The Partnership in the Structural Genome Consortium (www.thesgc.org) aims to generate small molecule inhibitors of protein kinases. These molecules will be available to any research group in Brazil and the world. In this context, new initiatives of publication and peer review, such as the Peerage of Science, the arXiv and PLoS, have confirmed this trend, as has the example of the open encyclopedia (Wikipedia) among others, which was so criticized at its beginning yet has now emerged as one of the best Web 2.0 tools for the dissemination of knowledge [46].

In pursuit of this greater interaction and in reference to the "Year of Science", the development agency promoted open data as well as Wulder et al (2012). Agency researchers demonstrated that there was a gap of 30 years between the science reflected in research sources and cutting-edge developments being made in this field in other languages (Wulder, Masek, Cohen, Loveland & Woodcoock, 2012).

I.e., a case study done by Brazilian initiative in the encyclopedia open, Wikipedia, added thousands of words to Portuguese Wikipedia on topics related to the mathematical properties of neural dynamics. They expanded the article in Portuguese on Alzheimer's disease and added to articles on more complex issues such as brachial plexus injury. They also created an introduction to biological neuron models and created a video that explains "Spike sorting" – a way to track and measure the electrical properties of cells – that appears in both Portuguese and English Wikipedia (Mena-Chalco, Digiampietri, Lopes, & Cesar-Jr., 2013; Souza et al., 2015). In this model, experts and researchers work to explain concepts to volunteers in the research group in Brazil, who will then write articles based on the involvement of experts, since science is always in motion (Van Noorden, 2012). Its noteworthy that many of the authors are postdoctoral researchers (Hossain & Aydin, 2011; Kvan & Candy, 2000; Wilson, Ko, & Reis, 2011).

FIOCRUZ is a Brazilian institution that has created and encourages Open Science policy knowledge and dissemination of scientific information (Pinheiro, 2014). This organization aims to help strengthen the preservation of mechanisms of institutional memory and increase access to, and the impact of, the intellectual production of FIOCRUZ, thus creating an important tool to promote, in an organized and united way, the dissemination, accessibility and, consequently, the visibility of knowledge generated by this institution. Your Institutional Repository (ARCA – Brazilian term) is the main instrument for carrying out this policy in light of the objectives to collect, host, preserve, make available and give visibility to the scientific production of the institution. Thus, it is mandatory to deposit in the Ark Institutional Repository both dissertations and theses from the Fiocruz Graduate programs and articles produced under Fiocruz that are published in scientific journals. In addition to texts, the repository can hold images and audio. The ARCA repository should have the ability to integrate with national and international systems, which automatically enables the inclusion and collection of relevant intellectual products, noting in particular the defined protocols and standards of the Open Archives Initiative model (OAI) (Chaifetz et al., 2007; Turner et al., 2015).

Another example, the partnership of the

Fiocruz with Drugs for Neglected Diseases Initiative (Dndi) that resulted in a new drug for malaria diseases. As part of its quest to develop new and simplified treatments for neglected diseases, the project presented excellent results of the pivotal Phase III clinical studies for a new malaria treatment. Thus, the results shown that the new fixed dose combination of artesunate/mefloquine (AS/MQ) is as effective as the existing separate tablets and is better tolerated with a lower incidence of early vomiting. Because of the reduction of the number of tablets as well as the simplification of the regimen, the new product is more convenient for the patients and the prescribers. This was possible due to the sharing of data and synergy of multidisciplinary actions (“Malaria: A new fixed-dose combination of artesunate and mefloquine is effective and well tolerated. – DNDi”, [s.d.]; Santelli et al., 2012; Sirima et al., 2016; Valecha et al., 2013).

The European Union is taking a leap forward in opening up knowledge. A study was recently published in the form of book entitled Open innovation, Open Science, open to the world. This study was commissioned to the European Commissioner for Research, Innovation and Science by the President of the European Union. The publication presents the knowledge gap as naturally arising from the potential of new information technologies. It aims to address the knowledge gap to better use public resources invested in science and innovation to produce educational – among other – advantages. It adds further principles for addressing the knowledge gap through open innovation, Open Science and citizen science (EU Bookshop, 2016).

The publication suggests the use of a concept called a "Global Research Area". According to this concept, "researchers and innovators can work with international colleagues so that researchers, scientific knowledge and technology circulate as freely as possible." For Brazil, the desired technologies for teaching science and engineering are those that have the properties of the "Global Research Area", i.e., "scientific knowledge and technology circulate as freely as possible” (Bhardwaj et al., 2011).

In this context, it is worth noting the seminar held by NOVA University of Lisbon on June 23, 2016. This was a seminar on Big Data, Sustainable Development and Open Science, promoted by the Hygiene and Tropical Medicine Institute (IHMT) and Institut Français, in partnership with FIOCRUZ, Brazil, and the National Innovation Agency in Lisbon. The event was attended by 57 participants who attended the sessions in person and through streaming. At issue were two main themes: the application of big data research and the use of Open Science in technology transfer. In the context of the first theme, the uses of big data and open health information were analyzed. In the second, there was discussion of innovation for social inclusion and low-income contexts, as well as for automated patent analysis using an open source free software, as an example of data processing by Open Science: the crawler Patent2Net (http://patent2netv2.vlab4u.info/ ).

As a contribution to ways of managing the

Big Data in Global Health to decision makers, the figure 4 shows data processed using technological information via Open Science. Using the descriptor “dengue” in the "crawler" Patent2Net it was identified, extracted and correlated the technological information on patents within European Patents Office database (inside the universe of more than 90 million patents).

Figure 4
Infographic of Dengue Patent Analysis and Chikungunya

Several results about dengue disease were obtained and some core information for decision makers are summarized in the infographic (figure 4). In this analysis, 1427 documents of patents were recovered, through which, with the crossing of the data, it was possible to extract essential information such as countries assignees, existing technologies and their interactions, established networks, technology trends, inventors and their locations, networks etc. At the same time, using a private analysis (closed science) in the same infographic, the technological evolution and others information are visualized for the descriptor "chikungunya".

The opportunities of Open Science contribute to all areas of science. In the area of heath, the pharmaceutical industry is the most intense in research, development and innovation (R,D&I) (Basil Achilladelis, 2001; Chaves, Oliveira, Hasenclever, & Melo, 2007). Big Pharmas' advances and contributions to public health are surrounded by patent protection. However, the increase of collaborative intelligence among its actors has been observed over the years, whether in papers or in the establishment of research networks for the patenting of their products. Thus, it can be said that even in a scenario monitored by "confidential" research, knowledge management transcends barriers and aligns itself in the context of the research of the new century (Cattell, Chilukuri, & Levy, [s.d.]; Rafols et al., 2014).

A report delivered to the U.S. Congress in August 2012 defines big data as “a term that describes large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information” (Chluski & Ziora, 2015). Big Data analysis is already being used successfully in several countries including the United States, which has incorporated the concept in almost all its productive sectors. In 2014, the US government presented the report Big Data: Seizing Opportunities. This report was based on consultations with major US stakeholders such as Apple, IBM, Google, and Bank of America, among others, on issues concerning opportunities and values in the use of Big Data. It evaluated how this new phenomenon will change relations between government, citizens, businesses and consumers (The White House, 2016).

Big Data refers to the third generation of the information age (Magalhaes, JL & Quoniam, L, 2015; Raghupathi & Raghupathi, 2014). Initially, some characteristics of big data go beyond size and volume. This exponential data volume met the criteria of the 3 Vs: Volume, Variety and Velocity (Laney, 2001). Then, an additional 2 Vs were added: Value and, specifically to health care, Veracity. Some authors add a final set of 3 Vs – Verity, Versatility and Viability – such that the combination of all "Vs" generate the "V" of value (ALEIXO & DUARTE, 2015). This configures a great complexity of data. Complexity of the data source can be assessed in four different aspects: data structure, data format, data itself and hierarchies used in the data. The way in which the data is received, read, validated, processed and stored by this depends on the characteristics of the data. Complexity of the data structure means that there can exist various relations between data, including complex keys in tables, that makes it difficult to integrate various data tables (Kim, Kim, & Kim, 2016).

Volume is a reference to the amount of information that is available daily on the Web, while the term “variety” is characterized in different ways by different sources. Accuracy and speed are related to the processing of such data, which aims to make it useful, available in realtime, and reliable (Hilbert & López, 2011). Value is the attribute wherein the effective treatment of Big Data can generate access to essential information and save money for the organization in question.

According to Minelli et al (2013), Big Data is divided into the perfect data storm, the perfect storm of convergence, and the perfect storm of computing, the last of which is the result of four phenomena: Moore's Law, mobile computing, social networking and cloud computing. This data collection should be used to present information in a selectively researched and objective way to increase business intelligence and enable improvements in the decision-making process (Minelli, M., Chambers, M., & Dhiraj, A., 2013).

The velocity and data volume available in the virtual world have demonstrated the power of Big Data, as well as the possibility of using the results of this information in the area of competitive intelligence – that is, to produce more in less time, surpass competitors and save time. The result is that data processing has been a differentiator in decision making.

According to Magalhães et al (2013), out of the daily volume of data added to the web, 47% is related to health and, of this, 43% is related to public health (Magalhaes, JL & Quoniam, L, 2015). Therefore, it is necessary to develop new tools for the identification, extraction and Big Data analysis of health data both globally and locally, or “glocally”. According to Humbert (2005), the term glocal refers to the attitude of thinking about problems globally and acting locally, as glocal actions can have a global impact.

This concept reflects the current state of globalization, in which technology has made it possible to reach beyond a single context, thus encouraging the participation of enterprises located in suboptimal locations (Humbert, M., 2003).

The opportunities associated with data and analysis in different organizations have helped generate significant interest in business intelligence and analytics, which is often referred to as the techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions (Chen, Chiang, & Storey, 2012).

A report on the real-world use of big data conducted by IBM in collaboration with the University of Oxford showed that Big Data analysis makes organizations up to 23 times more likely to overcome their market competitors than those that do not analyze such data. However, some productive sectors, such as healthcare, finance, telecommunications, government and energy, are especially adaptable to Big Data strategies.

Health is considered mostly a global public good: it is not exclusive; no single person or community is excluded from its possession or consumption; and its benefits are available to everyone. There is also the apparent consensus that health is not competitive; that is, the health of one person does not come at the expense of excluding others (Buse & Waxman, 2001; Haines et al., 2009; Hartz, 2012; Vance, Howe, & Dellavalle, 2009).

In the current health scenario, stakeholders are embracing the concept of evidence-based medicine, which means making the best clinical decisions based only on the best scientific evidence available, which Big Data can now provide. In most cases, compiling datasets into Big Data algorithms is the best source for evidence, since distinction in subpopulations (such as the presence of patients with hypertension caused by Pheochromocytoma) could be intermittent enough that individual smaller datasets cannot offer enough evidence to point out which statistical differences are evident. Pioneers in using Big Data are already achieving positive results, prompting other stakeholders to act (Jee & Kim, 2013).

Global Health is an area of study, research and practice that has as a priority the attainment of equity in the health of the world population and includes, in addition to the priorities of the Sustainable Development Objectives, other areas related to fundamental aspects of health such as diseases Non-communicable chronic diseases (cardiovascular diseases, diabetes, etc.) that represent high disease loads worldwide; The social determinants of health; Climate change and maldistribution of human resources in health (Gagnon, 2011; Koplan et al., 2009; Univesity of California San Francisco - UCSF, [s.d.]).

These aspects are particularly relevant in 21st century for several reasons: we live in a world of global connectivity (media), with high mobility (global citizen), with epidemics (HIV, Ebola) and pandemics (flu). In this context, it is essential to have an approach based not only on the disease, but also on social, economic and cultural aspects. Thus, the global health area is distinct from public health, international health and tropical medicine, with broader goals for achieving health equity (Koplan et al., 2009).

The contribution of Big Data to Global Health through Open Science is aligned with Global Health 2035 – The Grand Convergence in Public Health is an effort to address and eliminate global disparities in infectious, child and maternal mortality rates (Cesario, 2016). According to Kaslow et al (2016), the effort reaches its aims; by 2035 the mortality rates of lower-income countries (LICs) and rural areas of middle-income countries (MICs) will converge with those of higher-income countries (HICs) and the bestperforming middle-income countries (MICs). Therefore, investment in health and developing a new investment framework to achieve dramatic health gains by 2035 creates opportunities for action for national governments of low-income and middle-income countries and by the international community (Kaslow et al., [s.d.]).

In this sense, to use Open Science in collaborative research and to identify, extract and correlate the Big data to Global Health presents itself as a path to the Global Health goals for 2035. This is an opportunity to anticipate a scenario of stagnant investments and no improvements in science, development and technology (Cesario, 2016; Jamison et al., 2013a; Terry et al., 2012; Univesity of California San Francisco - UCSF, [s.d.]; Wassan, 2001).

Many analyses of the relationship between globalization and health have considered health as a byproduct, as a spontaneous result – sometimes positive, sometimes negative – of strange globalizing forces, perhaps only motivated by other interests. Global Health is a desirable social goal today regardless of whether it is distorted by the influence of monetary fundamentalism. It deserves the best evidence, whether because of its intrinsic value or as a symbol of the dominance of human values over other interests (S. M. Paul et al., 2010).

Regarding this process, a cooperative network is necessary. According to Pierre Lévy (1994), "collective intelligence is an intelligence distributed throughout, constantly valued, coordinated in real time, resulting in effective mobilization of skills," which seeks the recognition and enrichment of people. The concept of collective intelligence was created based on the insights of Pierre Lévy (1994), and is related to intelligence technologies. It is characterized by a new form of sustainable thinking using the social connections that make feasible the use of the open networks of Internet computing. These intelligence technologies are represented in particular by the languages, sign systems, logical resources and instruments that we use. All our intellectual functioning is induced by these representations. Humans are unable to think alone and without the aid of any tools (A. Paul et al., 2017).

According to Bonabeau (2009), collective intelligence contributes strongly to the transfer of knowledge and power from the individual to the collective (Wang & Hajli, 2017). Open sources of collective intelligence will eventually generate results superior to the knowledge generated by the proprietary software developed within corporations. Education, and how people learn to participate in cultures outside of formal learning contexts, is crucial in this new global context. Learning through the means of collective intelligence is crucial; it is important for the democratization of science because it is linked to a culture based on knowledge and is sustained by sharing collective ideas, thus contributing to a better understanding of our diverse society (Gagnon, 2011; Jamison et al., 2013b; Janssen et al., 2017). Collective ideas may come from or flow into the science of institutions (Cesario, 2016; Kaslow et al., [s.d.]; Terry et al., 2012; Wassan, 2001).

In this sense, the creation of the collective knowledge environment for Global Health is justified by the development of innovative activities, such as these Collaborative Knowledge Networks (Cesario, 2016). This collective "open" learning environment produces positive effects on health outcomes if certain educational and institutional strategies are implemented. These reforms span the exploitation of the potential of Information and Communication Technologies (ICT) - as an example, Web 2.0 tools for the treatment of Big Data in health.