our  last blog post is that it is very difficult to give detailed feedback on a guide that is mostly whitespace. Below is an updated mock-up, which describes a set of RDM practices along the continuum of data management maturity. At present, we are not aiming to illustrate a full range of data management practices. More simply, this mock-up is intended to show the types of practices that could be described by our guide once it is complete.

Project Planning

The “Project Planning” stage describes practices that occur prior to the start of data collection. Our examples are all centered around data management plans (DMPs), but other considerations at this stage could include training in data literacy, engagement with local RDM services, inclusion of “sharing” in project documentation (e.g. consent forms), and project pre-registration.

Data Collection

The “Data Collection” stage describes practices related to the acquisition, accumulation, measurement, or simulation of data. Our examples relate mostly to standards around file naming and structuring, but other considerations at this stage could include the protection of sensitive or restricted data, validation of data integrity, and specification of linked data.

Data Analysis

The “Data Analysis” stage describes practices that involve the inspection, modeling, cleaning, or transformation of data. Our examples mostly relate to documenting the analysis workflow, but other considerations at this stage could include the generation and annotation of code and the packaging of data within sharable files or formats.

Data Output

The “Data Output” stage describes practices that involve the communication of either the data itself of conclusions drawn from the data. Our examples are mostly related to the communication of data linked to scholarly publications, but other considerations at this stage could include journal and funder mandates around data sharing, the publication of data papers, and the long term preservation of data.

Next Steps

Now that we’ve solicited a round of feedback from the community that works on issues around research support, data management, and digital curation, our next step is to broaden our scope to include researchers.

Specifically we are looking for help with the following:

• Do you find the divisions within our model useful? We’ve used the research data lifecycle as a framework because we believe it makes our tool user-friendly for researchers. At the same time, we also acknowledge that the lines separating planning, collection, analysis, and output can be quite blurry. We would be grateful to know if researchers or data management service providers find these divisions useful or overly constrained.
• Should there be more discrete “steps” within our framework? Because we view data management maturity as a continuum, we have shied away from creating discrete steps within each division. We would be grateful to know how researchers or data management service providers view this approach, especially when compared to the more quantitative approach employed by CARDIO, the Capability Profile Template, and DMVitals.
• What else should we put into our model? Researchers are faced with changing expectations and obligations in regards to data management. We want our model to reflect that. We also want our model to reflect the relationship between research data management and broader issues like openness and reproducibility. With that in mind, what other practices and considerations should or model include?