LIFE4000: Semester 1a, 2023

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Units LIFE4000 Semester 1a 2023 [Block mode]

Unit outline_

LIFE4000: Data and Technology for the Life Sciences

Semester 1a, 2023 [Block mode] - Camperdown/Darlington, Sydney

Overview

Advances in digital technology are creating new ways to quantify biological processes and properties, from the scale of molecules to ecosystems. The life scientist of the 21st century needs to understand how to collect, manage, synthesise, and communicate this information within a reproducible workflow in order to make robust inferences about the natural world. This intensive unit of study will introduce you to key concepts and tools across three modules: digital project and data management, evidence synthesis and meta-analysis, and scientific coding using R. The focus is on active learning, discussion, and problem-solving across intensive workshop-based practicals, rather than the traditional lecture format. By completing this unit you will further understand the practical realities of scientific inquiry. To that end, you will develop a flexible skillet for conducting reproducible and open research to ensure the results of your work are maximally beneficial to both your future self and the broader community. Knowledge of how to work with data through the entire pipeline -from sampling to synthesis-will be useful wherever it is encountered in your education, career, and life.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	144 credit points of units of study including a minimum of 24 credit points at the 3000- or 4000-level and 18 credit points of 3000- or 4000-level units from Science Table A or 1
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Completion of units in quantitative research methods, mathematics or statistical analysis at least at 1000-level
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Thomas White, thomas.white@sydney.edu.au

Type	Description	Weight	Due	Length
Online task	Quiz Online Canvas quiz	15%	Week 04 Due date: 17 Mar 2023 at 23:59	60 minutes
Outcomes assessed:
Assignment	Data-driven news article Written task	35%	Week 05 Due date: 24 Mar 2023 at 23:59	1000 words
Outcomes assessed:
Assignment	Systematic review & map Written report	50%	Week 07 Due date: 06 Apr 2023 at 23:59	1500-2000 words
Outcomes assessed:

Assessment summary

Quiz: Brief, online quiz comprised of multiple-choice and short-answer questions to assess foundational knowledge.
Data-driven new article: Short writing and data-visualisation task.
Systematic review/map: A scientific report detailing the results of a student-designed systematic review and map.

Assessment criteria

Result name	Mark Range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 – 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard late penalties apply, as detailed in the unit outline.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 02	Prac 1a: Data processing and manipulation in R with the 'tidyverse'	Practical (3 hr)
Week 02	Prac 1b: Introduction to evidence synthesis	Practical (3 hr)
Week 03	Prac 2a: The principles and practice of datavis with `ggplot2`	Practical (3 hr)
Week 03	Prac 2b: Extracting data for evidence synthesis	Practical (3 hr)
Week 04	Prac 3a: Data analysis & statistics in R	Practical (3 hr)
Week 04	Prac 3b: Qualitative evidence synthesis: systematic reviews and maps	Practical (3 hr)
Week 05	Prac 4a: Creating flexible, reproducible documents with R Markdown	Practical (3 hr)
Week 05	Prac 4b: Quantitative evidence synthesis: meta-analysis	Practical (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 6 credit point unit, this equates to roughly 120-150 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 and apply the core principles of reproducibility and transparency in scientific research.
LO2. Manipulate, analyse, and visualise data, and integrate this knowledge in the production of reproducible 'living' documents.
LO3. Communicate critical, data-driven insights into biological processes orally and in writing.
LO4. Demonstrate an understanding of how to handle, store, and preserve primary biological data.
LO5. Assess the quality of existing evidence, and how to synthesise it using contemporary qualitative and quantitative methods

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.

More exercises of varied difficulty, adjusted the assessments to be even more relevant to hons students.

Additional information

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

Disclaimer

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