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Cold/Cozy Mice - Finding the needles in the haystack of biomedical literature

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Cold/Cozy Mice - Finding the needles in the haystack of biomedical literature
 
Identifier https://doi.org/10.7910/DVN/FECXDT
 
Creator Helena Deus
 
Publisher Harvard Dataverse
 
Description Problem Statement: the task facing biomedical scientists hoping to find publications that corroborate or debunk a hypothesis is akin to finding a needle in a haystack that keeps growing. Strategies that mine or summarize the scientific literature exist but have been largely focused on recovery of named entities (e.g. proteins, cells) or more sophisticated methods that make use of ontologies to recover also related terms and even, more recently, machine learning methods when there is sufficient training data.


Approach: In our approach, we have extracted annotations of units and measures (U&M) in open-access scientific literature in ScienceDirect.com, which we then used in combination with contextual information (e.g. section of the paper) and regular expressions to identify the specific entity being measured (e.g. Housing Temperature).


Results and Discussion: from a corpus of ~1.1M open access publications we found 299 relevant papers using the U&M approach combined with its surrounding contextual information. We found a clear prevalence of papers mentioning housing conditions in the range of 20-25°C, which is the approximate temperature range suggested by NIH guidelines. We also found a small increase in the number of papers describing mouse thermo-neutral housing conditions in the period after the observation that this variable has an impact in mice tumor growth (2014-2016). This dataset contains those results.
 
Subject Computer and Information Science
Medicine, Health and Life Sciences
 
Contributor Deus, Helena