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    "description": "<p>The open-source Python package pyam provides a suite of features and methods\u00a0for the analysis, validation and visualization of reference data and scenario results\u00a0generated by integrated assessment models, macro-energy tools and other frameworks\u00a0in the domain of energy transition, climate change mitigation and sustainable development.\u00a0It bridges the gap between scenario processing and visualisation solutions\u00a0that are \"hard-wired\" to specific modelling frameworks\u00a0and generic data analysis or plotting packages.</p><p> </p><p> The package aims to facilitate reproducibility and reliability\u00a0of scenario processing, validation and analysis by providing well-tested\u00a0and documented methods for working with timeseries data in the context of climate policy and energy systems.\u00a0It supports various data formats, including sub-annual resolution\u00a0using continuous time representation and \"representative timeslices\".</p><p> </p><p> The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users.</p><p> </p><p> The code base is implemented following best practices of collaborative\u00a0scientific-software development.\u00a0This manuscript describes the design principles of the package\u00a0and the types of data which can be handled.\u00a0The usefulness of pyam is illustrated by highlighting several recent applications.</p>",
    "publication_date": "2021-09-01",
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