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1 Click to edit Master title style Portage Research Management Click to editdata Master subtitle styleplatforms Dataverse North and the Federated Research Data Repository (FRDR) Sept 18, Amber Leahey (Scholar s Portal), Lisa Goddard (UVic), Lee Wilson (Portage/ACENET), Todd Trann (Compute Canada) RDA 10th Plenary Colocated Event, Centre Mont-Royal, Montréal, QC 9/8/2017 1

2 About Portage Portage is a national RDM network launched by the Canadian Association of Research Libraries (CARL) in 2015 that coalesces initiatives to build RDM capacity and infrastructure with libraries Networks of Expertise: Pan-Canadian RDM expertise Provide access to resources, tools, and experts in the area of RDM Infrastructure Platforms Working with library consortia, institutions, and other infrastructure partners to assemble essential RDM infrastructure and service components

3 Platform: Dataverse Using Dataverse at your Institution/Library Amber Leahey (Scholars Portal, OCUL) Lisa Goddard (University of Victoria)

4 Dataverse overview

5 Repository features Digital object identifier (DOI) minting and standard data citation Collaborative data management environment Metadata creation (DDI, DataCite, Dublin Core) File versioning and checksums Tabular data visualization Flexible user & file permissions (open - restricted) Creative commons (cc-zero) licensing

6 Dataverse goals Increase data sharing among researchers Provide standard tools for managing data Support data reuse and reproducibility Promote researcher data Ensure data are well preserved

7 Dataverse hosted installations (Canada)

8 Dataverse look and feel

9 Use Case: Journal citation 1. Researcher requires a DOI to cite data in a journal article currently under review. DOI is issued prior to publication.

10 Use Case: Lab/team collaboration 2. Research group includes team members going out in the field to collect data. Uploading datasets in Dataverse ensures the project group and professor has access to latest data. Data are versioned, and restricted to just team members.

11 Use Case: Data publication 3. Researcher would like to openly share and publish their recent research project and data. This ensures promotion of research, and data are available to others to build on. Researcher makes data available openly with CC-zero licensing.

12 Use Case: Library data collections 4. Libraries hold a variety of types of research data; data that is licensed; data that was produced or enhanced by the library; and data that has been archived with the library. Library curated datasets can be made available for reuse through Dataverse.

13 Use Case: RO / Departmental research tracking 5. Institutions increasingly require indicators about research outputs. A department can set up a Dataverse to track researcher outputs and ensure researchers within the department are sharing data within one another, the institution, or the world.

14 Strengths Bilingual tool (beta) Support for any file format/ data type Flexible for a variety of setups and use cases Collaborative tool Support for research data standards: metadata, DOI, versioning, API, OAI-PMH Open-source research community

15 Limitations 2GB file size upload Limited connections to other research management information systems RO admin systems, DMP Assistant, etc. Limited connections to other research data repositories / services No providers

16 Usage by the numbers Usage statistics are for Harvard Dataverse:

17 Data depositors Usage statistics are for Harvard Dataverse:

18 Disciplinary coverage Usage statistics are for Harvard Dataverse:

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56 Dataverse integrations Open Journals System (OJS) Amazon S3 (coming 4.8) OpenStack SWIFT Rsync (large files) (coming 4.9) Open Science Framework (OSF) TwoRavens WorldMap DataCite ORCID Shibboleth Archivematica (in development) Miniverse (usage statistics)

57 Integrations with FRDR (2017-) 1. FRDR harvests Dataverse metadata 2. Brings together Dataverses across Canada into a single-search interface 3. Makes local institutional repository data discoverable alongside subject repositories

58 Community Developments (Canada) DataCite Canada (DOI) CISTI/ DataCite Canada Data Explorer (tabular data) Scholars Portal Internationalization / French interface UdeM & Scholars Portal

59 Development Roadmap ( ) (latest release) -additional admin features, auto-fill ORCID id 4.8 -AWS S3 support 4.9 -large file support (non-http upload using Rsync) 5.0 -Sensitive data support (DataTags and privacy detection)

60 Thank you! Acknowledgments Dataverse North Working Group Lisa Goddard (Lead) (Victoria), James Doiron (UofA), Eugene Barsky (UBC), Amber Leahey (OCUL/SP), Andrew Nicholson (UT/M), Alexandra Cooper (Queen s), Laure Perrier (UofT), Kathryn Ruddock (U Calgary), Susan Mowers (uottawa), Carol Perry (Guelph), Maggie Jean Neilson (Acadia), Carrie Breton (Guelph), Corey Davis (Victoria), Pierre Roberge (UQAM), Berenica Vejvoda (McGill), Barbara Znamirowski (Trent), Sally Taylor (UBC), Erin MacPherson (Dalhousie), Jason Flynn (Dalhousie), Brianne Selman (Winnipeg), Michael Steeleworthy (Wilfrid Laurier University), Kaitlin Newson (OCUL/SP), Dee Wallace (Winnipeg), Lisa O Hara (Manitoba), Lee Wilson (Portage/Acenet)

61 Q&A and discussion How do you see Dataverse fitting into your local RDM strategy?

62 The Federated Research Data Repository Lee Wilson (Portage/ACENET) Todd Trann (Compute Canada)

63 Many Stakeholders! A collaboration among libraries, institutions, HPC, and federal organizations involving many stakeholders across the RDM sphere Partnership between Compute Canada and the Canadian Association of Research Libraries (CARL) Partnership between Compute Canada and Globus Hosted on Compute Canada hardware and infrastructure Steering and input from CARL/Portage, Portage Network of Expertise, and individual institutions

64 What is FRDR? FRDR = Federated Research Data Repository (DFDR en français) A scalable, federated platform for digital research data management and the discovery of Canadian research data Discovery Storage Preservation Curation

65 Discovery FRDR s harvester indexes data repositories across Canada to make research data held in many repositories discoverable from a single platform - Currently supports OAI-PMH and CKAN endpoints - Metadata can be crosswalked or adv. search facets provided - FRDR itself is indexed by the same harvester - We generate JSON-LD for the search backend Goal: supplement existing repository sites and improve discovery - Break down repository siloing, avoid being just another repositoy

66 Storage A place for Canadian researchers to deposit large data sets Big data transfer using Globus Connect Active storage: 100 TB = 50 TB at two locations Designed for scalability Storage and repositories can be distributed, owned, and operated by organizations/institutions A place to deposit data sets if researcher does not have a local option

67 Preservation Preservation for long term usability of data sets Automatic Archivmatica processing for datasets up to 300 GB or 3000 files Archiving (and automatic geographic replication) of data sets to fit in with researcher backup strategy

68 Timeline

69 Limited Production Limited Production means: FRDR will be on "production hardware Many of the features expected for launch will be in place We will work with a select number of research groups to curate and ingest their data CARL commits to making their data discoverable and accessible into and beyond the full service launch In the event that FRDR is not funded, CARL commits to working with researchers to find a suitable repository for their data

70 Limited Production (cont.) Limited Production means: Limited Production will help us to refine our service model and operational work flows with a small number of interested researchers We have identified eight research groups ranging from the natural/physical sciences to the social sciences and digital humanities; providing a good variety of data types, volume, and client use cases/needs Limited Production will last until full service launch in April 2018 Limited Production site: Demonstration site:

71 FRDR walkthrough (

72 Major things that work today Direct deposit of data sets through Globus File Transfer Direct download of datasets through HTTPS Automatic preservation of data sets with Archivematica Support for multiple storage sites Security, privacy, and embargo support

73 Major things that work today (cont.) Issuing DOIs Bilingual user interface for both repository and discovery Indexing items from selected Canadian repositories Support for multiple licenses Faceted search in the discovery interface ORCID integration (best SSO solution currently!)

74 Features Coming This Year* Direct upload of datasets through HTTPS API for automated deposit Scaling up capacity for preservation and archiving Increasing the number of repositories being indexed Improved metrics and reports for items, research groups, and institutions Geo-spatial search in discovery

75 Feature Enhancements & Future Directions FRDR envisioned as a series of micro-services Federated search and discovery Archivematica-as-a-Service (AmaaS) Support for existing VREs, analytic, and collaboration tools like Jupyter Notebook and Open Science Framework Offering researchers a working store to publish pipeline Integration with Dataverse communities and collections of Dataverse with FRDR storage and transfer Leveraging the Portage Network of Expertise for feature extension

76 FRDR and your Institution Discovery: Make data stored in your institutional repository discoverable from a single portal Storage: Big Data storage for research on your campus Preservation: Archivematica processing for large datasets Customization: Bring your own storage to FRDR! Metrics: Ability to generate reports about researcher outputs Local curation support, national coordination

77 Distributed Service Model Nationally coordinated RDM platform supported by CARL and the Portage Network of Expertise Curation support at the institutional to regional-level multiple FTEs embedded in academic libraries leveraging long-standing institutional knowledge and relationships with local research communities Envisioned service model for all Portage-supported platforms, not just FRDR

78 Strengths Scalability Capable of handling large data sets Federated search and discovery of Canadian research data Customizable metadata Automatic preservation processing for large datasets Long-term storage and backup

79 Limitations No individual file-level metadata New platform with development still on-going Lacks the extensive community base of Dataverse Currently lacks support for communities and collections

80 Acknowledgements

81 Questions?

82 15-minute break!

83 Vision for Canadian RDM Services We envision seamless, equitable access to RDM platforms and services for Canadian institutions and researchers

84 Vision for Canadian RDM Services A National Research Data Service that leverages existing RDM expertise and knowledge from across Canadian institutions Distributed service model composed of librarian experts, local institutional RDM contacts, resources, & tools Partnering with infrastructure and software providers Interoperable platforms and tools for RDM Position paper has been prepared by the Leadership Council for Digital Research Infrastructure (LCDRI) for Innovation, Science, and Economic Development Canada (ISED)

85 Multiple Platforms; One Service PLANNING Stora STORAGE & DISCOVERY Share & preserve, small, well-curated data sets, public data Data Management Plans for funding applications Preserve larger & messier datasets, discovery, sharing where possible

86 Challenges Integrating Dataverse, FRDR, and the DMP Assistant on a single platform Understanding and articulating the strengths and limitations of Dataverse and FRDR Defining business and service models for Portage Services Securing sustainable funding support Aligning with funder and policy timelines

87 Discussion Questions 1. What are the major barriers or challenges to RDM at your institution? 2. How do you see the two platforms fitting into your local institutional infrastructure for RDM? 3. What do you think of the vision of Canadian RDM platforms and services presented today? Feel free to add your thoughts to the Google Doc:

88 Thank you!