Research scientist needs intelligent search through nested citations in papers


"I work in post-secondary higher education (university). I, like many, wear multiple hats. First, I am a research scientist. I travel the world coring lake samples and looking at algae fossils to better understand climatic variation. Secondly, I am an educator. I have taught students at universities in the US, Holland, Lithuania, and S. Korea. Third, I am Director of Institutional Research (student statistics). Fourth, I am a faculty adviser to undergraduate and graduate students. 

One of the biggest problems facing my primary role as a research scientist is that information is often obscured by nested citations. That is, to get to the original methodology of a particular technique, one must spend tens to hundreds of man-hours simply going back through papers to figure out a small nuance in methodology that was never cited following that initial paper (e.g., XYZ et al publishes a paper, its methodology is cited, which is then cited without giving reference to the initial XYZ et al publication, repeat ad infinitum). If there was a software that could drill down to initial creators it would be an incredible time and resource saver. 

The only method I could see is to create an AI search engine that could understand what it is I am looking for given a certain set of criteria. Current methods using tags do not work well and are often more time consuming than are helpful. 

I make around $35/hr (salary, but broken down)... I would reasonably pay $1500-1700 for this software (more so if it could also manage my citation library)"

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8 months ago

I'm not sure what merit the AI aspect would be, but the idea of obtaining all nested citations (to a certain distance) and then searching within them is certainly doable. I think it would require the assumption that your original methodology lies somewhere in the resulting network, rather than sourcing non-cited references.

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over 2 years ago

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