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.nojekyll

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plan.html

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<li>What data are needed?</li>
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<li>Are such data available?</li>
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<li>When and how will the data be acquired?</li>
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<li>Who will be doing what</li>
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<li>Who will be doing what?</li>
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<li><p><strong>Data formats</strong>:</p>
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search.json

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"href": "plan.html#developing-your-data-management-plan-dmp",
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"title": "Data Management Plan",
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"section": "Developing your Data Management Plan (DMP)",
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"text": "Developing your Data Management Plan (DMP)\nWhen developing your data management plan, we recommend using the FAIR & CARE principles as guidance to maximize the reusability of your data by you, your collaborators, other researchers, and future-you. Your plan should ensure that detailed documentation adopting existing standards is developed during the entire duration of your project (don’t wait until the very end!!) and that this documentation is archived along with your data and code in a publicly accessible data repository will set you up for success.\n\n\n\nsource: https://www.library.ucsb.edu/sites/default/files/dls-n04-2021-fair-navy.pdf\n\n\nBelow is a set of questions that will help your team think about the data and resources you will need along your project’s data lifecycle.\n\nDescribing the research data: Provide a description of the data the group will collect or re-use, including the file types, data set size, the number of expected files or sets, content, and source of the data (creator and method of collection).\n\nWhat data are needed?\nAre such data available?\nWhen and how will the data be acquired?\nWho will be doing what\n\nData formats:\n\nAre there any standard formats in the specific research field for managing or disseminating the data sets that have been identified (e.g., XML, ASCII, CSV, .shp, .gdb, GeoTIFF)?\nWho from the group will have responsibility for ensuring that data standards are properly applied, and data are properly formatted?\n\nMetadata: Metadata is documentation that helps make data sets reusable. Think about what details someone would need in order to be able to understand and use these files. For example, perhaps a readme.txt file is necessary to explain variables, the structure of the files, etc. In addition, it is recommended to leverage metadata disciplinary standards, including ontologies and vocabularies. Here is a good resource for metadata standards in environmental sciences. When applicable, also describe other scientific products - models, scripts, and/or workflows - your group will be producing using README files and documenting your code.\nData sharing and access The data may have significant value for other researchers beyond this project, and sharing this data is part of the group’s responsibility as members of the scientific community. Specify the extent to which data can be reused, including any access limitations. List any proprietary software that might be needed to read the files. If some data is not shareable due to confidentiality, non-disclosure agreements (NDA), or disclosure risk, state such limitations and the rationale behind them.\nIntellectual property and re-use: If data were collected from the client organization, does the group have the right to redistribute it? If so, are there any restrictions on redistribution? If the group created its data files, would it assign a Creative Commons license to its data?\nData archiving and preservation: Throughout the project, the group may produce a large number of files. At the end of the project, groups must submit data produced by the project (except data protected by non-disclosure agreements) and when relevant raw data as well. Not all data needs to be saved. Here are some questions to ask yourselves:\n\nIf another researcher wanted to replicate the group’s work or re-use the group’s data, what data and documentation would be required for them to do so?\n\nWhere will the data and metadata be stored after the project is completed?\nIs there a subject-specific and/or open-access repository that is appropriate for the data?\n\n\nOne advantage to depositing your data in a data repository is that you can get a DOI that lets you easily share and cite your data. Most of the data repositories also track views, downloads, and citations for your data archive, which can be used as a metric or a proxy for research impact.\n\nWant to know more?\n\nMore extensive guidelines on developing your project data management plan using: Renata G Curty. (2023). DMP Recommendations (DCC Template). Zenodo. https://doi.org/10.5281/zenodo.7566971"
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"text": "Developing your Data Management Plan (DMP)\nWhen developing your data management plan, we recommend using the FAIR & CARE principles as guidance to maximize the reusability of your data by you, your collaborators, other researchers, and future-you. Your plan should ensure that detailed documentation adopting existing standards is developed during the entire duration of your project (don’t wait until the very end!!) and that this documentation is archived along with your data and code in a publicly accessible data repository will set you up for success.\n\n\n\nsource: https://www.library.ucsb.edu/sites/default/files/dls-n04-2021-fair-navy.pdf\n\n\nBelow is a set of questions that will help your team think about the data and resources you will need along your project’s data lifecycle.\n\nDescribing the research data: Provide a description of the data the group will collect or re-use, including the file types, data set size, the number of expected files or sets, content, and source of the data (creator and method of collection).\n\nWhat data are needed?\nAre such data available?\nWhen and how will the data be acquired?\nWho will be doing what?\n\nData formats:\n\nAre there any standard formats in the specific research field for managing or disseminating the data sets that have been identified (e.g., XML, ASCII, CSV, .shp, .gdb, GeoTIFF)?\nWho from the group will have responsibility for ensuring that data standards are properly applied, and data are properly formatted?\n\nMetadata: Metadata is documentation that helps make data sets reusable. Think about what details someone would need in order to be able to understand and use these files. For example, perhaps a readme.txt file is necessary to explain variables, the structure of the files, etc. In addition, it is recommended to leverage metadata disciplinary standards, including ontologies and vocabularies. Here is a good resource for metadata standards in environmental sciences. When applicable, also describe other scientific products - models, scripts, and/or workflows - your group will be producing using README files and documenting your code.\nData sharing and access The data may have significant value for other researchers beyond this project, and sharing this data is part of the group’s responsibility as members of the scientific community. Specify the extent to which data can be reused, including any access limitations. List any proprietary software that might be needed to read the files. If some data is not shareable due to confidentiality, non-disclosure agreements (NDA), or disclosure risk, state such limitations and the rationale behind them.\nIntellectual property and re-use: If data were collected from the client organization, does the group have the right to redistribute it? If so, are there any restrictions on redistribution? If the group created its data files, would it assign a Creative Commons license to its data?\nData archiving and preservation: Throughout the project, the group may produce a large number of files. At the end of the project, groups must submit data produced by the project (except data protected by non-disclosure agreements) and when relevant raw data as well. Not all data needs to be saved. Here are some questions to ask yourselves:\n\nIf another researcher wanted to replicate the group’s work or re-use the group’s data, what data and documentation would be required for them to do so?\n\nWhere will the data and metadata be stored after the project is completed?\nIs there a subject-specific and/or open-access repository that is appropriate for the data?\n\n\nOne advantage to depositing your data in a data repository is that you can get a DOI that lets you easily share and cite your data. Most of the data repositories also track views, downloads, and citations for your data archive, which can be used as a metric or a proxy for research impact.\n\nWant to know more?\n\nMore extensive guidelines on developing your project data management plan using: Renata G Curty. (2023). DMP Recommendations (DCC Template). Zenodo. https://doi.org/10.5281/zenodo.7566971"
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sitemap.xml

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