Estimating word complexity with binary or continuous scores is a challenging task that has been studied for several domains and natural languages. Commonly this task is referred to as Complex Word Identification (CWI) or Lexical Complexity Prediction (LCP). Correct evaluation of word complexity can be an important step in many Lexical Simplification pipelines. Earlier works have usually presented methodologies of lexical complexity estimation with several restrictions: hand-crafted features correlated with word complexity, performed feature engineering to describe target words with features such as number of hypernyms, count of consonants, Named Entity tag, and evaluations with carefully selected target audiences. Modern works investigated the use of transforner-based models that afford extracting features from surrounding context as well. However, the majority of papers have been devoted to pipelines for the English language and few translated them to other languages such as German, French, and Spanish. In this paper we present a dataset of lexical complexity in context based on the Russian Synodal Bible collected using a crowdsourcing platform. We describe a methodology for collecting the data using a 5-point Likert scale for annotation, present descriptive statistics and compare results with analogous work for the English language. We evaluate a linear regression model as a baseline for predicting word complexity on handcrafted features, fastText and ELMo embeddings of target words. The result is a corpus consisting of 931 distinct words that used in 3,364 different contexts.