endovascular stenting emerging as first-line therapy for rapid symptom relief, while definitive treatment targets the underlying cause

De nuevo, un sesgo de género, en el material publicado/seleccionado.

Interesante experimento de humanidades digitales en pequeño.

Y sin embargo, este bonito propósito, es ortogonal al tamaño de las bases de datos. Otras hermenéuticas computacionales podrían ocurrir y de hecho ocurren, en lo pequeño.

Jonathan Robinson on Twitter. (n.d.). Twitter. Retrieved 6 September 2021, from https://twitter.com/jon_m_rob/status/1431734411335176199

Bartik, A. W., Cullen, Z. B., Glaeser, E. L., Luca, M., Stanton, C. T., & Sunderam, A. (2020). The Targeting and Impact of Paycheck Protection Program Loans to Small Businesses (Working Paper No. 27623; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27623

Granja, J., Makridis, C., Yannelis, C., & Zwick, E. (2020). Did the Paycheck Protection Program Hit the Target? (Working Paper No. 27095; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27095

What would be the links between these people? If you asked someone to list their friends, you will get lists which produce a star network. There needs to be a second round of questions involving friends of friends. Getting network data requires asking interrelated people.

p. 100

Data are not useful in and of themselves. They only have utility if meaning and value can be extracted from them. In other words, it is what is done with data that is important, not simply that they are generated. The whole of science is based on realising meaning and value from data. Making sense of scaled small data and big data poses new challenges. In the case of scaled small data, the challenge is linking together varied datasets to gain new insights and opening up the data to new analytical approaches being used in big data. With respect to big data, the challenge is coping with its abundance and exhaustivity (including sizeable amounts of data with low utility and value), timeliness and dynamism, messiness and uncertainty, high relationality, semi-structured or unstructured nature, and the fact that much of big data is generated with no specific question in mind or is a by-product of another activity. Indeed, until recently, data analysis techniques have primarily been designed to extract insights from scarce, static, clean and poorly relational datasets, scientifically sampled and adhering to strict assumptions (such as independence, stationarity, and normality), and generated and alanysed with a specific question in mind.

Good discussion of the different approaches allowed/required by small v. big data.