Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). Fig 1 illustrates the relationship beten hypothetical research and data life cycles and highlights the links to the rules presented in this paper. The DMP undergoes peer review and is used in part to evaluate a project’s merit. Plans also document the data management activities associated with funded projects and may be revisited during performance reviews.#R##N##R##N##R##N##R##N#Fig 1#R##N##R##N#Relationship of the research life cycle (A) to the data life cycle (B); note: highlighted circles refer to the rules that are most closely linked to the steps of the data life cycle.#R##N##R##N##R##N##R##N#Earlier articles in the Ten Simple Rules series of PLOS Computational Biology provided guidance on getting grants [1], writing research papers [2], presenting research findings [3], and caring for scientific data [4]. Here, I present ten simple rules that can help guide the process of creating an effective plan for managing research data—the basis for the project’s findings, research papers, and data products. I focus on the principles and practices that will result in a DMP that can be easily understood by others and put to use by your research team. Moreover, following the ten simple rules will help ensure that your data are safe and sharable and that your project maximizes the funder’s return on investment.
['William K. Michener']
I am writing a research paper on a idea Ten Simple Rules for Creating a Good Data Management Plan
[{"from": "human", "value": "I am writing a research paper on a idea Ten Simple Rules for Creating a Good Data Management Plan"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle\u2014from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). Fig 1 illustrates the relationship beten hypothetical research and data life cycles and highlights the links to the rules presented in this paper. The DMP undergoes peer review and is used in part to evaluate a project\u2019s merit. Plans also document the data management activities associated with funded projects and may be revisited during performance reviews.#R##N##R##N##R##N##R##N#Fig 1#R##N##R##N#Relationship of the research life cycle (A) to the data life cycle (B); note: highlighted circles refer to the rules that are most closely linked to the steps of the data life cycle.#R##N##R##N##R##N##R##N#Earlier articles in the Ten Simple Rules series of PLOS Computational Biology provided guidance on getting grants [1], writing research papers [2], presenting research findings [3], and caring for scientific data [4]. Here, I present ten simple rules that can help guide the process of creating an effective plan for managing research data\u2014the basis for the project\u2019s findings, research papers, and data products. I focus on the principles and practices that will result in a DMP that can be easily understood by others and put to use by your research team. Moreover, following the ten simple rules will help ensure that your data are safe and sharable and that your project maximizes the funder\u2019s return on investment."}]