10-15-13 “Metadata and Repository Services for Research Data Curation” Presentation Slides

October 15, 2014 Hot Topics: DuraSpace Community Webinar Series

Hot Topics: The DuraSpace Community Webinar Series

Series Six: “Research Data in Repositories”

Curated by David Minor

October 15, 2013 Hot Topics: DuraSpace Community Webinar Series

Webinar 2: Metadata & Repository Services for Research Data Curation

Presented by: Declan Fleming, Chief Technology Strategist, UC San Diego Library Matt Critchlow, Manager of Development and Web Services, UC San Diego Library Arwen Hutt, Metadata Librarian, UC San Diego Library

Hot Topics Web Seminar Series: Research Data in Repositories

The UC San Diego Experience Second Webinar: Metadata and Repository Services

for Research Data Curation

General Series Intro

• First webinar: Intro and Framing: UC San Diego decisions and planning

• Second Webinar: Deep dive into technology and metadata

• Third Webinar: The perspective from researchers, next steps

Your esteemed presenters …

First webinar: David Minor – Program Director, Research Data Curation Declan Fleming - Chief Technology Strategist

Second webinar: Declan Fleming - Chief Technology Strategist Arwen Hutt - Metadata Librarian Matt Critchlow - Manager of Development and Web Services

Third webinar: Dick Norris – Professor, Scripps Institution of Oceanography Rick Wagner – Data Scientist at San Diego Supercomputer Center

Today we will …

• Discuss real-world researcher interaction

• Document how metadata and files combine to make digital objects

• Describe the DAMS data model and how it supports complex research objects

• Detail the technology driving the DAMS

• Point to the future

Working with Researchers: Pilots

• The Brain Observatory

• NSF OpenTopography Facility

• Levantine Archaeology Laboratory • Scripps Institute of Oceanography

Geological Collections

• The Laboratory for Computational

Astrophysics

Working with Researchers: Process

• Introductory meeting • Metadata point person • Ongoing discussions • One on one work

Iterative, collaborative, customized, experimental…pilot!

Working with Researchers: Data management

• Collocation • Clean up • Identifiers • Metadata

Working with Researchers: What is an object?

• What are the boundaries on a discreet set or subset of data? What is required to make the data intelligible, usable and reusable?

• What needs to be preserved? • What do they want to display and/or share? • What do they want to be able to refer to or

cite?

Working with Researchers: What is an object?

Slice

Etc…

or

Brain

Artifact

Site

or

Working with Researchers: Take Aways

They are the subject experts

There are a lot of broad level similarities

But no such thing as one size fits all

We want a new data model…

• One that is flexible and accommodates disparate metadata from a variety of sources

• While promoting consistency within the data store • One that supports relationships within and between

objects • One that is more community engaged, both sharing

vocabularies and technology, and utilizing others shared vocabularies and technologies

• One that supports improved management of objects and metadata

DAMS Data Model Development Process

• Five people, in a room, 16 hours a week for 4 months

• Worked through existing data, use case scenarios, known data requirements, investigated known ontologies, etc.

• Lots and lots and lots of discussion • Utilizes MADS (Metadata Authority Description

Schema) • Results = a data dictionary and an OWL ontology • Living document

DAMS Data Model: Flexibility

• The data model provides enough flexibility that we can accommodate a wide variety of data within the schema – Vocabularies – Use of “types” or “display labels” to distinguish

specific subtypes of a data field – Flexible structures and relationships – Extensible

DAMS Data Model: Consistency

• But enough consistency that searching and display rules do not need to be customized for each individual collection of material – Rules can be applied at the level of the broader

concept • As well as establishing the organizational

structure necessary for maintaining consistency over time – Evaluation and approval of modifications

DAMS Data Model: Relationships

• It allows us to create a number of different relationships – Collections and sub-collections – Collections and objects – Objects and components

(complex hierarchical objects) – Other related resources internal

or external to the DAMS

complex object example

DAMS Data Model: Vocabularies

• Allow management of local & community vocabularies – Vocabulary terms as entities – Ability to encode authority data (vocabulary

source, value uri, etc.) as well as sameAs relationships between the same term expressed in multiple sources

– Ability to update authority records as community vocabularies become more formalized.

DAMS Data Model: Management

• One that supports improved management of objects and metadata – Authority management of vocabulary terms – Event metadata!

DAMS Architecture

Preservation: Chronopolis

Current DAMS Process 1. Create Bagit bags for all objects 2. Host via HTTP(S) 3. Bags are retrieved and ingested into Chronopolis DAMS4 Process 1. Create Bagit bags for Δ objects using Event metadata 2. Host via HTTP(S) or enqueue on messaging queue for

ingestion

Storage

Storage: EMC Isilon 72NL

Storage For Library Collections 1 cluster of 5 Nodes 1 Node = 36 x 2TB Drives Total Current Usable Storage of 320TB OneFS 7.0.2.1

Storage: OpenStack

Storage For Research Data Collections Testing: • Performance versus Local Storage • Large Files (up to 1TB)

– Segmenting files > 5GB – Lexical order bug fix: 1,10,2 -> 0001,0002,…0010

• Rackspace CloudFiles API VS OpenStack REST API Testing Notes: https://libraries.ucsd.edu/blogs/dams/openstack-testing-notes/

DAMS Repository

DAMS Repository

Core Repository Application: Create, Read, Update, Delete (CRUD) Uses: Jena, ActiveMQ, JHOVE, Apache Tika, FFMPEG, ImageMagick Manages: • Metadata Triplestore • Storage • Solr

DAMS Repository: Metadata Triplestore

DAMS Repository: Metadata Triplestore

Triplestore was: Allegrograph Triplestore is: PostgresSQL DB + Jena • Schema: (ID), Parent, Subject, Predicate, Object Jena Usage: • Core/RDF API – Parsing, loading, updating, serializing RDF • ARQ API – SPARQL queries

DAMS Repository: REST API

Hydra Framework

Source: https://wiki.duraspace.org/display/hydra/Technical+Framework+and+its+Parts

DAMS Repository: Fedora API-ish

Fedora API – Next PID

Fedora API – Next PID

DAMS Manager

DAMS Manager

Java application using Spring MVC framework • Collection Management

– Metadata Ingest and Export – File Ingest – Derivative Generation – Solr indexing by Collection

• Administrative Reporting and Statistics

DAMS Hydra Head

DAMS Hydra Head

DAMS Hydra Head: Blacklight

RDF in Hydra

RDF in Hydra: (Read) Nested Attributes

RDF in Hydra: (Create) Nested Attributes

DAMS Hydra Head: Complex Objects

Next Steps

Beta Release: Late October Production Release: January Future: • Sufia/Curate Integration for administrative functionality • Additional Linked Data Integration and Crosswalks

– Schema.org, OpenURL, Dublin Core, ResourceSync

• Fedora4

More Information

DAMS Overview https://github.com/ucsdlib/dams/wiki/DAMS-Manual DAMS Hydra Head https://github.com/ucsdlib/damspas DAMS Ontology https://github.com/ucsdlib/dams/tree/master/ontology DAMS REST API https://github.com/ucsdlib/dams/wiki/REST-API Hot Topics Series 3: Get a Head on the Repository with Hydra http://duraspace.org/hot-topics Hydra Technical Overview https://wiki.duraspace.org/display/hydra/Technical+Framework+and+its+Parts OneFS Technical Overview http://www.emc.com/collateral/hardware/white-papers/h10719-isilon-onefs-technical-overview-wp.pdf Isilon Overview http://www.emc.com/collateral/software/data-sheet/h10541-ds-isilon-platform.pdf

Coming Up Next

Final Webinar (October 31) The researcher perspective from two of our pilot participants Dick Norris – Professor, Scripps Institution of Oceanography Rick Wagner – Data Scientist at San Diego Supercomputer Center

Questions?

Thanks! Declan Fleming @declan | dfleming@ucsd.edu Arwen Hutt @arwenh | ahutt@ucsd.edu Matt Critchlow @mattcritchlow | mcritchlow@ucsd.edu