Add TsFile (converted from BAIGroup/OlmoEarth-v1-Potomac-Sewage-Spill-2026)

README.md

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+---
+license: apache-2.0
+task_categories:
+- time-series-forecasting
+- tabular-regression
+language:
+- en
+tags:
+- tsfile
+- timeseries
+- time-series
+- format:tsfile
+- geospatial
+- olmoearth
+- chesapeake-bay
+- potomac-river
+- water-quality
+- sewage-spill
+- event-tracking
+- usgs
+pretty_name: Potomac Sewage Spill Reference Dataset (2026) (TsFile)
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: potomac_flow_daily.tsfile
+---
+
+# Potomac Sewage Spill Reference Dataset (2026) (TsFile)
+
+Apache TsFile version of [`BAIGroup/OlmoEarth-v1-Potomac-Sewage-Spill-2026`](https://huggingface.co/datasets/BAIGroup/OlmoEarth-v1-Potomac-Sewage-Spill-2026).
+
+## Overview
+
+Validated, time-aligned reference dataset supporting GeoAI tracking of the **2026 Potomac River sewage spill** (Glen Echo, MD — 240–300 million gallons released from the 72-inch Potomac Interceptor on January 19, 2026). The original repository provides an event timeline, hydrologic context, monitoring station locations, and AOI corridor polygons for AI2's downstream Sentinel-1/2 plume detection.
+
+This TsFile copy contains the **tabular daily streamflow time series** only — the part of the repository that is a time series:
+
+- **8 USGS gauges** documented along the corridor; **846 daily flow records**, **December 2025 – March 2026**, daily granularity. Seven gauges carry data; site `01651000` (anacostia_lower, `anacostia_lower` role) had no NWIS data in the event window and therefore contributes no rows.
+- **Source:** USGS NWIS daily values service, parameter `00060` (discharge, cfs), statistic `00003`.
+- Each record is one gauge-day, phase-tagged for the four-phase spill chronology: `pre_spill_baseline` (2025-12-01 → 2026-01-18), `active_release` (2026-01-19 → 2026-01-24), `bypass_period` (2026-01-25 → 2026-03-14) and `post_recovery` (2026-03-15 → 2026-03-31).
+- This is an event reference / validation dataset, not a training set: 32 station-phase reference points are far below any fine-tuning threshold. BAI prepares time-aligned reference data; the OlmoEarth team performs the Sentinel-1/2 plume detection.
+
+## Schema (TsFile structure)
+
+- **Time** (INT64, milliseconds) — daily timestamp parsed from the source `date` column (`YYYY-MM-DD`), i.e. `00:00:00 UTC` of each day.
+- **site_no** (TAG) — USGS station number, e.g. query one gauge with `WHERE site_no='01646500'`.
+- **discharge_cfs** (FIELD, DOUBLE) — daily mean streamflow in cubic feet per second.
+- **role** (FIELD, STRING) — corridor role of the gauge (e.g. `near_spill`, `tidal_dc`, `lower_potomac`).
+- **phase** (FIELD, STRING) — spill phase for that gauge-day.
+- **name** (FIELD, STRING) — human-readable USGS station name.
+- **latitude** (FIELD, DOUBLE), **longitude** (FIELD, DOUBLE) — gauge coordinates.
+
+`role`, `name`, `latitude` and `longitude` are constant per station and are therefore repeated on every row; they are kept as FIELD columns so no source column is dropped. They can simply be ignored when only the flow measurements are needed.
+
+Type mapping: the source `discharge_cfs`, `latitude` and `longitude` are `double` and stay `DOUBLE`; the remaining non-time columns are strings and stay `STRING`.
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("potomac_flow_daily.tsfile")
+with TsFileReader(str(path)) as reader:
+    schemas = reader.get_all_table_schemas()
+    print("tables:", list(schemas))
+    table_name = next(iter(schemas))
+    table = schemas[table_name]
+    columns = [column.get_column_name() for column in table.get_columns()]
+    print("columns:", columns)
+    field_names = [
+        column.get_column_name()
+        for column in table.get_columns()
+        if column.get_column_name() not in {"Time", "time"}
+    ]
+    if field_names:
+        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
+            batch = result.read_arrow_batch()
+            if batch is not None:
+                print(batch.to_pandas().head())
+```
+
+## Source & license
+
+- Original dataset: <https://huggingface.co/datasets/BAIGroup/OlmoEarth-v1-Potomac-Sewage-Spill-2026>
+- Author / publisher: Ziming Qi and BAI Group
+- Paper: none
+- License: Apache-2.0 (USGS source data and agency reports are public domain)
+
+### Conversion notes
+
+- **One source file only.** `data.parquet` (846 rows) and `tabular/potomac_flow_daily.csv` are documented mirrors of the same daily flow table. Only `data.parquet` was converted; converting both would duplicate every row. The parquet was preferred because it preserves `site_no` as a zero-padded string (`01638500`), whereas the CSV parses it as an integer and drops the leading zero.
+- **Not time series / not included.** The geospatial assets (`.geojson` files such as `waterbodies.geojson`, `monitoring_stations.geojson`, `spill_metadata.geojson`, `event_log.geojson`), `timeline.json`, `event_log.csv`, the CBP manifests, `reference_event_logs/`, docs and `studio/`/`rslearn/` assets are not time series and are **not** part of this TsFile copy. The original dataset remains available for those geospatial and event-narrative assets.
+- **Missing values.** 18 of the 846 `discharge_cfs` values are null in the source (real gaps in the USGS daily record); they are preserved as nulls and were not imputed or dropped.