Newsletter
Open Science has transformed ecology, moving from hidden codes and data in appendices to global platforms like GitHub and Global Biodiversity Information Facility (GBIF). These changes foster transparency, collaboration, and broader accessibility. However, challenges remain, from data and code standardization to shifts in academic culture. Embracing open practices will empower the next generation of ecologists to drive impactful, inclusive, and globally connected research.
Bionote
Marianna Chimienti is a lecturer in Marine Top Predator Ecology at Bangor University in the UK. Her academic journey spans several countries: completed her undergraduate and graduate studies in Italy, earned a Master of Research and PhD in the UK, undertook her first postdoctoral project in Denmark, and pursued a Marie Skłodowska-Curie Individual Fellowship in France before returning to the UK for her current role. This diverse international experience has shaped her commitment to transparency and inclusivity, which is evident in her support for Open Science practices and her encouragement of data and code sharing to foster collaboration.
How did it all start for me? I remember very clearly the days I was preparing the manuscript for my first PhD chapter. My supervisors said, “Marianna, did you prepare your codes so that someone can run it with their own data or the data you share? Let’s have one of the other PhD researchers check if it is understandable and easy to run.” I felt (and still feel) a mix of emotions. I was excited – “What? Me, really? People would actually use my codes to analyze their data?”. At the same time, I felt responsible for what I was publishing out there. It was important that I was clear and transparent. My journey in open science and open science practices started with my PhD (2013-2017), which is still evolving.
How open practices in data and code sharing have evolved in ecology
As a curious mind and a growing scientist, I eagerly wanted to know how to apply the analytical approaches I was reading about in scientific publications to my own data. Analytical codes were still published mainly as appendices to our scientific publications, and data repositories were just starting to appear and be adopted. We were limiting the access and transparency of our science.
The advent of digital platforms and repositories dramatically changed this landscape. We increasingly use GitHub for code sharing and collaborative project development, fostering an environment of transparency and reproducibility. Data sharing has gained momentum through established repositories like the Global Biodiversity Information Facility (GBIF), which provides access to vast datasets on species occurrences, and platforms such as Dryad and Zenodo that facilitate the storage and sharing of research data with persistent Digital Object Identifiers (DOIs). Large databases like Copernicus offer satellite data invaluable for ecological research, enabling scientists to monitor environmental changes at unprecedented scales.
Furthermore, Movebank serves as a data repository specifically for animal tracking data, underscoring the importance of tailored solutions in data management. This shift towards Open Science not only enhanced the accessibility of ecological data but also fostered inclusivity, allowing a broader range of researchers, including those from underrepresented groups, to engage in ecological research and collaborate.
Driving impact and sustainability
The impacts of these developments are palpable in our daily lives. Thanks to open science practices, I have developed my network as a scientist. Beyond my work, open science practices promote informed decision- making in environmental policy, conservation efforts, and sustainable practices. Enhanced transparency and data availability empower citizens, policymakers, and educators, fostering a more informed public regarding ecological issues.
Navigating upcoming challenges
The journey of Open Science is not without its challenges. One of the foremost challenges lies in fostering open science practices across generations of researchers. Be mindful of the effort that is required to collect those datasets, contact data owners, and offer collaboration. Analytical codes and platforms also evolve. Updating older codes and translating across languages (from R to Python, for example, or vice versa) is also an example of good practice. Equally significant is the need to standardize and connect the ever- growing number of approaches and datasets within ecology. The proliferation of data sources, while beneficial, has led to challenges in interoperability and data integration. Researchers often face difficulties in aligning different data formats, terminologies, and methodologies, which can hinder collaborative efforts and the synthesis of knowledge.
Collaborative efforts, such as developing community-led data standards and promoting interoperable data formats, are vital to overcome these obstacles. Initiatives like the Data Quality and Reporting Standards provide a framework for enhancing data quality and usability, facilitating seamless integration and comparison across studies.
As academic culture and reward systems often prioritize traditional publication metrics, it can be difficult to cultivate a mindset that values outputs such as published datasets and code repositories. I am pleased to see that these outputs are starting to be valued in funding calls.
As the ecological community continues to embrace Open Science, it will be imperative to create a culture that not only values data sharing but also supports the infrastructure necessary for effective collaboration and communication. This involves engaging diverse stakeholders, including researchers, educators, policymakers, and community members, in the conversation about Open Science. The commitment to transparency and inclusivity will remain critical in shaping the next generation of ecological research and its impact on society.
Marianna Chimienti
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School of Ocean Sciences, Bangor University, UK
m.chimienti@bangor.ac.uk
@MariannaKimient
