I. INTRODUCTION

Presently, Open Science receives more and more importance every day. It can be defined as the movement to make scientific investigation and everything that it contains (ideas, data, tests, model code, etc.), accessible to all levels of society. Specifically, in the scientific area, it plays an important job because it implies easy communication of knowledge so that everyone can be able to understand and repeat the process done with the aim to validate information and make research more transparent and collaborative.

However, because this widespread interest in Open Science has emerged relatively recently, we can look in the history that it all began in the 17th century with the advent of the academic journal, when the demand by society for access to scientific knowledge reached a point where it became necessary for groups of scientists to share resources [1] with each other so that they could collectively do their work. [2]

This article aims to be a guide for Open Science and is composed of the following sections: In section II I write about what knowledge means, in section IIII present the concept of Open Science – what it is and why is that important, as well as the meaning of open and how to put open science in practice. Finally, section IV offers conclusions.

II. THE KNOWLEDGE

Knowledge is important because it helps to push societies forward. When we think about it, a good information structure and a proper data processing helps us to use the information in a better way. However, the story does not end here, because to have information is just only the first step. To analyse this information, to manage it and use it, so that we can achieve the maximum benefit from it is what makes the usability of the information important.

a. What is Data?

Data is a symbolic representation (numeric, alphabetic, etc), an attribute or characteristic of an entity. Data describes empirical facts, events and entities. Alone, data in itself does not mean anything, however put into context, conveniently grouped, structured and interpreted data can be considered the basis of relevant information for humans that can be used in decision making, reducing uncertainty or performing calculations.

b. What is Information?

In general, information is an organised set of processed data, which is a message that changes the state of knowledge of the subject or system that receives the message. 

So, let us make an explanation with an example. We would like to make a “piña colada” drink:  

Data: It is a set of data about an event, fact, phenomenon or situation. In our example: A cup of ice, a cup of diced pineapple (frozen), pineapple juice, coconut cream, white rum, dark rum, pineapple slices. This represents a set of data, which by itself does not tell us much.

Information: Contains data organised in a given context. Following the previous example, if we collect the data: 1 1/2 cup ice, 1/2 cup diced pineapple, frozen, 2 ounces pineapple juice, 2 ounces coconut cream, 1 1/2 ounces white rum, 1 ounce dark rum, pineapple slices, we have as a result: information about what you will need to make a “piña colada” drink.

Presentation: It delivers the meaning. That is, it represents something for someone and its value depends on who, when, what and why uses that information. Returning to the previous example, having the information about what you’ll need to make a “piña colada” drink, you then can use this information to make one for your birthday party or any other event.

Knowledge: Its purpose may be to reduce uncertainty and increase understanding about something. That is, reduce the lack of secure and clear knowledge of something that allows a person to make decisions with greater probability of success. But on the other hand, it can also serve for a person to learn more about something and increase its knowledge (satisfying curiosity), but then does not give a specific use of that information.

In the world where we are living today, there are many kinds of data with many potential uses and applications and we will find it in culture, science, finance, environment, weather, statistics, etc.

III. OPEN SCIENCE CONCEPTS

As you can see open science evokes many different concepts and covers many different fronts. Speaking about it means speaking about open research, open data and open access (See figure 1.):

Figure 1. Open Science Concepts

  • Open Data (OD): the idea behind this movement is that some data (i.e public data, personal data should stay private for example), should be freely available to everyone without restrictions from copyright, to use and republish as they wish. See Diagram 1.
  • Open Access (OA): it refers to all investigations result being distributed online and free of cost. This concept often involves as well the addition of a Creative Commons license to help promote reuse. See Diagram 2.
  • Open Research (OR): the aim of this concept is concerned with making scientific research more collaborative, efficient and transparent. It is the practice of making the entire primary record of a research project publicly available online as it is recorded. See Diagram 3.

From this concepts above, we will quickly see that there is always that word OPEN coming through, and it means immediate, free availability of research outputs without restrictions on use as commonly imposed by publisher copyright agreements.