Exploring NASA-TOPS in Community: Open Data

In the framework of the Pre-NASA TOPS Study Group, we gathered once again to review what we learned about open data and share previous experiences. Our community was eager to discuss the topic from contemporary and complex perspectives. Continuing the series of articles on our shared learning in the Study Group, we present a summary of the third meeting below.

Collaboration Takes Us Further

As we progress through the Open Science 101 modules, we notice the interconnections between the presented topics. For example, when dealing with open data, we need to consider the licenses we can use to share them and the importance of planning data management in a research project—topics covered in previous and subsequent modules. Resources such as persistent identifiers, licenses, metadata, and reproducible protocols form the foundation of open data.

All this is presented within the framework of the FAIR principles, discussing how different actors produce, use, and share data. In the third encounter, our community also deemed it essential to base our practices on the CARE principles when it comes to responsible use and governance of data in Latin America. This broadens data access and its potential uses, which is especially important in a context of socioeconomic scarcity and deep power inequalities.

Meeting the standards and protocols of Open Science is no simple task. In our discussion, we shared feelings of isolation and frustrating situations when trying to work with open data. As one participant reminded us, collaborative networks are a naturalized part of subsistence in Latin America. In an environment where data sharing is increasingly common and crucial for scientific and technological advancement, collaborative communities and networks are essential to mitigate the problem: we heard an example of a working group practicing anonymization or de-identification of personal data as strategies to protect individual privacy while facilitating data use and sharing for scientific and academic purposes, thus promoting their reuse and accessibility.

In this module, we also reviewed how to find, evaluate, and cite an open dataset, the use of repositories, the review of documentation and licenses, and how to cite correctly. Again, networks and communities can serve as a bridge to reliable and accessible data sources. One participant shared their experience in a project promoting the reuse of ecological microbiotas, an initiative that benefits researchers worldwide by providing access to valuable data and promoting collaboration as a strategy to tackle complex problems in ecology and microbiology.

Sharing Responsibly in Public and Academic Settings

Module 3 of the course illustrates how documentation, open formats, and credit attribution support responsible data usage and sharing within communities. This ensures preferences and criteria such as compatibility with other resources and tools and non-commercial use. Various actors (institutions, regulatory frameworks, funders, and others) influence the decision to share a dataset. Ideally, communities involved should be consulted and actions should be aligned with their political systems. Based on this consideration, we collectively discussed how open data is increasingly promoted in the public sector and academia.

Two participants shared their experiences with epidemic modelling using anonymized data, particularly the challenges and opportunities of sharing such data in public health and epidemiology. While protecting individuals’ privacy, anonymized data can facilitate scientific research and the development of effective strategies to address public health issues. Data management is a challenge in clinical settings, making it crucial to follow established protocols and regulations to ensure the confidentiality and integrity of information.

One of the complementary resources for our meeting, the ILDA Regional Open Data Barometer, recommends that governments invest in teams to implement open policies at all administrative levels consistently and sustainably. It is also necessary to improve data quality, taking special care to consider gender dimensions and other relevant variables, ensuring data includes all members of society. Since the module focuses on open data, we found relevant information on legislation and regulations related to the protection of personal and sensitive data. This provides an overview of resources and practical tools to better understand and comply with these standards, promoting a culture of ethics and responsibility in scientific research and professional practice.

However, with the advances of artificial intelligence (AI) in its various forms, ethical and legal standards must be updated to address these emerging technologies, always including the perspectives of local communities and the changes they experience with new technologies in their daily lives. In the realm of AI, it is essential to ensure fair and equitable access to available resources and data for all individuals and institutions involved, promoting equal opportunities and inclusive collaboration in the scientific and technological community.

As more potential pathways open to transform industries and fields of study, ethical and social challenges must be addressed collaboratively and responsibly. Proper management of massive data requires careful consideration of the involved risks and benefits, as well as adherence to ethical and legal standards in professional practice.

As practitioners of responsible and accessible open science, we aim to contribute to these delicate issues, orienting ourselves toward the benefit of our communities.

Do you want to reuse any of our content? Please, be our guest!

These were the materials we used in the second meeting of our study group (in Spanish):

• Presentation
• Shared document
• Recording

Our materials are available for free under this CC BY 4.0 license. You can reuse or edit any material that appears here. We only ask that you include a reference to this website or the material citation when available. For further information, please contact us at formacion@metadocencia.org.

Regarding the meetings

Between January and March 2024, 6 meetings are being held to explore the contents of the Open Science 101 course, which is part of the NASA TOPS initiative.

Ver detalles de los encuentros (in Spanish)

Acknowledgments

This publication was made possible thanks to a grant from the Chan Zuckerberg Initiative (DOI: 10.5281/zenodo.7386372) grants from NASA 80NSSC23K0854 (DOI: 10.5281/zenodo.8215455), 80NSSC23K0857 (DOI: 10.5281/zenodo.8250978), and 80NSSC23K0861 (DOI: 10.5281/zenodo.8212072), and the DAF2021-239366 grant and grant DOI https://doi.org/10.37921/522107izqogv from the Chan Zuckerberg Initiative DAF, a fund advised by the Silicon Valley Community Foundation (Funder DOI 10.13039/100014989) and the “Open Cloud Collaborative Project for Latin America and Africa (the Catalyst Project)” grant from the same funder (DOI: https://doi.org/10.5281/zenodo.8431422).

Did you like this publication? You can freely reuse it under a CC BY 4.0 license, just remember to cite it. This is the quote that we recommend you use to reference it:

Melissa Black, Nicolás Palopoli (2024). “Exploring NASA-TOPS in Community: Open Data”. Zenodo. https://doi.org/10.5281/zenodo.13327321