Two decades of fumigation data from the Soybean Free Air Concentration Enrichment facility

  • Elise Kole Aspray
  • Timothy A. Mies
  • Elizabeth A. Ainsworth
Data Descriptor



  • Curated resources that support scientific research often go out of date or become inaccessible. This can happen for several reasons including lack of continuing funding, the departure of key personnel, or changes in institutional priorities. We introduce the Open Data, Open Code, Open Infrastructure (O3) Guidelines as an actionable road map to creating and maintaining resources that are less susceptible to such external factors and can continue to be used and maintained by the community that they serve.

    • Charles Tapley Hoyt
    • Benjamin M. Gyori
    CommentOpen Access
  • The solution of the longstanding “protein folding problem” in 2021 showcased the transformative capabilities of AI in advancing the biomedical sciences. AI was characterized as successfully learning from protein structure data, which then spurred a more general call for AI-ready datasets to drive forward medical research. Here, we argue that it is the broad availability of knowledge, not just data, that is required to fuel further advances in AI in the scientific domain. This represents a quantum leap in a trend toward knowledge democratization that had already been developing in the biomedical sciences: knowledge is no longer primarily applied by specialists in a sub-field of biomedicine, but rather multidisciplinary teams, diverse biomedical research programs, and now machine learning. The development and application of explicit knowledge representations underpinning democratization is becoming a core scientific activity, and more investment in this activity is required if we are to achieve the promise of AI.

    • Christophe Dessimoz
    • Paul D. Thomas
    CommentOpen Access
  • As the number of cloud platforms supporting scientific research grows, there is an increasing need to support interoperability between two or more cloud platforms. A well accepted core concept is to make data in cloud platforms Findable, Accessible, Interoperable and Reusable (FAIR). We introduce a companion concept that applies to cloud-based computing environments that we call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require data and platform governance structures and are designed to support the interoperability of sensitive or controlled access data, such as biomedical data. A SAFE environment is a cloud platform that has been approved through a defined data and platform governance process as authorized to hold data from another cloud platform and exposes appropriate APIs for the two platforms to interoperate.

    • Robert L. Grossman
    • Rebecca R. Boyles
    • Stan Ahalt
    CommentOpen Access
  • The ongoing debate on secondary use of health data for research has been renewed by the passage of comprehensive data privacy laws that shift control from institutions back to the individuals on whom the data was collected. Rights-based data privacy laws, while lauded by individuals, are viewed as problematic for the researcher due to the distributed nature of data control. Efforts such as the European Health Data Space initiative seek to build a new mechanism for secondary use that erodes individual control in favor of broader secondary use for beneficial health research. Health information sharing platforms do exist that embrace rights-based data privacy while simultaneously providing a rich research environment for secondary data use. The benefits of embracing rights-based data privacy to promote transparency of data use along with control of one’s participation builds the trust necessary for more inclusive/diverse/representative clinical research.

    • Scott D. Kahn
    • Sharon F. Terry
    CommentOpen Access
  • Data harmonization is an important method for combining or transforming data. To date however, articles about data harmonization are field-specific and highly technical, making it difficult for researchers to derive general principles for how to engage in and contextualize data harmonization efforts. This commentary provides a primer on the tradeoffs inherent in data harmonization for researchers who are considering undertaking such efforts or seek to evaluate the quality of existing ones. We derive this guidance from the extant literature and our own experience in harmonizing data for the emergent and important new field of COVID-19 public health and safety measures (PHSM).

    • Cindy Cheng
    • Luca Messerschmidt
    • Joan Barceló
    CommentOpen Access