Semantic change detection (i.e., identify- ing words whose meaning has changed over time) started emerging as a grow- ing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social sci- ence. However, several obstacles make progress in the domain slow and diffi- cult. These pertain primarily to the lack of well-established gold standard datasets, resources to study the problem at a fine- grained temporal resolution, and quantita- tive evaluation approaches. In this work, we aim to mitigate these issues by (a) re- leasing a new labelled dataset of more than 47K word vectors trained on the UK Web Archive over a short time-frame (2000- 2013); (b) proposing a variant of Pro- crustes alignment to detect words that have undergone semantic shift; and (c) intro- ducing a rank-based approach for evalu- ation purposes. Through extensive nu- merical experiments and validation, we il- lustrate the effectiveness of our approach against competitive baselines. Finally, we also make our resources publicly available to further enable research in the domain.