When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data.

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this "no consensus" stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular "V" characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the "V" characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

Increasing expression of dual-specificity phosphatase 12 mitigates oxygen-glucose deprivation/reoxygenation-induced neuronal apoptosis and inflammation through inactivation of the ASK1-JNK/p38 MAPK pathway.

Dual-specificity phosphatase 12 (DUSP12) is abnormally expressed under various pathological conditions and plays a crucial role in the pathological progression of disorders. However, the role of DUSP12 in cerebral ischaemia/reperfusion injury has not yet been investigated. This study explored the possible link between DUSP12 and cerebral ischaemia/reperfusion injury using an oxygen-glucose deprivation/reoxygenation (OGD/R) model. Marked decreases in DUSP12 levels have been observed in cultured neurons exposed to OGD/R. DUSP12-overexpressed neurons were resistant to OGD/R-induced apoptosis and inflammation, whereas DUSP12-deficient neurons were vulnerable to OGD/R-evoked injuries. Further investigation revealed that DUSP12 overexpression or deficiency affects the phosphorylation of apoptosis signal-regulating kinase 1 (ASK1), c-Jun NH2-terminal kinase (JNK), and p38 mitogen-activated protein kinase (MAPK) in neurons under OGD/R conditions. Moreover, blockade of ASK1 diminished the regulatory effect of DUSP12 deficiency on JNK and p38 MAPK activation. In addition, DUSP12-deficiency-elicited effects exacerbating neuronal OGD/R injury were reversed by ASK1 blockade. In summary, DUSP12 protects against neuronal OGD/R injury by reducing apoptosis and inflammation through inactivation of the ASK1-JNK/p38 MAPK pathway. These findings imply a neuroprotective function for DUSP12 in cerebral ischaemia/reperfusion injury.