9.3.1. Multi-level Classification: Topics, Branches and Majors

The knowledge organization is based on a multi-level semantic structure labeled as Majors, Branches and Topics. The linear hierarchical relationship between them defines the main objects and creates the macro-logical of the dataset structure.

The interdependence between the objects is assigned based on the relationship tables parameters.

9.3.2. Scientists: Name and Alternative Name

Based on the bibliography analysis, the data model considers the use of two fields for the scientists’ names. The use of the alternative name is optional and it can be more detected on scientists from Middle Western countries.

 9.3.3. Scientists: Birth, Death and Flourish Dates

The proposed temporality approach addressed the specific issues about the indexation of historical data instead of the usual standards for temporal modeling that consider birth and death dates.

The first issue was that the date structure adopted is composed of three data parameters: birth date, death date and flourished date (usually represented on bibliography by the use of fl with the date).

The second aspect explored was the use of attributes based on boolean parameters that define if one or more of the dates are historiographically not accurate. Thus, the date fields date_birth_est, date_death_est and date_fl_est may be enhanced with the use of the temporal attributes regarding its accuracy.

9.3.4. Citizenship

The citizenship attribute had been categorized into three types: Born, Naturalized and Contextualized. The contextual citizenship refers to the geographical contextualization for countries that have been merged and/ or changed in relation to nowadays.

For example, Persian scientists may be indexed as Persian born citizenship as well as Iranian contextual citizenship. The data model allows the coexistence of both attributes. Another example refers to the world-famous physicist Albert Einstein, who was born in Germany in 1879. Two decades later, in 1901 he obtained the Swiss citizenship thru Naturalization. Then, in 1940, Einstein also obtained North American citizenship thru Naturalization.

This approach was selected by many aspects, mostly because of the need to make the dataset available to an efficient plug-and-play integration with map API’s and other data tools. These proposed promises certainly will be discussed and validated by scholars, and contributions for this discussion will be very welcome.

It was considered initially including these fields into the user table (user), assigning specific fields for each kind of citizenship (born, naturalized and contextualized). However, after the first analysis, the data model with a separated relationship table has been demonstrated more effective for the knowledge representation on the dataset.

9.3.5. Titles

The titles are stored in two different fields on the data model: “specific title” and “generic title”. For example, a scientist that hold the specific title defined as “nuclear physicist” will also hold the generic title as “physicist”.

The use of these structured and linked fields – based on primary key ID instead of the use of syntax “anyone who has physicist” on the title – intends to create a more robust data model allowing a more sophisticated use of the data.

9.3.6. Multi-language and Multi-major Ready

The first delivery of the research project has been focused on the History of Physics, History of Chemistry and History of Mathematics. However, the data model is fully ready to operate in multi-language and multi-major.

Then, other fields of study may be incorporated in the future based on the same structure, as well it may be expanded with other historical contexts and periods.