Ga naar de startpagina
Zoek leermateriaalKennis & expertiseCommunities
NL|EN
Privacy policyDeelnemende instellingen
Creative CommonsCreative Commons AttributionSURFFunded by the European Union

Filteren

Zoekresultaten (9)

HBO/WO

MBO

3 maart 2021

Communicating Research to the Public: Coronavirus, joggers, and social media

This case study asks students to use the Netherlands Code of Conduct for Research Integrity and Adina L. Roskies’ article “Neuroimaging and Inferential Distance” (2007) to discuss and assess a recent controversial example of communicating scientific findings to the public during Covid-19. Students prepare the materials in advance of class, where they work in small groups to assess the case in light of the two sources. The class reconvenes for a plenary discussion in which they generate tips and tops, or good and bad practices, for integrity in the communication of scientific findings in the era of social media.

Document

Samantha Marie Copeland, Andrea Gammon

23 augustus 2023

Tutorial: Research portfolio

Define what a scientific poster is and its role in academic and professional settings. Recognize the standard sections of a scientific poster, including title, abstract, introduction, methods, results, discussion, conclusion, and references. Understand the purpose of each section and its role in conveying research findings effectively. Explain the principles of visual hierarchy and how it guides the viewer's attention. Explore various layout options for organizing content logically and coherently. Summarize research findings succinctly without sacrificing essential information. Recognize the role of visual elements in making complex concepts more accessible to the audience. Recognize the importance of providing proper credit to sources and collaborators. Understand ethical considerations related to data presentation and research transparency. Understand the logistics of presenting a poster at conferences or events.

Website

HCAIM Consortium

2023

Maker Lab Assignment: Programming and Data Analysis: Python Exercises

Before the Maker Lab students begin the open inquiry phases, they first learn and practice some important skills, supported by small assignments. In this assignment they are tasked with using a worksheet in Jupyter Notebook and the Python programming language for data analysis and visualization and uncertainty analysis of a provided data set. The analyzed data set is generated quickly via an online survey completed by all students. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Contents: student worksheet in Jupyter Notebook: Programming-and-DataAnalysis_Python-Exercises.ipynb (and .pdf) instructor link to Google Forms survey for data collection: https://docs.google.com/forms/d/e/1FAIpQLSfWsdadhq-v3ubt1p3EIkzxAVyAyQ-1hBYst_RodV7PEp9E7A/viewform?usp=sharing example of data file: example_Data_Collection_For_Python_Exercises.csv

Document

Forrest Bradbury

2023

Lecture Notes chapter 1: Introduction to Jupyter Notebook and Markdown

This "Maker Lab" course uses Jupyter Notebook files (and the Python programming language) for data analysis and visualization and uncertainty analysis. Chapter 1 of the lecture notes introduces the Jupyter Notebook platform. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Maker Lab Lecture Notes

This collection of the Maker Lab's Lecture Notes (alongside the Lecture Sldies and several Assignments files) detail some of the learning material and activities that prepare students for the open inquiry phases. Specifically, students learn to use Python for data analysis & visualization and uncertainty analysis, and are given an overview into the processes of empirical research in the natural sciences. Note: These Lecture Notes files are written as Jupyter Notebooks, often containing interactive features (though static PDFs are also provided for the benefit of non-users of Jupyter Notebooks). It is possible and convenient to download all of the Maker Lab's Jupyter Notebook files at once via the JupyterPython-materials zip file - unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Forrest Bradbury

2023

Lecture Notes chapter 3: Uncertainty Analysis

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 3 of the lecture notes introduces Uncertainty Analysis using Python. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Lecture Notes chapter 2: Introduction to Python Programming

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 2 of the lecture notes introduces the Python programming language. It consists of four sub-chapters, each published as separate files. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize these .ipynb files. But the attached PDFs will give an idea of their content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Lecture Notes chapter 5: Fourier Transform

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 5 of the lecture notes introduces the discrete Fourier Transform using Python. This lecture notes document is presented in the Maker Lab course as optional reading, but sometimes relevant to students with projects involving (for example) signal analyses in the temporal frequency domain. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Lecture Notes chapter 4: Curve Fitting

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 4 of the lecture notes introduces Curve Fitting using Python. In fact, one of these documents covers Curve Fitting using two different Python libraries (lmfit and scipy), however, the lmfit library is the recommended one for the Maker Lab course, as it's more user-friendly and covers most uses needed by the student projects. Thus, the other document cuts out the scipy library explanations, keeping the document shorter and easier to follow. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury