Add data stewardship guide

docs/source/data_management/data_stewardship.md

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+# Data Stewardship at LASP
+
+LASP has a hard-earned, world-class reputation for creating valuable, high-quality
+datasets. These datasets, often paid for by U.S. taxpayers, are our legacy.
+
+## Purpose
+
+The world of science is rapidly evolving, particularly in how research data is managed
+and how science is conducted. Expectations from government agencies, OSTP, and publishers
+around open and accessible research data have significant implications for:
+
+- Scientific conduct and integrity
+- Funding for science
+- Scientific publication
+- Attribution and rewards
+
+By following best practices in data stewardship, LASP ensures its datasets remain
+accessible, reproducible, and valuable for the scientific community.
+
+## Key principles
+
+Several key principles guide data stewardship and open science at LASP:
+
+### Open Data
+
+Funding agencies now expect data—and, in some cases, the software that created it—to
+be made available beyond the project that produced it. Agencies such as USGS, NASA,
+NOAA, and NSF require data management plans in proposals, and funded projects must
+meet these responsibilities. This generally includes:
+
+- Making data and metadata publicly accessible.
+- Ensuring machine-readability for automated tools.
+
+Organizations such as ESIP, RDA, ESDSWG, and CODATA work to enhance data management and
+sharing nationally and internationally.
+
+### Enabling a Science Ecosystem
+
+Scientific artifacts should be [preserved as a lasting world heritage](https://www.agu.org/share-and-advocate/share/policymakers/position-statements/position_data).
+As scientific data grows in complexity and scale, interoperable tools, standards, and conventions play a crucial role
+in simplifying and automating data processing, analysis, and metadata collection. Data that is
+[FAIR](fair_principles.md)—findable, accessible, interoperable, and reusable—is essential to supporting these efforts.
+
+### Reproducibility
+
+A key issue in modern science is the [reproducibility crisis](https://en.wikipedia.org/wiki/Replication_crisis),
+where peer reviewers find it difficult to replicate results from many publications. While complete reproducibility
+may not always be feasible, best practices in data management ensure:
+
+- Experiments can be rerun, validated, or verified.
+- Data and documentation are identifiable and machine-readable.
+- Automated tools can process and analyze data.
+- Workflow and notebook tools enable shareable, reproducible workflows.
+
+For more on improving reproducibility in Earth science research, see: [EOS article](https://eos.org/opinions/improving-reproducibility-in-earth-science-research).
+
+### Return on Investment
+
+Good data management maximizes the impact of a dataset, allowing for wider use now and in the future. This
+benefits both science and society by increasing the return on investment in data collection and analysis.
+
+### Scientific Publication
+
+Publishing is evolving, with journals increasingly requiring:
+
+- Data contributions alongside publications.
+- Linking publications with datasets and executable code.
+
+Organizations like [FORCE11](https://www.force11.org/about) advocate for semantically enhanced, media-rich
+digital publishing, which is more powerful than traditional print media.
+
+### Attribution for Datasets and Science Software
+
+Funding agencies and publishers are promoting cultural shifts in how datasets and software are credited. High-quality
+datasets and software are now recognized as independent scientific contributions.
+
+Since datasets are often generated by software, proper software management is a critical component of data stewardship.
+The [Software Sustainability Institute](https://www.software.ac.uk/) supports sustainable research software.
+
+## How to apply these principles
+
+To meet the expectations outlined above:
+
+1. **Develop a Data Management Plan (DMP)**
+  - Address how data will be stored, accessed, and shared.
+  - Align with agency requirements (e.g., NASA, NSF).
+
+2. **Ensure Open Access**
+  - Make datasets and metadata publicly available.
+  - Use machine-readable formats when possible.
+
+3. **Use Standardized Metadata and Formats**
+  - Follow best practices for documentation and accessibility.
+  - Use tools that support automated metadata generation.
+
+4. **Maintain Provenance and Reproducibility**
+  - Record data lineage, processing steps, and software versions.
+  - Use reproducible workflows and notebook tools.
+
+5. **Publish Data Alongside Research**
+  - Link datasets with publications.
+  - Provide clear attribution for datasets and software.
+
+6. **Engage with Data Stewardship Communities**
+  - Participate in organizations like ESIP, RDA, and CODATA.
+  - Follow emerging best practices in open science.
+
+### Assessing data management maturity
+
+To assess data stewardship maturity, consider the following rubrics:
+
+![Data Management Assessment](../_static/data_management_assessment.png)
+![Metadata Assessment](../_static/metadata_assessment.png)
+
+Efforts to define levels of data stewardship maturity have produced useful rubrics that have been shown to help
+repositories and projects evaluate and improve their data stewardship efforts.  In particular, and similar to the
+CMMI that was developed for process improvement, a maturity matrix for data stewardship was developed.  A high-level
+view of that matrix is presented here.  Note that Level 3 was determined to be the recommended level of attainment
+for operational digital products stewarded by national data centers.
+
+![Data Stewardship Maturity Matrix](../_static/data_stewardship_maturity_matrix.png)
+
+## Useful Links
+
+- [FAIR Principles](fair_principles.md)
+- [AGU Position Statement on Data](https://www.agu.org/share-and-advocate/share/policymakers/position-statements/position_data)
+- [Improving Reproducibility in Earth Science Research](https://eos.org/opinions/improving-reproducibility-in-earth-science-research)
+- [FORCE11 on Digital Publishing](https://www.force11.org/about)
+- [Software Sustainability Institute](https://www.software.ac.uk/)
+
+## Acronyms
+
+- **AGU** = American Geophysical Union
+- **CODATA** = Committee on Data for Science and Technology
+- **DMP** = Data Management Plan
+- **ESDSWG** = Earth Science Data Systems Working Group
+- **ESIP** = Earth Science Information Partners
+- **FAIR** = Findable, Accessible, Interoperable, and Reusable
+- **FORCE11** = Future of Research Communication and e-Scholarship
+- **NASA** = National Aeronautics and Space Administration
+- **NOAA** = National Oceanic and Atmospheric Administration
+- **NSF** = National Science Foundation
+- **OSTP** = Office of Science and Technology Policy
+- **RDA** = Research Data Alliance
+- **USGS** = United States Geological Survey
+
+Credit: Content adapted from a Confluence guide written by Anne Wilson and Shawn Polson.

docs/source/data_management/index.rst

@@ -7,4 +7,5 @@ Data Management
 
    file_formats/index
    metadata.md
-   fair_principles.md
+   fair_principles.md
+   data_stewardship.md