Convert text into synthesized speech and get a list of supported voices for a region. 
    Microsoft's 'Cognitive Services Text to Speech REST' API <https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/rest-text-to-speech?tabs=streaming>  
    supports neural text to speech voices, which support specific languages and dialects that are identified by locale.

Provides data science tools for conservation science, including
    methods for environmental data analysis, humidity calculations,
    sustainability metrics, engineering calculations, and data
    visualisation. Supports conservators, scientists, and engineers
    working with cultural heritage preventive conservation data.
    The package is motivated by the framework outlined 
    in Cosaert and Beltran et al. (2022) 
    "Tools for the Analysis of Collection Environments" 
    <https://www.getty.edu/conservation/publications_resources/pdf_publications/tools_for_the_analysis_of_collection_environments.html>.

Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.

Merging data from multiple sources is a relevant approach for
    comprehensively evaluating complex systems. However, the inherent problems
    encountered when analyzing single tables are amplified with the generation
    of multi-block datasets, and finding the relationships between data layers
    of increasing complexity constitutes a challenging task. For that purpose,
    a generic methodology is proposed by combining the strength of established
    data analysis strategies, i.e. multi-block approaches and the Orthogonal
    Partial Least Squares (OPLS) framework to provide an efficient tool for the
    fusion of data obtained from multiple sources. The package enables quick
    and efficient implementation of the consensus OPLS model for any horizontal
    multi-block data structures (observation-based matching). Moreover, it
    offers an interesting range of metrics and graphics to help to determine
    the optimal number of components and check the validity of the model
    through permutation tests. Interpretation tools include score and loading
    plots, Variable Importance in Projection (VIP), functionality predict for
    SHAP computing, and performance coefficients such as R2, Q2, and DQ2
    coefficients.
    J. Boccard and D.N. Rutledge (2013) <doi:10.1016/j.aca.2013.01.022>.