Dynamic World, Near real-time global 10m land use land cover mapping

  • Christopher F. Brown
  • Steven P. Brumby
  • Alexander M. Tait
Data Descriptor

Announcements

  • Covid-19 resources

    A collection presenting a series of rapidly evolving resources that aggregate and bring cohesion to the massive volume of data being generated in the COVID-19 crisis

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  • With increased availability of disaggregated conflict event data for analysis, there are new and old concerns about bias. All data have biases, which we define as an inclination, prejudice, or directionality to information. In conflict data, there are often perceptions of damaging bias, and skepticism can emanate from several areas, including confidence in whether data collection procedures create systematic omissions, inflations, or misrepresentations. As curators and analysts of large, popular data projects, we are uniquely aware of biases that are present when collecting and using event data. We contend that it is necessary to advance an open and honest discussion about the responsibilities of all stakeholders in the data ecosystem – collectors, researchers, and those interpreting and applying findings – to thoughtfully and transparently reflect on those biases; use data in good faith; and acknowledge limitations. We therefore posit an agenda for data responsibility considering its collection and critical interpretation.

    • Erin Miller
    • Roudabeh Kishi
    • Caitriona Dowd
    Comment Open Access
  • The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) is a multinational interdisciplinary endeavor of a large earth system sciences community.

    • Stephan Frickenhaus
    • Daniela Ransby
    • Marcel Nicolaus
    Comment Open Access
  • The biomedical research community is investing heavily in biomedical cloud platforms. Cloud computing holds great promise for addressing challenges with big data and ensuring reproducibility in biology. However, despite their advantages, cloud platforms in and of themselves do not automatically support FAIRness. The global push to develop biomedical cloud platforms has led to new challenges, including platform lock-in, difficulty integrating across platforms, and duplicated effort for both users and developers. Here, we argue that these difficulties are systemic and emerge from incentives that encourage development effort on self-sufficient platforms and data repositories instead of interoperable microservices. We argue that many of these issues would be alleviated by prioritizing microservices and access to modular data in smaller chunks or summarized form. We propose that emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. We challenge funders, developers, and researchers to support a vision to improve interoperability through microservices as the next generation of cloud-based bioinformatics.

    • Nathan C. Sheffield
    • Vivien R. Bonazzi
    • Andrew D. Yates
    Comment Open Access
  • In response to COVID-19, governments worldwide are implementing public health and social measures (PHSM) that substantially impact many areas beyond public health. The new field of PHSM data science collects, structures, and disseminates data on PHSM; here, we report the main achievements, challenges, and focus areas of this novel field of research.

    • Cindy Cheng
    • Amélie Desvars-Larrive
    • Sophia Alison Zweig
    Comment Open Access
  • Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective.

    • Malin Sandström
    • Mathew Abrams
    • Wojtek J. Goscinski
    Comment Open Access
  • The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets.

    • Martin Norgaard
    • Granville J. Matheson
    • Melanie Ganz
    Comment Open Access

Infrastructure risk and disaster data

This collection brings together a series of works that aim to facilitate the understanding and prevention of disasters involving critical infrastructures. Leading the collection is a series of papers sharing data on the environmental impact of the nuclear accident in Fukushima. The additional papers in the collection provide data related to a broad range of critical infrastructures and enable the assessment and mitigation of possible infrastructure disaster hazards and risks.
Collection

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