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1. Measuring Public Open-Source Software in the Federal Government: An Analysis of Code.gov

   https://doi.org/10.6339/24-JDS1148  

   Shrivastava, Rahul; Korkmaz, Gizem (January 2024, Journal of Data Science)

   This paper presents an in-depth analysis of patterns and trends in the open-source software (OSS) contributions by the U.S. federal government agencies. OSS is a unique category of computer software notable for its publicly accessible source code and the rights it provides for modification and distribution for any purpose. Prompted by the Federal Source Code Policy (USCIO, 2016), Code.gov was established as a platform to facilitate the sharing of custom-developed software across various federal government agencies. This study leverages data from Code.gov, which catalogs OSS projects developed and shared by government agencies, and enhances this data with detailed development and contributor information from GitHub. By adopting a cost estimation methodology that is consistent with the U.S. national accounting framework for software investment proposed in Korkmaz et al. (2024), this research provides annual estimates of investment in OSS by government agencies for the 2009–2021 period. The findings indicate a significant investment by the federal government in OSS, with the 2021 investment estimated at around $407 million. This study not only sheds light on the government’s role in fostering OSS development but also offers a valuable framework for assessing the scope and value of OSS initiatives within the public sector. 

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2. From GitHub to GDP: A framework for measuring open source software innovation

   https://doi.org/10.1016/j.respol.2024.104954  

   Korkmaz, Gizem; Santiago_Calderón, J Bayoán; Kramer, Brandon L; Guci, Ledia; Robbins, Carol A (April 2024, Research Policy)

   Open source software (OSS) is software that anyone can review, modify, and distribute freely, usually with only minor restrictions such as giving credit to the creator of the work. The use of OSS is growing rapidly, due to its value in increasing firm and economy-wide productivity. Despite its widespread use, there is no standardized methodology for measuring the scope and impact of this fundamental intangible asset. This study presents a framework to measure the value of OSS using data collected from GitHub, the largest platform in the world with over 100 million developers. The data include over 7.6 million repositories where software is developed, stored, and managed. We collect information about contributors and development activity such as code changes and license detail. By adopting a cost estimation model from software engineering, we develop a methodology to generate estimates of investment in OSS that are consistent with the U.S. national accounting methods used for measuring software investment. We generate annual estimates of current and inflation-adjusted investment as well as the net stock of OSS for the 2009–2019 period. Our estimates show that the U.S. investment in 2019 was $37.8 billion with a current-cost net stock of $74.3 billion. 

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3. MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing

   https://doi.org/10.3390/metabo13070842  

   Thompson, P Travis; Moseley, Hunter_N B (July 2023, Metabolites)

   In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems. MESSES helps transform tabular data from multiple sources into a Metabolomics Workbench specific deposition format. The package provides three commands, extract, validate, and convert, that implement a natural data transformation workflow. Moreover, MESSES facilitates richer metadata capture than is typically attempted by manual efforts. The source code and extensive documentation is hosted on GitHub and is also available on the Python Package Index for easy installation. 

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4. Perceived inclusivity and trust in protected area management decisions among stakeholders in Alaska

   https://doi.org/10.1002/pan3.10312  

   Goodson, Devin J.; Riper, Carena J.; Andrade, Riley; Cebrián‐Piqueras, Miguel A.; Hauber, Mark E. (April 2022, People and Nature)

   1. The success of conservation initiatives often depends on the inclusion of diverse stakeholder interests in the decision-making process. Yet, there is a paucity of empirical knowledge concerning the factors that explain why stakeholders do—or do not—believe that they are meaningfully represented by government agencies. 2. Our study provides insight into the relationship between trust and stakeholder perceptions of inclusivity in public land management decisions. Here, we focus on the U.S. state of Alaska, where almost two-thirds of the land area are managed by the federal government. 3. We used structural equation modelling to test whether an individual's trust and the information sources used to learn about land management positively influenced perceived inclusivity. We conceptualized trust in terms of four dimensions that reflected an individual's disposition to trust, trust in the federal government, trust in shared values and trust that agencies adhere to a moral code. 4. We found that survey respondents across the U.S. state of Alaska had a limited disposition to trust others, did not trust federal land management agencies, did not believe agencies shared their values pertaining to protected area management and did not believe that agencies adhered to a moral code. 5. Beliefs about the morality of agencies were the primary driver of perceived inclusivity in land management decisions, indicating that agencies should focus on solving problems through deliberation and discussion about moral principles rather than by force. 6. Information acquired from professional, community-based or environmental advocacy exchanges also positively influenced perceived levels of involvement among stakeholders in resource management decisions. 7. These results provide a roadmap for how land management agencies can improve public relations and work towards a model of inclusive conservation around protected areas. 

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5. Academic Tracker: Software for tracking and reporting publications associated with authors and grants

   https://doi.org/10.1371/journal.pone.0277834  

   Thompson, P. Travis; Powell, Christian D.; Moseley, Hunter N. (November 2022, PLOS ONE)

   Zhang, Yuji (Ed.)

   In recent years, United States federal funding agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF), have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. Enforcement is primarily through annual and final reports submitted to these funding agencies, where all peer-reviewed publications must be registered through the appropriate mechanism as required by the specific federal funding agency. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and even funding delays for current and new research grants. So, it’s important to make sure every peer-reviewed publication is reported properly and in a timely manner. For large collaborative research efforts, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle. In order to help with this reporting burden, we have developed the Academic Tracker software package, implemented in the Python 3 programming language and supporting Linux, Windows, and Mac operating systems. Academic Tracker helps with publication tracking and reporting by comprehensively searching major peer-reviewed publication tracking web portals, including PubMed, Crossref, ORCID, and Google Scholar, given a list of authors. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes. The source code and extensive documentation is hosted on GitHub ( https://moseleybioinformaticslab.github.io/academic_tracker/ ) and is also available on the Python Package Index ( https://pypi.org/project/academic_tracker ) for easy installation. 

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