  Exploring and quantifying semantic relatedness is central to representing
language and holds significant implications across various NLP tasks. While
earlier NLP research primarily focused on semantic similarity, often within the
English language context, we instead investigate the broader phenomenon of
semantic relatedness. In this paper, we present \textit{SemRel}, a new semantic
relatedness dataset collection annotated by native speakers across 13
languages: \textit{Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi,
Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic,
Spanish,} and \textit{Telugu}. These languages originate from five distinct
language families and are predominantly spoken in Africa and Asia -- regions
characterised by a relatively limited availability of NLP resources. Each
instance in the SemRel datasets is a sentence pair associated with a score that
represents the degree of semantic textual relatedness between the two
sentences. The scores are obtained using a comparative annotation framework. We
describe the data collection and annotation processes, challenges when building
the datasets, baseline experiments, and their impact and utility in NLP.
