The actual data lives in 11 .tsfile files (Apache IoTDB TsFile, a binary time-series format). The HF viewer does not support .tsfile, so it cannot render the real data. The only files the viewer could auto-preview are the sidecar .csv files (adjacency/*_adj.csv, column_mapping.csv, conversion_summary.csv) — but those are static graph/metadata tables, not the time-series data. Showing them would misrepresent this dataset as having no timestamps, so the viewer is turned off (viewer: false).

Each .tsfile does contain a proper time axis. Below is a real preview read back from AUcase/AUcase.tsfile (millisecond Time plus regional case counts; only 4 of 8 regions shown for width):

Time (epoch ms)	Time (UTC)	australian_capital_territory	new_south_wales	queensland	victoria
1585699200000	2020-04-01	4	150	38	51
1585785600000	2020-04-02	3	116	54	68
1585872000000	2020-04-03	4	91	38	49
1585958400000	2020-04-04	2	104	27	30
1586044800000	2020-04-05	3	87	7	20

To read a .tsfile, use the TsFile SDK (Python or Java):

from tsfile import TsFileReader, ColumnCategory

reader = TsFileReader("AUcase/AUcase.tsfile")
schemas = reader.get_all_table_schemas()
table = next(iter(schemas))                      # e.g. "aucase"
fields = [c.get_column_name() for c in schemas[table].get_columns()
          if c.get_category() in (ColumnCategory.FIELD, ColumnCategory.TAG)]
with reader.query_table(table, fields, batch_size=65536) as rs:
    batch = rs.read_arrow_batch()                # Arrow batch; includes the `time` column
    print(batch.to_pandas().head())

Original Dataset Information

Original dataset: https://huggingface.co/datasets/ruiqil/SpatialEpiBench

According to the original dataset README, SpatialEpiBench is a benchmark collection of 11 spatiotemporal epidemic forecasting datasets. It covers public-health surveillance modalities including influenza-like illness surveillance rates, confirmed cases, test positivity, inpatient and outpatient hospitalizations, hospital admissions, doctor visits, and deaths. The datasets span the United States, Canada, and Australia, with daily or weekly temporal resolution depending on the data source.

The original repository provides each time-series dataset as a CSV file, paired with a corresponding spatial adjacency matrix in a _adj.csv file. The original dataset card declares license CC-BY-4.0.

Original dataset overview from the source README:

Dataset	Frequency	Country	Modality	Time
`AUcase`	daily	Australia	cases	2020-2021
`CAcase`	daily	Canada	cases	2020-2021
`CANpositivity`	daily	U.S.	test positivity	2020-2021
`CHNGinpatient`	daily	U.S.	inpatient hospitalizations	2020-2024
`CHNGoutpatient`	daily	U.S.	outpatient visits	2020-2024
`CPRadmissions`	daily	U.S.	hospital admissions	2020-2023
`DVcli`	daily	U.S.	doctor visits	2020-present
`HHShosp`	daily	U.S.	hospitalizations	2021-2024
`ILI2019`	weekly	U.S.	surveillance rate	2010-present
`JHUcase`	daily	U.S.	cases	2020-2023
`NCHSdeaths`	weekly	U.S.	deaths	2020-present

Converted Files

Each original time-series CSV was converted into one TsFile. The conversion keeps the original wide-table layout as closely as possible: each regional CSV column remains a measurement column in the matching TsFile.

Source CSV	TsFile	Source rows	Regions	Source time column	First time	Last time	Missing source values	Renamed region columns
`AUcase.csv`	`AUcase/AUcase.tsfile`	640	8	`time_value`	2020-04-01	2021-12-31	0	5
`CAcase.csv`	`CAcase/CAcase.tsfile`	640	13	`time_value`	2020-04-01	2021-12-31	0	6
`CANpositivity.csv`	`CANpositivity/CANpositivity.tsfile`	642	51	`time_value`	2020-03-01	2021-12-02	318	0
`CHNGinpatient.csv`	`CHNGinpatient/CHNGinpatient.tsfile`	1309	51	`time_value`	2020-01-01	2023-08-01	34	0
`CHNGoutpatient.csv`	`CHNGoutpatient/CHNGoutpatient.tsfile`	1309	51	`time_value`	2020-01-01	2023-08-01	0	0
`CPRadmissions.csv`	`CPRadmissions/CPRadmissions.tsfile`	644	51	`time_value`	2020-12-16	2023-02-21	0	0
`DVcli.csv`	`DVcli/DVcli.tsfile`	2162	51	`time_value`	2020-02-01	2026-01-01	0	0
`HHShosp.csv`	`HHShosp/HHShosp.tsfile`	978	51	`time_value`	2021-08-23	2024-04-26	0	0
`ILI2019.csv`	`ILI2019/ILI2019.tsfile`	481	52	`time`	2010-10-03	2019-12-29	0	0
`JHUcase.csv`	`JHUcase/JHUcase.tsfile`	730	51	`time_value`	2020-04-01	2022-03-31	0	0
`NCHSdeaths.csv`	`NCHSdeaths/NCHSdeaths.tsfile`	310	51	`time_value`	2020-01-26	2025-12-28	991	0

Conversion Changes

Compared with the original CSV layout, this conversion made these changes:

One source time-series CSV was converted to one TsFile, resulting in 11 TsFile files.
The original date/week column (time_value, or time for ILI2019) was parsed into the TsFile Time column at millisecond precision.
Regional measurement columns were kept as wide measurement columns.
Region column names containing spaces or other schema-unsafe characters were normalized for TsFile/schema parsing, for example spaces were replaced with underscores. The full mapping is provided in column_mapping.csv.
Missing numeric values from the source CSV files remain null/missing in the staged Parquet input. The TsFile import represents these as absent values for the affected measurement/time.
The _adj.csv spatial adjacency matrices were not written into TsFile because they are static graph metadata, not time-varying measurements. They are preserved unchanged under adjacency/.
The original dataset README is included as SOURCE_DATASET_README.md for reference.

No image, video, audio, or other media files were present in the source repository. The source files are CSV tables.

Sidecar Files

adjacency/*.csv: original spatial adjacency matrices copied from the source dataset.
column_mapping.csv: original region column names and their TsFile-safe measurement names.
conversion_summary.csv: row/region/missing-value summary computed from the source CSV files used for this conversion.
SOURCE_DATASET_README.md: original Hugging Face dataset README downloaded from the source repository.

Validation Summary

Source value cells across all main CSV files: 450736
Missing source value cells: 1343
Region columns renamed for TsFile compatibility: 11
All 11 generated .tsfile files were validated locally as present and non-empty.

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