https://dialnet.unirioja.es/descarga/articulo/4511387.pdf

The origins of order. Self-organization and selection in evolution

https://purews.inbo.be/ws/files/12909626/Nesshover_etal_2017_SciTotalEnviron.pdf

The science, policy and practice of nature-based solutions : An interdisciplinary perspective

http://www.ccs.fau.edu/~tognoli/TK_2014Neuron.pdf

The metastable brain.

Multistable coordination dynamics exists at many levels, from multifunctional neural circuits in vertebrates and invertebrates to large-scale neural circuitry in humans. Moreover, multistability spans (at least) the domains of action and perception, and has been found to place constraints upon, even dictating the nature of, intentional change and the skill-learning process. This paper reviews some of the key evidence for multistability in the aforementioned areas, and illustrates how it has been measured, modelled and theoretically understood. It then suggests how multistability—when combined with essential aspects of coordination dynamics such as instability, transitions and (especially) metastability—provides a platform for understanding coupling and the creative dynamics of complex goal-directed systems, including the brain and the brain–behaviour relation.

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2011.0351

Multistability and metastability: understanding dynamic coordination in the brain

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2013.0521

Graph analysis of functional brain networks: practical issues in translational neuroscience

Cultures from all over the world have developed different views of nature throughout human history. Many of them are rooted in traditional systems of beliefs, which indigenous people use to understand and interpret their biophysical environment (Iaccarino, 2003). These systems of managing the environment constitute an integral part of the cultural identity and social integrity of many indigenous populations. At the same time, their knowledge embodies a wealth of wisdom and experience of nature gained over millennia from direct observations, and transmitted—most often orally—over generations.

The importance of this traditional knowledge for the protection of biodiversity and the achievement of sustainable development is slowly being recognized internationally (Gadgil et al , 1993). For example, Article 8 of the Convention on Biological Diversity urges us to “…respect, preserve and maintain knowledge, innovations and practices of indigenous and local communities embodying traditional lifestyles relevant for the conservation and sustainable use of biological diversity….” (United Nations, 1992). In addition, traditional or indigenous knowledge has been rediscovered as a model for a healthy interaction with, and use of, the environment, and as a rich source to be tapped into in order to gain new perspectives about the relationship between humans and nature.

However, our difficulty in approaching the knowledge from indigenous cultures is already reflected in the way in which we describe and name it. No universal definition is available, and many terms are used to establish what indigenous people know (Berkes, 1993), including traditional knowledge or traditional ecological knowledge, local knowledge, indigenous knowledge or science, folk knowledge, farmers' knowledge, fishers' knowledge and tacit knowledge. Each of these terms carries different implications, and there is an ensuing discussion about which one is the most appropriate. The word ‘traditional’, for example, places the emphasis on the transmission of knowledge along a cultural continuity, but might ignore …

/pdf/western-science-and-traditional-knowledge-despite-their-1jnagwlcvm.pdf

Western science and traditional knowledge. Despite their variations, different forms of knowledge can learn from each other.

The ultimate aim of scientific research is to understand the natural world. In order to achieve this goal, Western science has relied on different cognitive strategies, including simplification, both in terms of analysis and explanation. As the British natural philosopher Sir Isaac Newton (1643–1727) put it, “Truth is ever to be found in the simplicity, and not in the multiplicity and confusion of things.” In a way, examples of simplification include using idealized models, such as a ‘perfect sphere rolling down a smooth plane in a vacuum’; conducting experiments in a strictly controlled environment such as the laboratory; analysing complex systems by reducing them to their individual parts; and generally by using a linear and deterministic concept of how the world, including life, works.

The French philosopher and mathematician Rene Descartes (1596–1650) was the first to introduce reductionism to Western thinking and philosophy. According to his view, the world can be regarded as a clockwork mechanism; to understand it, one need only investigate the parts and then reassemble each component to recreate the whole. Descartes' work was expanded by Newton (1643–1727) and ultimately culminated in the Principia Mathematica in 1687—one of the most influential science books ever written—in which Newton further advanced the idea of a ‘clockwork universe’.

Since the time of Newton, classical mechanics has been regarded as the foundation of scientific research. Scientists, including biologists, have adopted the Newtonian approach both at the ontological level—in terms of their conception of the world and the things of which it is made—and the epistemological level—in terms of their approach to understanding those things. The Newtonian epistemology, in fact, states that scientific knowledge has to provide an objective representation of the external world. The world's apparent complexity can be resolved by analysis and reducing phenomena to their simplest components. “Once you …

Complexity in biology: Exceeding the limits of reductionism and determinism using complexity theory

Afew years ago, Chris Anderson, former editor in chief of Wired magazine, published a provocative and thought‐provoking article: “The end of theory: the data deluge makes the scientific method obsolete” (http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory/). As the title indicates, Anderson asserted that in the era of petabyte information and supercomputing, the traditional, hypothesis‐driven scientific method would become obsolete. No more theories or hypotheses, no more discussions whether the experimental results refute or support the original hypotheses. In this new era, what counts are sophisticated algorithms and statistical tools to sift through a massive amount of data to find information that could be turned into knowledge.

> … [an] imagined future in which the long‐established way of doing scientific research is replaced by computers that divulge knowledge from data at the press of a button…

Anderson's essay started an intense discussion about the relative merits of data‐driven research versus hypothesis‐driven research that has much relevance for many areas of research, including bioinformatics, systems biology, epidemiology and ecology. Yet, his imagined future in which the long‐established way of doing scientific research is replaced by computers that divulge knowledge from data at the press of a button deserves some inquiry from an epistemological point of view. Is data‐driven research a genuine mode of knowledge production, or is it above all a tool to identify potentially useful information? Given the amount of scientific data available, is it now possible to dismiss the role of theoretical assumptions and hypotheses? Should this new mode of gathering information supersede the old way of doing research?

The scientific method encompasses an ongoing process of formulate a hypothesis‐test with an experiment–analyze the results‐reformulate the hypothesis. Such a way of proceeding has been in use for centuries and is basically accepted in our Western society as the most reliable way to produce robust knowledge.

However, Anderson is not the …

/pdf/could-big-data-be-the-end-of-theory-in-science-a-few-remarks-3rfvzquuhe.pdf

Could Big Data be the end of theory in science? A few remarks on the epistemology of data-driven science.

EMBO Reports (2019) e47682

Contemporary research is increasingly characterized by two contrasting trends [1]. One is a process of increasing and continuous specialization, which requires scientists to attain a congruent degree of expertise in a particular area of research. This trend is reflected in the proliferation of new scientific disciplines, and their further division into subfields. The other trend, which developed over the past decades, is increasing cooperation not only at an intradisciplinary level, but also across and beyond disciplines: that is, multi‐, inter‐ and trans‐disciplinary research. The aim is to bring together scientists with different expertise and resources, with the possibility of cross‐fertilizing each other and to develop new, synthetic views.

### The rationale for involving multiple disciplines

The need for involving several different disciplines arose as scientists realized that particular problems are too complex to be effectively addressed by a single field of study. An obvious example is climate change along with environmental challenges, sustainable development and the societal implications. It requires the competencies and tools from multiple disciplines—natural sciences, engineering and social sciences—to study the causes and effects and develop solutions.

It is also recognized that many systems or phenomena can and should be investigated at different levels and from different points of view, given their multidimensional nature. Take for example human beings, which can be referred to as physical, chemical, biological, cognitive, and sociocultural objects [2]. Each level of organization raises specific issues that should be studied through appropriate strategies and methods, along with the interactions between different levels. Generally, instead of being disciplinary oriented, another way of conceiving scientific investigation is phenomenon‐ or object of study‐oriented.

Multidisciplinarity and interdisciplinarity have also become important for research policy, as exemplified by European Research Council's initiatives, and numerous areas of study, including science education and research management. Various research institutions around the world, such …

/pdf/scientific-research-across-and-beyond-disciplines-challenges-139ks60d3q.pdf

Scientific research across and beyond disciplines: Challenges and opportunities of interdisciplinarity.

At the beginning of the twentieth century, a range of seemingly unexplainable observations challenged the Newtonian view of the physical world and plunged physics into a major crisis. This crisis and the resulting intellectual advances led to the birth of quantum mechanics in the 1920s, a theoretical field that provided a new framework for the explanation of phenomena that were defying classical laws of mechanics. One of the main insights that emerged from quantum physics was the principle of complementarity, which Niels Bohr (1885–1962) formulated to explain the fact that, in quantum physics, two theories regarded as mutually exclusive are required to explain a single phenomenon (Bohr, 1958).

In particular, Bohr was dealing with wave–particle duality: light exhibits different properties depending on the means of observation. Some phenomena, such as diffraction patterns, are best explained if light is considered as electromagnetic waves, whereas others, such as the photoelectric effect, require that light behaves like particles. Bohr's principle of complementarity established that, for investigations at the subatomic level, light could be considered as either, depending on the methods of measurement; that these two descriptions should not be regarded as contradictory, but as complementary; and that it is not possible to formulate a synthesis of the two.

Interestingly, Bohr also believed that complementarity could be applied to contexts other than quantum mechanics and the realm of subatomic particles. In a lecture titled Light and Life that he gave to a congress on light therapy in Copenhagen in 1932 (Bohr, 1933), he proposed that a notion of complementarity might be needed for understanding biological phenomena. Bohr suggested that experiments to analyse the molecular properties of organisms and biological functions were basically incompatible—the complex organization of living systems cannot, in fact, be preserved under the experimental conditions of a complete quantum mechanical analysis …

/pdf/complementarity-in-biology-2we39cqqja.pdf

Fulvio Mazzocchi

Papers

Western science and traditional knowledge. Despite their variations, different forms of knowledge can learn from each other.

Complexity in biology: Exceeding the limits of reductionism and determinism using complexity theory

Could Big Data be the end of theory in science? A few remarks on the epistemology of data-driven science.

Scientific research across and beyond disciplines: Challenges and opportunities of interdisciplinarity.

Complementarity in biology