FSCI Data Discovery
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This document summarizes a session from the Force 11 Scholarly Communications Institute Summer School on data discovery. The session covered metadata, including what it is, types of metadata, and standards. It discussed how people search for and find data through various sources. The session also explored the FAIR data principles of findable, accessible, interoperable and reusable data and had breakout groups discuss applying these principles in practice.
FSCI Data Discovery
- 1. Force 11 Scholarly Communications Institute Summer School 31 July – 4 August 2017 University of California, San Diego Data in the Scholarly Communications Lifecycle Natasha Simons Senior Research Data Specialist
- 2. Wednesday 2 August Session one – data discovery • What is data discovery? • What is metadata? • Exercise: describe this dataset! • More about metadata including types, storage, standards, vocabularies, the power of rich metadata, m-2-m metadata exchange • Discussion groups exploring the FAIR data principles: • Findable • Accessible • Interoperable • Reusable Duration: 60 mins
- 3. Why do people search for data?
- 4. Why do people search for data*? • Exploratory/Scoping • Reuse/Secondary data analysis • Can be starting point or ad hoc • Peer review • Reproduce/extend results • Repurpose (e.g. for mashups, visualisations, simulations) • Verify claims (e.g. report findings) *Not in any order; not exhaustive!
- 5. How do people find data?
- 6. How do people find data*? • Google • Ask a colleague • Find link to data in a journal article • Data journals • Data registries e.g. re3data • Open data portals e.g. data.gov • Institutional repositories • Data / Discipline repositories e.g. Dryad • Project website • Data discovery aggregators like Research Data Australia • Library catalogues, databases *Not in any order; not exhaustive!
- 7. Characteristics of finding data • Movable feast / changing beast • No standard practice, universal standard or vocab • Databases are non-exhaustive • Methods for searching and terms driven by why people are looking and how the data is stored
- 8. Find Identify Select Obtain When looking for data, people need to: This is our guide for creating metadata records! FISO
- 10. Exercise: create metadata Your task: 1. Divide into 2 groups 2. Each take one of the CSV data files 3. Describe the data! Duration: 10 mins
- 11. Metadata records How did you go? What did you learn? Here are the original metadata descriptions: CSV dataset #1 - https://data.qld.gov.au/dataset/marine-oil- spills-data CSV dataset #2 – https://data.qld.gov.au/dataset/koala-hospital-data
- 12. Types of metadata Metadata elements can describe either a single item or a collection, and can serve different purposes. Examples of metadata for a photograph could include: • descriptive metadata, such as the name of the photographer, the location and subject of the photograph, the date and time that the photograph was taken • technical metadata, such as the type of camera used to take the photograph, the file format in which the photograph is stored, the exposure time and dimensions of the photograph, and so on • access and rights metadata, defining who is allowed to view the photograph under what conditions, and what they can do with it (reuse) • preservation metadata, which keeps track of actions taken to preserve or sustain the photograph for later access and use. Source: ANDS website
- 13. Where does metadata come from? • Metadata can be created manually by people or automatically by instruments or computers. • Metadata capture is easiest if it is automatically generated when the data is created, for example, the metadata your camera captures every time you take a photo. • For much research data, the researcher needs to create the descriptive and provenance metadata, as only they have that information. When should metadata for research data be created? Whenever it is needed, particularly: • During the course of data collection • When the data changes • And at the end when the data is deposited and ‘published’
- 14. Where is metadata stored? • Metadata can be stored in local source systems like repositories – often with the data it is about. • Metadata that enables research data to be discovered and accessed should be published in discovery portals like Research Data Australia, or in discipline or institutional portals. • Metadata that gives detailed contextual information and supports reuse, such as data-item-level metadata, workflows, analysis, and detailed methods information, is usually stored with the data.
- 15. The power of rich metadata • Well described metadata records show the power of rich metadata in making research data collections discoverable, citable, reusable and accessible for the long term. • Two-Rocks moorings data 2004 - 2005 metadata record in the CSIRO Data Access Portal contains 35 metadata fields which enable researchers to quickly and accurately assess the relevance of this dataset to their research. The metadata record and the data are closely linked through co-location on the same access page. The Files tab contains additional metadata about each of the 17 files within this collection: file type, last modified, and file size. • Rich metadata allows records to be syndicated to other data catalogues; here is the same Two-Rocks mooring data record syndicated to: • Research Data Australia: Australia’s aggregated research data catalogue • Marlin Oceans and Atmosphere: a discipline-specific metadata catalogue
- 16. Metadata standards A metadata standard is a schema that has been formally approved and published, with governance procedures in place to maintain and update the standard. Examples: Dublin Core - http://dublincore.org/documents/dces/ RIF-CS – http://services.ands.org.au/documentation/rifcs/1.6/guidelines/rif- cs.html How do I find metadata standards? • See this disciplinary metadata directory
- 17. Vocabularies and research data • A vocabulary sets out the common language a discipline has agreed to use to refer to concepts of interest in that discipline. • Researchers planning observation or surveys need to define their data items clearly. • An agreed vocabulary (a standard) makes a good starting point for translating concepts into other vocabularies so that collaboration can occur. • Indexing vocabularies are used to tag items in library catalogues and search portals and to provide keywords for academic journal articles. • A vocabulary service is a machine-to-machine service that can support activities such as creating, managing and querying vocabularies. Want more? • Read the ANDS Guide on vocabularies for research data • Explore Research Vocabularies Australia • Check out the COAR vocabularies
- 18. m-2-m metadata exchange m-2-m = networked devices to exchange information and perform actions without the manual assistance of humans. Examples: OAI-PMH – also known as metadata harvesting and commonly used by repositories and repository aggregators APIs – such as the ORCID API that enables things like authenticating against the ORCID registry
- 19. Want more? Have a go at these Things: Thing 4 – data discovery Thing 11 – what’s my metadata schema? Thing 12 – vocabularies for data description Thing 13 – walk the crosswalk
- 20. Discussion: FAIR data Your task: • Divide into groups of 2 or 3 people • Read through the FAIR data principles: https://www.force11.org/group/fairgroup/fairprinciples • Discuss: • Are these good principles? Why? Why not? • How might these principles be put into practice? • Then we will re-group and you will be invited to share Duration: 20 mins
- 21. FAIR data - resources In Nature - https://www.nature.com/articles/sdata201618 EUDAT webinar - https://eudat.eu/events/webinar/fair-data-in-trustworthy- data-repositories-webinar LIBER webinar - http://libereurope.eu/blog/2017/02/23/liber-webinar-fair- data-principles-fair/ The European Commission has established an Expert Group on Turning FAIR Data into Reality (E03464) which will run until Spring 2018. Horizon 2020 Guidelines on FAIR Data Management - http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/ oa_pilot/h2020-hi-oa-data-mgt_en.pdf
- 22. With the exception of logos, third party images or where otherwise indicated, this work is licensed under the Creative Commons Australia Attribution 3.0 Licence. ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program. Monash University leads the partnership with the Australian National University and CSIRO. Natasha Simons natasha.simons@ands.org.au Tw: @n_simons ORCID: https://orcid.org/0000-0003-0635-1998
Editor's Notes
- #21: https://www.force11.org/group/fairgroup/fairprinciples