lasp · vmartinez-cu · Oct 27, 2025 · Oct 9, 2025 · Oct 9, 2025 · Oct 9, 2025
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+# Managing Data: Storage, Format, Access
+
+This guide covers storage options, data format choices, and access protocols for datasets to ensure that datasets are
+managed in ways that meet open science principles and institutional requirements. It supports Data Systems workflows by
+improving data accessibility, promoting reproducibility, and enhancing usability and compliance with funder and
+publisher requirements.
+
+## Storage
+
+Here we assume that a dataset must be publicly accessible: machine readable on a public network in a manageable format.
+Storage on local disks or external hard drives is not considered open and accessible.
+
+Storage choice depends on many factors, including dataset size, access needs, performance needs, need for
+authentication, other data management needs (e.g., the need for a DOI), etc.
+
+**LASP resources**
+
+- LISIRD, for solar irradiance and related data products
+- LASP options: (e.g. dsapps, lasp-store)
+- LEMR for metadata
+
+**CU Boulder resources**
+
+- CU Scholar is suitable for small datasets that provides a DOI, a generically-styled landing page, and some storage.
+- CU Boulder PetaLibrary for information about CU Research Computings PetaLibrary storage resources.
+
+**External repositories**
+
+- Dryad: a curated resource that makes research data discoverable, freely reusable, and citable.
+- Figshare: a web-based interface designed for academic research data management and research data dissemination.
+  It accepts all file types (with in-browser viewing).
+- Simple storage in the cloud: AWS Glacier, S3
+- Fedora, Zenodo, Open Science Framework, Dataverse
+
+## Data Formats
+
+To maximize the potential application of tools to data, data should be provided in common, open, self-describing,
+machine readable formats such as:
+
+- netCDF/HDF
+- CDF
+- ASCII
+- FITS
+- JSON
+
+What is considered 'common' may vary somewhat by science domain. The use of proprietary data formats, such as IDL
+".sav" files, is discouraged because of the need for a license in order to use the data.
+
+"Self-describing" means that metadata about the data is included in the file or package. Self-describing formats
+include: NetCDF, HDF, and CSV with headers. ASCII or CSV data without header information, or binary data, are not
+self-describing.
+
+## Machine Readability
+
+Any dataset can be machine readable if one writes one-off code to do so.   However, datasets should be interoperable,
+which implies not having to create nor rely on specialized code.
+
+Machine readability isn't a binary condition of a dataset; it's a continuum. The issue is whether one can use a dataset
+in an existing tool that conforms to current standards. Use of a common data format or metadata schema is the first
+step in achieving machine readability.
+
+There are ways to structure data that foster machine readability, and ways that do not.
+
+To improve machine readability:
+
+- Use consistent naming conventions and delimiters
+- Represent missing values clearly
+- Include metadata in self-describing formats
+
+### Examples of what to do and what not to do with your data
+
+![Machine_readable_example_1](../_static/machine-readable-1.png)
+![Machine_readable_example_2](../_static/machine-readable-2.png)
+![Machine_readable_example_3](../_static/machine-readable-3.png)
+![Machine_readable_example_4](../_static/machine-readable-4.png)
+![Machine_readable_example_5](../_static/machine-readable-5.png)
+![Machine_readable_example_6](../_static/machine-readable-6.png)
+![Machine_readable_example_7](../_static/machine-readable-7.png)
+![Machine_readable_example_8](../_static/machine-readable-8.png)
+![Machine_readable_example_9](../_static/machine-readable-9.png)
+![Machine_readable_example_10](../_static/machine-readable-10.png)
+![Machine_readable_example_11](../_static/machine-readable-11.png)
+![Machine_readable_example_12](../_static/machine-readable-12.png)
+![Machine_readable_example_13](../_static/machine-readable-13.png)
+![Machine_readable_example_14](../_static/machine-readable-14.png)
+![Machine_readable_example_15](../_static/machine-readable-15.png)
+![Machine_readable_example_17](../_static/machine-readable-17.png)
+![Machine_readable_example_18](../_static/machine-readable-18.png)
+![Machine_readable_example_20](../_static/machine-readable-20.png)
+![Machine_readable_example_21](../_static/machine-readable-21.png)
+![Machine_readable_example_22](../_static/machine-readable-22.png)
+
+## Useful Links
+
+- [Data Stewardship at LASP](data_stewardship.md)
+- [FAIR Principles](fair_principles.md)
+- [netCDF](file_formats/netcdf.md)
+- [LISIRD](https://lasp.colorado.edu/lisird/)
+- [Zenodo](https://zenodo.org/)
+- [CU Scholar](https://scholar.colorado.edu/about)
+- [CU PetaLibrary](https://www.colorado.edu/rc/resources/petalibrary)
+- [Dryad](https://datadryad.org/)
+- [Figshare](https://figshare.com/)
+- [AWS Glacier](https://aws.amazon.com/glacier/)
+- [AWS S3](https://aws.amazon.com/s3/)
+- [Fedora](https://duraspace.org/fedora/)
+- [Open Science Framework](https://osf.io/)
+- [Dataverse](https://dataverse.org/)
+
+## Acronyms
+
+List of acronyms used in the guideline
+
+- **AWS** = Amazon Web Services
+- **CDF** = Common Data Format
+- **CSV** = Comma-Separated Values
+- **DOI** = Digital Object Identifier
+- **FITS** = Flexible Image Transport System
+- **HDF** = Hierarchical Data Format
+- **IDL** = Interactive Data Language
+- **LEMR** = LASP Environmental Metadata Repository
+- **LISIRD** = LASP Interactive Solar Irradiance Data Center
+- **MOU** = Memorandum of Understanding
+- **NetCDF** = Network Common Data Form
+
+Credit: Content taken from a Confluence guide written by Anne Wilson and Shawn Polson
diff --git a/pyproject.toml b/pyproject.toml
@@ -36,4 +36,4 @@ homepage = "https://github.com/lasp/"
 repository = "https://github.com/lasp/developer-guide"
 
 [tool.codespell]
-ignore-words-list = "nd, SORCE"
+ignore-words-list = "nd, SORCE, sav"