2019-09-04, 15:45–16:00, Track 2 (Baroja)
A tool for ecological research, environmental education and digital literacy. The aim of fractaL is to bring the ecological studies to a broad audience using an intuitive approach through synesthetic methods associated to a robust scientific base.
Currently we witness a strong change in the climate of the planet caused by human activities that affects both living and non-living environmental components in a velocity and intensity as never seen before. Additionally, the lack of a shared scientific and technological knowledge combined with the fact that people are not participants or creators of content, result in a passive condition where these fields are centralized on a small group that in general ignores the social realities.
In light of these scenarios, fractaL purposes an integrative perspective focused on the ecological issues and the use of free-open technologies as tools for learning, practices and applications of knowledge on environmental research. Built up by an interdisciplinary group, fractaL aims to open up new ways using a complementary approach of numbers (environmental data) with the uncountable (synesthesia using the fractal principles) in which cognitive aspects are activated, and where critical thinking and knowledge are put in practice.
Considering these, fractaL choose to base its code on Python and use the concept of fractal geometry, defined by the spatial heterogeneity, complexity at different scales. This “roughness” that is characteristic in natural structures is far from the smooth structures and shapes created by human and has revolutionized not only mathematics, but also ecology and environmental studies because attempted to understand chaotic and unpredictable processes that has reached an unprecedented capability to explain more complex and systemic issues once constrained by classical Euclidean paradigms. These contributions to ecological studies is used as the base to the synesthetic methods and artistic tools in order to bring the ecological studies to a broad audience, as well as to provide a solid tool for researchers already literate in Python.
The fractaL’s code is developed to ensure a strong tool for both data analysis and learning. Accordingly, our intention is to build a code where experienced and beginner users can access, study and use it for their needs. Thus, the code brings both a set of functions dedicated to Synesthetic representation and a set with the most frequent data and statistical analysis used in environmental studies, as well as to fractal dimensions. The objective is to cover a big number of environment dataset which can be accessed from open data initiatives and from monitoring systems developed as a complementary part of the project.
The functions dedicated to exploring and analyzing data use mainly the scipy, numpy, pandas and matplotlib applied to data analysis and descriptive statistic. In this case, fractaL uses the numpy, scipy and matplotlib for descriptive analysis, distribution tests, data manipulation, visualization and fractal analysis. The pandas is used for processing JSON and CSV files, time series, data frame, and panel.
The synesthetic implementation will be divided in two groups: (1) a non-interactive exhibition based on dataset and sensor information, and (2) environmental impact on the ecosystem dynamic using simulation for interactive installations. For the first 10 months the objective is to focus on the non-interactive approach. The set of the synesthetic tools has been developed in parallel to the data analysis development since the early stages of fractaL. With them, the project intend to offer a strong set of tool for each step of environmental data analysis, sonification and visualization of data.
This set of tools dedicated to synesthesia is composed by the code for image representation using Processing.py, an efficient and powerful tool for image projections and interactive visualization. On the other side the code for sonification is built using mainly on the modules MIDITime, Pygame, scipy, numpy and pandas.
The Learning and Participatory Research
Citizen Science and Digital Literacy play a key-role in environmental research, as already observed in projects that have been developed in the last decades. The main challenges we had faced when the project started were how to promote science and scientific method, how develop technical skills related to digital literacy and in parallel achieve the social and gender equity. Thus, the project has two lines: (1) the learning activity based on workshops and (2) the research practices where the project can interact with open data platforms and also with other projects directly related, as “Hacking Ecology”. Both research and learning are characterized by the integration of science, open source technology and synesthesia.
The learning activities cover the principles of scientific research using workshops focused on a small project development for each meeting. The focus of the workshop will be the analysis of environmental data, combining the traditional application of data analysis and the synesthetic techniques using sonification and visualization. The analysis performed with Python use a dataset composed of a massive amount of data covering more than 10 years of ecological research in coastal lagoons, lakes, rivers, reservoirs and microcosms.
As a way to connect the communities since the early step, fractaL has developers from Rio de Janeiro and Quilmes that also promote and implement smaller version of the meetings in these cities. Also, the documentation for each step is a key-factor that will make possible the project replications by different groups interested in using fractaL for environmental data. This systemic perspective on many steps ensure its use in synesthetic tests, as well as with other projects and open source monitoring devices.
The research activities also focus the analysis of data on open source monitoring systems initiatives (e.g. Smart Citizen, Lufdaten.info) which are used during workshops promoting scientific literacy and participatory research. In this way, the project has an academic body of peer-reviewers from both Theoretical and Ecosystem Ecology, Education and Python that validates the code and its scientific robustness.
During the conceptual framework development the group had faced the challenges of create a project gathering together aspects that are usually avoided when working with scientific research. Although the use of programming languages is greatly adopted by the scientific community, the association with artistic experiment is still finding a resistance.
Mainly, when an artistic approach is considered, it is used in a “playful” or “gamified” way. The artistic dimension in fractal, without forgetting an entertaining educational proposal, remains tidily attached to scientific rules, data analysis, and environmental research. Doing so, fractaL aims to enable and facilitate the introduction of big questions in ecology, and the concepts of data analysis and programming language to a broad audience.
There is an amazing potential associated to fractaL that makes the project an already viable tool for been applied in ecological research and education fields for young researchers starting in ecological studies and interested in enhancing their computing language skills. In a medium term perspective, we expect to develop fractaL to use it in a broader environmental analysis and its integration with monitoring systems.