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KUB Datalab: Visualising

KUB Datalab

Tools

At KUB Datalab we use and support a large array of software.

We have tried to organise our main tools into categories below. Bear in mind, that many types of software can be used for multiple purposes. We have tried to categorise by main purpose.

Tools for visualising data

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Excel

Microsoft Excel allows users to organize, format and calculate data with formulas using a spreadsheet system. It features the ability to perform basic calculations, use graphing tools, create pivot tables and a macro programming language called Visual Basic for Applications, among other useful features.
Spreadsheet applications such as MS Excel use a grid of cells arranged in numbered rows and letter-named columns to organize and manipulate data. They can also display data as charts, histograms and line graphs.
MS Excel permits users to arrange data in order to view various factors from different perspectives. Microsoft Visual Basic is a programming language used for applications in Excel, allowing users to create a variety of complex numerical methods.

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GIS

GIS (Geographical Information System) is a software that can be used to work with geographic data, which makes sense to plot, analyse, visualise and present on maps. In GIS you work e.g. with measuring distances between points and summing data within a geographically delimited area. GIS is used in various creative ways in the humanities, social sciences and natural sciences.

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Metashape

Metashape is software used for photogrammetry. Metashape is not open source, but the library has the program installed on six machines. Software for photogrammetry is used in various exciting ways in many different areas from archeology, game design, VR and industry to reproduce three-dimensional models of objects. It is used for analysis, interpretation and dissemination.

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Orange

Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis.
Orange components are called widgets. They range from simple data visualization, subset selection, and preprocessing to empirical evaluation of learning algorithms and predictive modeling.
Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration.

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Python

Python is a programming language available under an Open Source license. It is smart to know a bit about python programming, partly because the programming language is becoming more and more widespread and used in research, partly because more analyzes in the humanities, social sciences and natural sciences depend on algorithms and calculations. KUB Datalab’s python courses deal with e.g. on analyzes of text data and web scraping.

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R

R is a programming language specifically designed for statistical data analysis. It is more or less the industry standard for explorative data analysis, data cleaning and visualization.
KUB Datalab offers courses, both general and tailored to specific needs in R. In our open workshops, we consults on how solve specific problems in and with R. You must find out on your own which statistical test you need to apply, and what visualization best suits your data. When that is decided – we will do our utmost to get you to your goal.
Our approach is based on the tidyverse. We find that Base R solutions, in general are more difficult for beginners to grasp. Close collaboration with our resident Python experts ensures that we are ready to switch gears if necessary.

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Voyant Tools

Voyant Tools is an Open Source software that can be used both online and offline, if you install it on your own computer. Voyant Tools can be used for text analysis. It houses several different tools that are combined on one platform. The program is useful for exploring, analysing and interpreting text collections and can be used for gaining insight into what different text analysis algorithms do and can do. It is most prevalent in the digital humanities, but will also be relevant in other areas such as digital social science.