## 3 Establishing a Common Approach for Dataset-Based Learning
### Considerations of a Dataset for DBL  
As it has just been said, in a DBL learning activity, the learning process is built around a dataset. Thus, the characteristics of the dataset are crucial, and they are expected to affect activity development. The main characteristics of a dataset are the following: the size and complexity, the authenticity, how the students obtain it, and the format.  
It is important to note that the dataset definitions do not refer to its size nor the complexity. That is, a DBL activity can use datasets of different sizes and complexities, but the teacher must consider that the size of the dataset may influence the learning outcomes. In [12] (p. 1054), a large dataset is defined as a dataset \"that cannot be viewed in a single screen of a spreadsheet program without scrolling\"; in these cases, computational tasks such as summarizing and plotting the data are necessary. Thus, small datasets may be used when practising data management or the use of computing software [59; 60] are not the intended learning outcomes. For this reason, the use of relatively small datasets is widespread across undergraduate classrooms. Working with small datasets allows students to ask their own questions, design experiments or manipulate equipment, and generate and analyse their own data. Although these are important learning outcomes, it must also be noted that working with datasets that are limited in size or complexity does not give students the opportunity to practice data management, spreadsheet navigation skills, or hypothesis testing based on data. On the other hand, although working with large and complex datasets promotes the acquisition of data management skills [59], it also requires students to have some previous knowledge in computing. The lack of computing skills when working with datasets can result in student frustration, especially when dealing with extremely large datasets. For this reason, when designing a DBL activity, the students' previous knowledge must be considered to assess whether the dimension of the dataset is within the students' capabilities.  
It is highly encouraged to use authentic data that help to contextualize the activity in a real-world scenario, which engages the student [61; 62] and results in a more effective learning process [63]. Authentic data may be disorganized and noisy, which may obscure the purpose of the lesson [64]. For this reason, in some cases, the datasets may require previous work by the teacher to organize it and adapt it to the lesson. Nevertheless, messy data help students to think more deeply about data, making them work as detectives, arising discussion [64]. In addition, data variability provides an opportunity for discussion about data collection and quality assurance or quality control [12].  
The students can obtain the dataset from different sources. One option is that the teacher provides the datasets. A second option is that the students must search for the data in selected databases or on the Internet. It also worth considering that another approach is to have students collect their own data or generate their own dataset, for example, using experimental measurements in a laboratory or numerical results from a simulation. Generating datasets using simulations provides the chance to participate in practical and genuine learning activities, all without having to deal with various logistical challenges typically associated with field research, such as time consumption or instrumentation management. Depending on the origin, the data can be found in different formats, such as CSV, PDF, or TXT files. In order not to delay the activity, it is recommended that the students are familiar with the format. This may require the teacher to translate the dataset into a more user-friendly format [12].