STKD6800 Neuro-insights: Data Science Approaches in Neuroscience I Course description

Because most young researchers in life and health sciences do not have a solid quantitative background, they face difficulties when analyzing data independently. This difficulty represents a major drawback in research. Students waste time learning analytical methods by themselves that could be more quickly learned with proper instruction and support. Additionally, the lack of convention or standards in some fields is a source of confusion that slows the learning process. As consequence, the quality of insights and research productivity suffer. This course provides a comprehensive introduction to data science and big data applied to neuroscience research.

Its content is designed to train the participants in state-of-the-art techniques in data analysis and machine learning. This will enable the students to interact independently with the data and draw insights from them. The modules are organized so the participants have the opportunity to learn how to handle the most common data types (e.g., EEG, calcium imaging). Special attention is given to field-tested data management protocols, as they are critical for a fast transition from data acquisition to knowledge generation.

This is a hands-on course where the students will learn from implementing the analysis themselves with close supervision. The course will focus on case studies using data from real experiments; advanced students may choose to use their own data. The students will develop understanding through constant presentation of their work and dialectical reflection over their choices, results, and interpretations.

No prior knowledge required.

After completing this course the student should have the following learning outcomes:

Knowledge

On successful completion of this course, the student has knowledge of:

• Theoretical and practical aspects of data management and data processing for different data types (electrophysiology, imaging, and behavior), so they can independently engage with the state-of-the art literature.
• How statistics, dimensionality reduction, signal processing, calculus, and supervised and unsupervised learning may be applied to data analysis in neuroscience research.
• Commonly encountered problems in data science and the most trusted strategies to solve them.
• Basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

Skills

On successful completion of this course, the student has the ability to:

• Apply data analysis techniques to formulate a hypothesis, collect data, preprocess it, analyze it, and reach conclusions about the data.
• Identify and define a problem using data analytics.
• Critically assess the results.
• Identify new opportunities for organizational change including process improvements, cost reduction, or efficiency improvements.

General competence

On successful completion of this course, the student can apply:

• Data analysis principles to simple neuroscientific data.
• Methods and tools for data analysis and visualization.
• Heuristics and strategies commonly used in the research field to solve data analysis problems.

Sustainable development implies inter-, multi- and transdisciplinary encounters. This course will enable the PhD candidates to develop mixed methods and research design for a multitude of approaches. The course will introduce the candidates to different research methodologies in different research methods especially suitable to illuminate complex phenomena, including Responsible Research and Innovation (RRI), Universal design, Eco-Aesthetics, and holistic design solutions that can contribute to reconfiguration of the global society in a more sustainable direction. The candidates will learn to develop a research design appropriate for their PhD project and will acquire skills in research methods that are relevant to their projects. The syllabus may be abbreviated and adapted to fit the interest of the participants of the course in cooperation with the supervisors.

Completed Master’s degree (120 ECTS credits) or equivalent education level.

Upon completing the course, the candidates are expected to have gained the following learning outcomes (knowledge, skills, and general competence).

Knowledge

The candidate:

• has advanced knowledge about opportunities and challenges of inter- and transdisciplinary research
• has comprehensive knowledge about research ethics
• has a good understanding of Responsible Research and Innovation (RRI) and how to translate this into new, responsible practices.

Skills

The candidate:

• can reflect critically on strengths and weaknesses of various methods for production of knowledge
• can make a valid interdisciplinary, transdisciplinary or multidisciplinary research design
• has advanced skills in co-creation of knowledge
• can contribute to advanced collaboration in inter- and transdisciplinary disciplinary projects
• can analyse and reflect on ethical dilemmas in data collection

General competence

The candidate:

• can communicate in inter- and transdisciplinary teams
• can identify transfer value from empirical studies to other areas
• can translate the principles of Responsible Research and Innovation (RRI) into practice for socially and environmentally robust science and innovation

Lectures, workshops, fieldwork, group work, and individual work.

Active participation in the seminars is necessary to adequately understand the course material and themes. Participation is therefore mandatory, and candidates are expected to attend all days of teaching and required to attend at least 80 percent of teaching days. In special cases of documented illness, the course leader may accept exceptions to this requirement. In these cases, lack of participation can be substituted with alternative arrangements such as writing a reflection note.

Course requirements are assessed as confirmed or not confirmed. The course requirement must be completed and confirmed within the given deadline in order to have the right to submit a final essay.

The course requirements are:

• A plenary presentation on a subject decided in collaboration with the course lecturer.
• A prepared opposition to at least one other presentation.
• 80 % attendance is required

The course will introduce central theories and research traditions in media production and developments in the media field in the Norwegian and international contexts. "Media field" encompasses journalism, non-fiction writing, and other forms of media production and use, including in communication work.

The course is mandatory for all candidates in the specialization of Journalism and Media Studies, but candidates can apply for an exception if they have been admitted to another PhD course that gives an equivalent introduction to the theories and research traditions within the field of specialization and the doctoral dissertation’s subfield. It is recommended that the candidate completes the course as early as possible in their doctoral period.

Assessment is pass/ fail.