The Onolytics methodology is based on a new ontology of ethnicity that combines some of the multidimensional facets encapsulated in the diversity of people’s names: language, religion, geographical region, and culture. It is a methodology developed using data collected at very fine temporal and spatial scales, and made available, subject to safeguards, at the level of the individual. The dataset is classified assigning the most probable cultural ethnic and linguistic (CEL) group of origin to each name, termed Onolytics Types. Once the user processes a database of individuals names through Onolytics software, the algorithm establishes the most probable origin of each person based on the origins of his/her first name and surname following a set of complex rules and probability rates.
There is a growing need to understand the nature and detailed composition of ethnic groups in today’s increasingly multicultural societies. Ethnicity classifications are personally sensitive and can be the subject of public controversy. People may not feel comfortable self-reporting their ethnicity and, therefore, answers are prone to interviewer or respondent bias. Poor quality and/or a lack of availability of ethnicity classifications in routine administrative records and transactional datasets has serious consequences in terms of failing to meaningfully understand the diversity within populations.
Onolytics allows you to classify your populations (i.e. users, citizens, patients, or customers) using an academically validated methodology with sufficient accuracy and at a fraction of the cost and hassle of alternative methods (such as, asking for self-assigned ethnicity in surveys or forms or attempting to re-contact people in historical records). Furthermore, once you create a baseline measurement of the diversity and equity in access to your services, you can consistently monitor progress against a quantifiable such baseline, using a neutral and defensible methodology that is not skewed by changes in data collection or the subjective judgment of the intermediaries involved.
Onolytics classifies names into groups of common cultural ethnic and linguistic (CEL) origin using first names and surnames. Onolytics classification is organized in a hierarchical pyramid with three levels of detail of ethnic groups, termed the Onolytics Taxonomy.
At the base of such hierarchy there are a total of 185 independently assigned categories termed Onolytics Types, which represent the smallest building blocks of the Onolytics Taxonomy.
Onolytics is currently available as standalone software for maximum privacy. Furthermore, an API integration is currently under development.
Onolytics is a JAVA-based software program, and requires that the JAVA runtime environment be installed on the client machine. As such, it is Operating System independent and runs in Windows, Mac and Linux. If your client machine can run JAVA, you can run Onolytics.
Onolytics is also available as a consultancy service. You provide us with the data and we deliver the results.
The science studying names is called Onomastics, hence the root of our name Onolytics. Name analysis to classify the ethnic origin of people’s names has been carried out since at least the late 19th century, starting with George Darwin, the son of Charles Darwin who used surnames as an indicator of endogamy in the English aristocracy. Later, the U.S. Census began publishing research using Spanish Surnames in the 1940s and various researchers in the U.S., U.K., Germany the Netherlands and other countries have consistently used name analysis to ascribe ethnic origin for populations coming from different parts of the world. Most of these studies only used surnames to ascribe a person’s ethnic origin, and not first names.
Onolytics went beyond this research frontier and added first names origins in combination with surnames and a score that represents how likely a name is to originate from a certain ethnic group. This combination (first name and surname) together with a set of complex rules bundled in a proprietary algorithm, makes Onolytics classification very powerful.
Over 40 academic research projects have applied Onolytics and in some cases validated its accuracy against known ethnicity (self-reported). To read more about some of these external academic applications see over 40 publications mentioning or validating Onolytics (formerly known as Onomap)
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Most of us acquired our surnames and first names from our immediate ancestors, either passed down to us over generations or chosen by our parents in ways that are by no means random. Linguistic, religious, regional, cultural and legal factors all shape the ways in which our names are chosen and transmitted over time and across space. Intriguingly, naming conventions usually adhere to unwritten social norms and customs that with time end up producing distinctive ethnic and geographic patterns in name frequency distributions over space. A sort of “name sediment” accretes over time that can be very distinctive of particular places, only altered by migration flows and inter-group marriage between different human groups.
Furthermore, these mostly exceptional events can be disentangled in contemporary name distributions and sometimes traced back to their areas of origin. Prof. Pablo Mateos published a book compiling evidence on these patterns assembled from fields as diverse as linguistics, genetics, epidemiology, economics, geography, demography, sociology, anthropology, psychology, history, genealogy, physics, and computer science. This evidence is woven together into an innovative account of how personal name frequency distributions over space and time follow a set of regularities across societies that have hitherto not been studied from a joint, social science perspective on human difference over space. These are precisely the patterns that Onolytics helps you uncover.
Please visit the ‘Contact Us’ page and we’ll be in touch with you shortly!
In the meantime, you can also test Onolytics but filling out individual names on our home page (navigate to the form on the right side) or testing a batch file of 100 names.