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        <title>A Python-based tool for constructing observables from the DSN's
            closed-loop archival tracking data files - Bundle</title>
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            <author_list>Verma, A. K.</author_list>
            <publication_year>2022</publication_year>
            <doi>10.1016/j.softx.2022.101190</doi>
            <keyword>Radio science</keyword>
            <keyword>ATDF</keyword>
            <keyword>Closed-loop</keyword>
            <keyword>DSN</keyword>
            <keyword>Python</keyword>
            <description>This bundle contains the code presented in Verma, A. K., A Python-based tool for constructing observables from the DSN’s
                closed-loop archival tracking data files, SoftwareX, Volume 19, 2022, 101190, ISSN 2352-7110,
                https://doi.org/10.1016/j.softx.2022.101190
                
                Radio science data collected from NASA’s Deep Space Networks (DSNs) are made available 
                in various formats through NASA's Planetary Data System (PDS). The majority of these data are 
                packed in complex formats, making them inaccessible to users without specialized knowledge. This bundle includes the Python-based tool
                presented in the paper that can preprocess the closed-loop archival tracking 
                data files (ATDFs), produce Doppler and range observables, and write them in an ASCII table along 
                with ancillary information.
                
                A copy of the paper is also included in this bundle.
                
                This research was funded by NASA Cooperative Agreement Notice (CAN) Award 80NSSC22M0023.
            </description>
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