Datasets:

Otter21
/

ARGO_Profiles

@@ -4,17 +4,18 @@ language:
 - en
 tags:
 - climate
 ---
 Argovis Argo Ocean Profiles
-## Dataset summary
 This dataset contains ocean profile data collected by the international Argo float program and accessed via the Argovis API.  Each record corresponds to a single profile measured by an autonomous drifting float, including time, location, basin, and associated profile metadata fields that can be joined to the underlying temperature and salinity data structures.  The goal of this dataset is to provide a ready-to-use subset of Argo profiles for machine learning, geospatial analysis, and educational use.[1][2][3][4]
 The files were programmatically fetched using the Argovis API in Python, converted to pandas DataFrames, and exported as CSV.  This makes it easy to load the data in common data science environments (Python, R, Julia) without needing to write custom API integration code.[5][6][1]
-## Source and provenance
 - Original data source: Argo Global Data Assembly Centers (GDACs), accessed through the Argovis platform.[2][4]
 - Access method: Argovis API (https://argovis-api.colorado.edu/argo) with query filters on time and optional geographic constraints.[1][5]
@@ -25,7 +26,7 @@ The files were programmatically fetched using the Argovis API in Python, convert
 These data are a derivative, convenience-formatted view of the original Argo profile data; they do not modify scientific content, only representation (JSON → tabular CSV).
-## Files included
 Depending on how you upload, you might have some or all of:
@@ -41,7 +42,7 @@ Depending on how you upload, you might have some or all of:
 If additional files are added later (e.g., separate files for metadata, variables, or different time windows), the filenames should clearly reflect their content and time coverage.
-## Data fields (high-level)
 Typical columns in the main CSV include:[8][1]
@@ -59,7 +60,7 @@ Typical columns in the main CSV include:[8][1]
 Users are encouraged to consult Argovis and Argo documentation for full definitions of scientific variables, QC flags, and conventions.[4][3][1]
-## Intended uses
 This dataset is useful for:
@@ -75,7 +76,7 @@ This dataset is useful for:
 Because this dataset is derived from the operational Argo network, it reflects real measurement noise, missing data patterns, and QC flags that are valuable for realistic ML pipelines.[4][3]
-## How to load the data
 Example in Python with pandas:
@@ -101,7 +102,7 @@ df = dataset["train"].to_pandas()
 Adjust split name and file mapping depending on how the dataset is configured on the Hub.[9][10]
-## Limitations and caveats
 - This is a subset in time (and optionally space), not the full Argo archive.[2][4]
 - Deep scientific interpretation (e.g., water mass analysis) requires careful handling of:
@@ -112,7 +113,7 @@ Adjust split name and file mapping depending on how the dataset is configured on
 Users should always refer to the official Argo documentation and Argovis API documentation for authoritative descriptions of variables and processing.[11][1][3]
-## License and attribution
 The underlying Argo data are described as freely available without restriction and are treated similarly to open data in many catalogs.  However, proper acknowledgment of the Argo program is required in any work using these data.[12][2]
@@ -122,7 +123,7 @@ If using this dataset, please include an acknowledgment along the lines of:
 Also cite this Hugging Face dataset if it is used as a curated, preprocessed source in your workflows.
-## Contact and contributions
 If you find issues in the CSV export, want additional time ranges or regions, or would like to contribute parsing scripts or example notebooks (e.g., for plotting sections or training ML models), feel free to:

 - en
 tags:
 - climate
+pretty_name: 'ARGO_Profiles:'
 ---
 Argovis Argo Ocean Profiles
+## Dataset summary:
 This dataset contains ocean profile data collected by the international Argo float program and accessed via the Argovis API.  Each record corresponds to a single profile measured by an autonomous drifting float, including time, location, basin, and associated profile metadata fields that can be joined to the underlying temperature and salinity data structures.  The goal of this dataset is to provide a ready-to-use subset of Argo profiles for machine learning, geospatial analysis, and educational use.[1][2][3][4]
 The files were programmatically fetched using the Argovis API in Python, converted to pandas DataFrames, and exported as CSV.  This makes it easy to load the data in common data science environments (Python, R, Julia) without needing to write custom API integration code.[5][6][1]
+## Source and provenance:
 - Original data source: Argo Global Data Assembly Centers (GDACs), accessed through the Argovis platform.[2][4]
 - Access method: Argovis API (https://argovis-api.colorado.edu/argo) with query filters on time and optional geographic constraints.[1][5]
 These data are a derivative, convenience-formatted view of the original Argo profile data; they do not modify scientific content, only representation (JSON → tabular CSV).
+## Files included:
 Depending on how you upload, you might have some or all of:
 If additional files are added later (e.g., separate files for metadata, variables, or different time windows), the filenames should clearly reflect their content and time coverage.
+## Data fields (high-level):
 Typical columns in the main CSV include:[8][1]
 Users are encouraged to consult Argovis and Argo documentation for full definitions of scientific variables, QC flags, and conventions.[4][3][1]
+## Intended uses:
 This dataset is useful for:
 Because this dataset is derived from the operational Argo network, it reflects real measurement noise, missing data patterns, and QC flags that are valuable for realistic ML pipelines.[4][3]
+## How to load the data:
 Example in Python with pandas:
 Adjust split name and file mapping depending on how the dataset is configured on the Hub.[9][10]
+## Limitations and caveats:
 - This is a subset in time (and optionally space), not the full Argo archive.[2][4]
 - Deep scientific interpretation (e.g., water mass analysis) requires careful handling of:
 Users should always refer to the official Argo documentation and Argovis API documentation for authoritative descriptions of variables and processing.[11][1][3]
+## License and attribution:
 The underlying Argo data are described as freely available without restriction and are treated similarly to open data in many catalogs.  However, proper acknowledgment of the Argo program is required in any work using these data.[12][2]
 Also cite this Hugging Face dataset if it is used as a curated, preprocessed source in your workflows.
+## Contact and contributions:
 If you find issues in the CSV export, want additional time ranges or regions, or would like to contribute parsing scripts or example notebooks (e.g., for plotting sections or training ML models), feel free to: