Latest revision as of 08:17, 7 September 2017

Presenters

Robert Haubt is an interdisciplinary researcher and lecturer in Digital Humanities.

Introduction

n/a

Monday Session: Databases

About Database & Knowledge Management

Data Management Life Cycle

Why is any of this important?

ARC Data Management Requirements Effective data management is an important part of ensuring open access to publicly funded research data. Data management planning from the beginning of a research project helps to outline how data will be collected, formatted, described, stored and shared throughout, and beyond, the project lifecycle. ARC Research Data Management: http://www.arc.gov.au/research-data-management

1. Where will your research data be stored at completion of the project?
2. What access will you provide to the data set on completion of the project?
3. How will you enable others to reuse your research data?

ANDS Guide for ARC Data Management Section: http://www.ands.org.au/guides/arc-guide-to-filling-in-the-dm-section

Australian Initiatives towards Data Management

23 Things

23 (research data) Things is self-directed learning for anybody who wants to know more about research data. If you are a person who cares for, and about, research data and want to fill in some gaps, learn more or find out what others are thinking, then this may be for you!

23 Things, ANDS

http://www.ands.org.au/partners-and-communities/23-research-data-things

Intersect

Intersect is Australia's largest full-service eResearch support agency. We help researchers increase their impact through innovative technologies and expert advice. We work closely with our members and the wider research community to:

1. Increase research productivity by decreasing time from hypothesis to tested results.
2. Support research diversity by enabling collaborators to share data and experience across disciplines and across organisations.
3. Increase research longevity by storing and sharing the long tail of data beyond the research project lifecycle.

Links:

1. Co-developing eResearch infrastructure: Technology-enhanced research practices, attitudes and requirements http://www.intersect.org.au/docs/eResearch%20survey%20full%20reportv1.0.pdf
2. http://www.intersect.org.au/reports
3. http://www.intersect.org.au/news/eresearch-survey-report

The General Process

What do we use to plan data collection?

1. reuse old data
2. collect new data
3. new forms
4. new applications

What do we use to collect data?

1. Notebooks
2. Endnote (publications, notes etc.)
3. Evernote
4. Paper Forms
5. Punch Cards
6. Audio/Visual Media
7. Remote Sensing data
8. other technologies
9. Apps
10. etc.

What do we use Process Data / Store Data?

1. Paper Forms
2. Punch Card Reader
3. Evernote
4. Punch Card Reader
5. Excel
6. Access
7. FileMake
8. flat
9. relational
10. extended relational
11. object-oriented
12. object-relational
13. network
14. hierarchical
15. tripple store
16. quad store
17. List of Vendors: https://cs.fit.edu/~pbernhar/dbms.html

How do we analyze data?

1. sort / categorise
2. filter
3. query

What do we use to analyze data?

1. human-based-computation
2. digital-computation
3. human-computer-computation

Software:

1. dirtdirectory.org
2. http://tapor-test.artsrn.ualberta.ca/home
3. http://www.visualdataweb.org/relfinder/relfinder.php connect to LOD repositories using SPARQL Endpoint (http://dbpedia.org/sparql/)

Where do we store data?

Desk

1. paper forms

Hard Drive

1. text
2. image
3. sound
4. 3d
5. Excel

Databases

1. Access
2. Wikis
3. Drupal
   1. https://groups.drupal.org/drupal-cultural-heritage
   2. https://groups.drupal.org/node/477008
   3. https://www.drupal.org/node/2871023
   4. http://mukurtu.org/
4. Jekyll
5. Other

Database Projects

1. http://heurist.sydney.edu.au/
2. http://www.rockartdatabase.com

Paid Solutions

1. EMu
2. Elsevier
3. JSTOR
4. http://www.getty.edu/conservation/our_projects/field_projects/arches/
5. Other

How do we share data?

1. hard copies
2. digital data
3. ideas

1. Australian Gov.Public Archives
   1. 19 Heritage Organizations
   2. 37 Gov. Heritage Bodies
   3. 15 Non-Gov. Heritage Bodies
   4. http://www.environment.gov.au/heritage/organisations/

1. Interdisciplinary
   1. Linked Open Data
      1. http://linkeddata.org/
      2. http://lod-cloud.net/
   2. Data Repositories
   3. Local Repositories
      1. http://www.researchspace.org/
   4. Discipline Specific Repositories
      1. http://papyri.info/
   5. Research Data Repositories
      1. http://www.re3data.org/
   6. Statistics
      1. http://www.nationmaster.com/au
      2. http://www.kdnuggets.com/datasets/index.html
   7. Open Access
      1. http://oad.simmons.edu/oadwiki/Data_repositories

How do we structure this data so we can make sense of it all?

1. Schema
2. Thesaurus
3. Linked Data
4. Metadata
5. Ontology
6. Inference

International Projects that have addressed some of these issues

1. CIDOC
   1. http://www.cidoc-crm.org/
2. Europeana http://www.europeana.eu/portal/en
   1. http://pro.europeana.eu/project/locloud
3. Research Space, British Museum
   1. http://www.researchspace.org/

Example of Data Management Life Cycle

You go on field work and collect photogrammetric and remote sensing data to develop a 3D experience of your field work. Consider the Project Management Triangle. How would you approach your project management to maximize your outputs considering re-usability or data and risk management.

Data you collect:

1. text forms
2. images
3. video
4. sound

What platform do you use? Where does you data life? What can you do with your data?