By querying open data of notorious scientific databases via representational state transfers, and subsequently enforcing data management practices with a dynamic topic modeling approach on the referred metadata available, this work achieves a feasible form of article set analysis and classification. Research trends for a given field in specific moments are identified, and also the referred trends evolution throughout the years.

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This dataset is compiled from five years of observation from the Global Precipitation Measurement (GPM) core observatory Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). Retrieved emissivites and surface backscatter cross sections are gridded at quarter-degree, monthly resolution separately for non-snow-covered land, snow-covered land, and sea ice.

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These data are stored as numpy (.npy) files. Sample reading and plotting code is provided by the Jupyter notebook.

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Motor point identification is pivotal to elicit comfortable and sustained muscle contraction through functional electrical stimulation. To this purpose, anatomical charts and manual search techniques are used to extract subject-specific stimulation profile. Such information being heterogenous they lack standardization and reproducibility. To address these limitations; we aim to identify, localize, and characterize the motor points of forearm muscles across nine healthy subjects.

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