Background Many engineering education researchers acknowledge that their positionality impacts their research. Practices for reporting positionality vary widely and rarely incorporate a nuanced discussion of the impact of demographic identities on research. Researchers holding marginalized or relatively hidden identities must navigate additional layers regarding transparency of their positionality. Purpose We identify ways in which positionality impacts research, with a particular emphasis on demographic identity dimensions. We note that whether identities are relatively marginalized, privileged, hidden, or apparent in a research context creates complexities for conceptualizing, practicing, and disclosing one's positionality. Method In a collaborative inquiry informed by autoethnography, we assemble positionality reflections of current engineering education researchers to demonstrate the primary ways in which positionality impacts research. Results We find that positionality impacts six fundamental aspects of research: research topic, epistemology, ontology, methodology, relation to participants, and communication. These aspects of research delve deeper than conceptions of positionality as a methodological limitation, a measure to prevent bias, or a requirement for research quality. Conclusion The impact of positionality on research is complex, particularly when researchers occupy minoritized identities and for research topics that interrogate power relations between identity groups. By demonstrating the practices of interrogating and representing positionality, we hope to encourage more researchers to represent positionality transparently, thus making researchers' transparency safer for all. We argue that positionality is an important tool for reflecting on and dislocating privilege, particularly when working on equity research.

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.

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