Tools and Services for Managing Research Patricia Cruse University of California Curation Center California Digital Library

Management reality Ever increasing number and diversity of partners and stakeholders Ever increasing number, size, and diversity of content Decreasing resources Inevitability of disruptive technological and organization change

What keeps users up at night? What is “metadata”? Are there standards or best practices I should be aware of? How much will it cost? What is the ROI? Why should I care about preservation? I just need a place to put my data. Where can I get help? How can I share my work with colleagues? How can I make available the data associated with my publications? How can I fulfill the data management requirements of my grant? How can I make sure I get credit ? Can’t my work be included in the Web of Science? How can I provide access to my work?

New model for sustainable services Curation Focus on the full information lifecycle Technology/infrastructure Simple, flexible, adaptable Services Diverse Content Agnostic Sustainability A must UC Community External to UC Adopted from MacKenzie Smith, IDCC 2010

UC3 service landscape Create, edit, share, and save data management plans Open access scholarly publishing: papers, journals, books, seminars & more Curation repository: store, manage, preserve, and share research data Create and manage persistent identifiers Open source curation add-in for Microsoft Excel An infrastructure to publish and get credit for sharing research data Data Publication

The research data problem Journal article – Uniquely and persistently identified – Concept of “publish” – Multiple copies – Easily findable – Additional services: impact metrics, citation tracking, etc. Research data – Nope – Not really – Typically one – Difficult – Nope  Second-class citizens in the scholarly record

EZID: Long term identifiers made easy Precise identification of a dataset (DOI or ARK) Credit to data producers and data publishers A link from the traditional literature to the data (DataCite) Exposure and research metrics for datasets (Web of Knowledge) Primary Functions 1. Create persistent identifiers 2. Manage identifiers (and associated metadata) over time 3. Resolve identifiers Take control of the management and distribution of your research, share and get credit for it, and build your reputation through its collection and documentation

Merritt Curation repository open to the UC community and beyond Discipline / content agnostic Micro-services architecture Easy-to-use UI or API Hosted or locally deployed Primary Functions 1. Deposit 2. Manage (metadata, versions, etc) 3. Access (expose) 4. Share (with other researchers) 5. Preserve

Modes of use Dark archive for important digital assets Bright archive with direct discovery and access Preservation back-end for existing or new discovery and content management systems and services Integration with distributed data grids

DMPTool Coalition partners CDL DataONE Digital Curation Centre Smithsonian Institution UCLA Library UCSD Libraries University of Illinois University of Virginia Libraries Meeting funding agencies data management plan requirements Primary Functions 1. Step-by-step “wizard” 2. Templates and examples 3. Links to institutional resources and agency information 4. Plan publication and sharing

Surveys indicate: Most researchers are unaware of preservation options Documentation practices are poor Excel is just one tool in workflows Digital curation for Excel Excel is the database of choice for many researchers How to encourage data sharing, archiving, and publishing ? – Self-description – Enhance discovery – Facilitate the determination of suitability for use Primary Functions 1. Metadata description (through extraction and augmentation) 2. Check export compatibility 3. Transfer to repository

Open access publishing eScholarship – Scholarly communication from grey literature to post- prints – Support for text and multimedia – Faceted search (XTF) – Use metrics Primary Functions 1. Institutional repository 2. Papers, monographs, journals, and conferences 3. Peer review

Vision for a “data paper” Wrap the unfamiliar in a familiar façade Minimally, a cover sheet and a set of links to archived artifacts Cover sheet contains familiar elements: title, date, authors, abstract, identifiers Just enough metadata to permit basic exposure to and discovery – Indexing by services such as Web of Science, Google Scholar – Instilling confidence in the identifier’s stability

Challenges and lessons learned How to engage with the research community early in the lifecycle? How can we prioritize the use of scarce resources to support emerging initiatives? Deploy simple and flexible infrastructure and services that can be used in diverse ways Start small: provide simple solutions that build up to solve more complex problems Collaborate now more than ever!

Business model Much research results from funded activities What happens when the project is over? Need to shift from pay-as-you-go (PAYG) to pay once, preserve forever (POPF) or, pay once, and forget (POAF) POPF = PAYG 0 + (d*PAYG)/(1+r ‒d) But how long is forever?

Summary A structural problemA superhero solution?

What lets researchers sleep at night? What are “metadata”? Where can I get help? Data Publication

For more information EZID Merritt DMPTool DCXL eScholarship UC Curation Center (UC3) Stephen AbramsMark Reyes Lisa ColvinAbhishek Salve Patricia CruseTracy Seneca Scott FisherJoan Starr Erik HetznerCarly Strasser Greg JanéeMarisa Strong John KunzeAdrian Turner Margaret LowPerry Willet David Loy