Postgraduate Course: Scientific Project (STEM11011)
Course Outline
| School | Deanery of Clinical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | In this course students will have multiple opportunities to explore real scientific data. Multiple examples will be discussed and assessments would be drawn from a wide range of datasets including animal behaviour, immunohistochemistry, immunocyctochemistry, electrophysiological and molecular biology data. Students will analyse these datasets and gain feedback prior to assessments looking at:
1 data-handling skills
2 understanding of the background to the datasets
3 the relationship to the literature
4 how scientific experiments may be modified
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| Course description |
This course will allow students to experience and work with real scientific datasets as well as learning how they were generated and the statistical and ethical considerations required to plan/design them
This course will start with students going through scientific literature and breaking it apart before progressing to look at the individual component of a manuscript including results, methods, introduction and abstract and the discussion, with students given multiple opportunities to gain feedback on these components.
The assessments will be designed such that students can pick from the offered datasets and write a manuscript in stages
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2021/22, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
This course will be 100% assessed by coursework. Components of this assessment will be generated from a selection of the following: group presentation, ongoing assessment of discussion boards, analysis of scientific data or an essay question based around the key topics and learning outcomes to allow students to demonstrate their critical understanding and significant range of knowledge. Therefore a potential example of these assessments could be:«br /»
«br /»
1. 40% - dataset results (written 1500-2000 words)«br /»
students will be given a choice of scientific datasets and expected to present the data in figures and written up with appropriate statistical tests.«br /»
«br /»
2. 30% - dataset introduction (1000-1500 words)«br /»
Students will be expected to take the dataset that they have generated their results from and write an introduction to their results covering the background scientific literature and setting the scene for the scientific experiment«br /»
«br /»
3. 30% - dataset conclusion (1000-1500 words)«br /»
Students will be expected to take the dataset that they have generated their results and written their introductions to and write a conclusion/discussion which explores the strengths and weaknesses of their results and potential future work that could be done«br /»
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| Feedback |
Students will have feedback provided in multiple ways that will allow them to get feedback for all the individual components of this course via discussion boards and emails. This will include:
¿ Students submitting results and observations from small datasets ¿ allowing feedback to be given on data representation
¿ Students submitting aspects for methods, introductions and conclusions from given datasets ¿ allowing feedback on writing and researching the literature
Furthermore discussion boards will be used throughout the course and present multiple opportunities for students to gain feedback and input to their work and understanding. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of scientific datasets and how to analyse them
- Evaluate, criticise and appraise the literature around this topic
- Demonstrate their advancement in the basic research skills vital for presenting scientific data in a manuscript
- Communicate and engage with the course¿s concepts and principles with others outwith their own field
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Additional Information
| Graduate Attributes and Skills |
1. Due to working independently students must show a high degree of autonomy and time management skills to complete this course
2. Students will need to display a significant individual drive and determination to engage with this course and remain focused
3. Students will need to not only critically assess their own data with respect to drawing conclusions but this will also allow students to gain an understanding of real-life scientific data gathering and how to analyse and present that data |
| Keywords | Neurodegenerative diseases,stem cells,translation,immunohistochemistry,data analysis |
Contacts
| Course organiser | Dr David Hampton
Tel: (0131) 242 9421
Email: David.Hampton@ed.ac.uk |
Course secretary | Ms Catherine Renton
Tel: (0131 6)50 6410
Email: Catherine.Renton@ed.ac.uk |
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