Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies.

Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called "big data" are collected and used at unprecedented scales with the idea that algorithms can be designed to aid in chemical discovery. As data-enabled practices become ever more ubiquitous, chemists must consider the organization and curation of their data, especially as it is presented to both humans and increasingly intelligent algorithms. One of the most promising organizational schemes for big data is a construct termed an ontology. In data science, ontologies are systems that represent relations among objects and properties in a domain of discourse. As chemistry encounters larger and larger data sets, the ontologies that support chemical research will likewise increase in complexity, and the future of chemistry will be shaped by the choices made in developing big data chemical ontologies. How such ontologies will work should therefore be a subject of significant attention in the chemical community. Now is the time for chemists to ask questions about ontology design and use: How should chemical data be organized? What can be reasonably expected from an organizational structure? Is a universal ontology tenable? As some of these questions may be new to chemists, we recommend an interdisciplinary approach that draws on the long history of philosophers of science asking questions about the organization of scientific concepts, constructs, models, and theories. This Perspective presents insights from these long-standing studies and initiates new conversations between chemists and philosophers.

Growing knowledge: Epistemic objects in agricultural extension work.

We introduce a novel form of experimental knowledge that is the result of institutionally structured communication practices between farmers and university- and local community-based agronomists (agricultural extension specialists). This form of knowledge is exemplified in these communities' uses of the concept of grower standard. Grower standard is a widely used but seldom discussed benchmark concept underpinning protocols used within agricultural experiments. It is not a one-size-fits-all standard but the product of local and active interactions between farmers and agricultural extension specialists. Grower standard is in some ways similar to more familiar epistemic objects discussed in philosophy of experiment, such as controls or background conditions. However, we argue that grower standard is epistemically novel, due to how knowledge arising from it is coproduced by farmers and agricultural extension specialists. Further, in the United States, this knowledge coproduction is institutionally structured by federal legislature dating back to the 19th century. We use our analysis of grower standard to focus a discussion of the positionality of the coproducers as well as the epistemic products of this form of knowledge coproduction, and we explore the role extension work plays in shaping agricultural science more broadly.

Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts: A systematic review.

BACKGROUND: This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing odontogenic cysts. MATERIAL AND METHODS: A systematic review was conducted according to the PRISMA statements and considering eleven databases, including the grey literature. Protocol was registered in PROSPERO (CRD [Blinding]). The PECO strategy was used to define the eligibility criteria and only studies involving diagnostic accuracy were included. Their risk of bias was investigated using the Joanna Briggs Institute Critical Appraisal tool. RESULTS: Out of 437 identified citations, five papers, published between 2006 and 2019, fulfilled the criteria and were included in this systematic review. A total of 5,264 images from 508 lesions, classified as radicular cyst, odontogenic keratocyst, lateral periodontal cyst, glandular odontogenic cyst, or dentigerous cyst, were analyzed. All selected articles scored low risk of bias. In three studies, the best performances were achieved when the two subtypes of odontogenic keratocysts (solitary or syndromic) were pooled together, the case-wise analysis showing a success rate of 100% for odontogenic keratocysts and radicular cysts, in one of them. In two studies, the dentigerous cyst was associated with the majority of misclassifications, and its omission from the dataset improved significantly the classification rates. CONCLUSIONS: The overall evaluation showed all studies presented high accuracy rates of computer-aided systems in classifying odontogenic cysts in digital images of histological tissue sections. However, due to the heterogeneity of the studies, a meta-analysis evaluating the outcomes of interest was not performed and a pragmatic recommendation about their use is not possible.