OLET2628: Intensive April, 2020

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Units OLET2628 Intensive April 2020 [Block mode]

Unit of study_

OLET2628: Research Data Management

Intensive April, 2020 [Block mode] - Camperdown/Darlington, Sydney

Overview

This unit will give you an insight into the exciting world of data. More and more we are bombarded with concepts like big data, open source data, and sensitive data. Data management is what links these concepts. Much of the development in data management has come from research in the physical, biological, and social sciences, research in languages, finance, law, and medicine, and many more. In all cases it is critical to ensure that data is safe and accessible. Importantly, lessons learnt from research are also applicable to managing our own data. Therefore, this unit introduces key concepts of data management delivered through 12 interactive online modules. The modules will initially define research data and explore the various flavours of research data. Then you will discover what can go wrong when you don't manage your data and explore ways which you can best store your data. You will look at using open source data, and how to best access and share data. And finally you will look at how to manage sensitive data and explore what big data is.

Unit details and rules

Unit code	OLET2628
Academic unit	Life and Environmental Sciences Academic Operations
Credit points	2
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator
Lecturer(s)	Liana Pozza, liana.pozza@sydney.edu.au
Lecturer(s)	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Online Quizzes Complete quizzes available in Canvas	5%	-	n/a
Outcomes assessed:
Assignment	Peerwise Online discussion	55%	Week 08	n/a
Outcomes assessed:
Assignment	Big Data Assignment Report	40%	Week 09	n/a
Outcomes assessed:

Assessment summary

Each lesson within each module contains quizzes and the successful completion of these will contribute towards 5% of your final mark for 2-credit point students and a badge for 0-credit point students.

For students taking the 2-credit point version of this course, there will be two assessments, one due in week 8 (Peerwise), and one in week 9 (Big Data Assignment).

The Peerwise assessment requires students to use the discussion tool known as ‘Peerwise’ to ask selected questions and answer questions asked by their peers. The assement will be graded based on interaction and quality of questions/answers.
The Big Data Assignment requires students to create an infographic or report explaining and discussing a selected statement about ‘Big Data’. The target audience for this task is a city council, so students will need to demonstrate their ability to explain complex concepts to a broader audience.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name	Mark Range	Description
High Distinction	85-100	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75-84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65-74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50-64	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty
Fail	0-49	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 05	Module 1: Why manage research data?	Individual study (4 hr)
Week 06	Module 2: How to manage research data	Individual study (4 hr)
Week 07	Module 3: Managed Data: Repositories, Data Access and Sharing	Individual study (4 hr)
Week 08	Module 4: The future	Individual study (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. explain the importance of metadata and the concept of data provenance to produce traceable and reliable research data
LO2. distinguish between open, closed and shared data, and identify associated access/usage conditions
LO3. identify components of a ‘dirty’ dataset and recognise resources available to help prevent and manage dirty data
LO4. recognise sensitive data and associated management protocols
LO5. apply appropriate citation methods to ensure proper acknowledgement of research data authors and sources
LO6. demonstrate understanding of ‘Big data’ and the potential management issues which may arise with large and complex data
LO7. employ visualisation and reporting as a tool to convey complex topics to a wider audience.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Subject is now offered as April Intensive/Block mode, instead of running over Semester 1.

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

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