Finding Rising Stars (RS) can be very challenging since their definition may vary among organizations and topics of research. Rising Stars present characteristics that are similar to Opinion Leaders but the main differentiator is that RSs are still building up their relevance and walking the journey to become the next Thought Leaders (read more about how to find Thought Leaders here).

The case:
"We need to map Rising Stars in US, that are neurologists with experience in trials, working with Biomarkers in Alzheimer, that might be open to collaborate with industry"

To find these experts, it is important to use terms that will bring up in the search exactly the experts that are relevant to the topic to be explored. In this use case, two keywords can be used as search terms as described below: 

As covered in the Search and Construct Search Strings article, when a search is performed, the logic will be by default "Alzheimer AND Biomarkers", meaning that the experts must have been working with both topics used in the search to show up in the list of results (learn more here if you would like to use a different logic for the search). 

There are important characteristics to be taken into consideration when the objective is to select RSs from a pool of experts related to a topic. By using the filters available in Monocl Professional, several characteristics of the experts can be considered, such as level of seniority, experience in collaboration with companies, when they started their careers, number of publications or impact factor of the publications and more. For this use case, the following filters can be used:

  1. Location
    By using "United States" in the location filter, one will be selecting only experts based in the US.

  2. Career Start
    By choosing experts who started their career in 2000 (meaning the first paper available for a specific expert, disregarding the topic, is dated 2000 or later), one will exclude from the results all the experts that have been working on this topic for too many years and might be at the end of their career.

  3. Author Characteristics
    The objective is to use this filter to address a certain level of seniority, based on scientific productivity over time. To target rising stars, one can use total number of publications = 40-180, to consider prolific researchers, still not too established. Expert selection can also be driven by how relevant to the research topic they are by setting the filter published at least once in journal with ranking = 8+ as a way to select experts publishing in good journals.

  4. Investigator Characteristics
    The option "any trial involvement" will allow the search engine to select all the experts with experience in clinical studies in any way, including PI experience.

  5. Industry PaymentsSetting a minimum of 20 000 US dollars for total industry payments received will include in the list only the experts that have already collaborated with companies and might be open for more collaborations

  6. Specialty
    Use this filter to select a specific pool of physicians. In this case, by typing "neurology" and choosing the option "Psychiatry & Neurology - Neurology" can be one way to select the right physicians for this case.

You can see how this search would look like in Monocl Professional in the screenshots below. Note that the filters used and their specification are indicated in the green rectangles in the top of the list. Also, to help rank the experts that are relevant, it is possible to use the sorting option on top of the list. In this case, experts are ranked by "Relevance" and "active in the last 2 years" in the topic indicated by the keywords.

The screenshot above shows now a list of 18 relevant experts to be considered for this use case. You can quickly check this information by looking at the numbers disclosed under each name, but also exploring the charts for each expert on the right side. After selecting the right experts, it is possible to save the data in a project (see tutorial here) or export the list as an Excel, PowerPoint or CSV files (see tutorial here).

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