The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 6321, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowTypeError: Unable to merge: Field npy has incompatible types: list<item: double> vs list<item: list<item: list<item: list<item: float>>>>: Unable to merge: Field item has incompatible types: double vs list<item: list<item: list<item: float>>>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CropDynamicsBench
Processed global data for weather-conditioned vegetation forecasting and in-season yield assessment of maize, rice, soybean, and wheat. NDVI and GPP span 1982--2016; yield history, ERA5-Land, and legacy LAI include 1981--2016. This is the numeric data release, not a collection of model outputs.
Download
Files are losslessly compressed tar shards. Their members preserve the
Data/ layout used by the processing code. manifest.json lists every member's
shape, dtype, size and SHA-256, as well as the checksum of each archive.
The release contains 2,691 files in 52 shards: 5.48 GiB compressed and
44.40 GiB extracted, including the optional SEAS5 supplement. Reserve about
60 GiB for the download cache and extracted data together.
Select product prefixes in shards/ or use the companion code repository's
scripts/download_processed_data.py. All shards can also be obtained with:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="PHENOBIID/CropDynamicsBench", repo_type="dataset",
local_dir="CropDynamicsBench_download")
The download contains archives, not automatically extracted NumPy arrays.
Use the verified extraction script from the code release to restore Data/.
For exact reproduction, pin a dataset commit rather than the moving main branch.
Contents and shapes
| Shard prefix | Contents | Typical shape |
|---|---|---|
crop_active |
Four-crop yield, aligned ERA5-Land, MIRCA-OS month mappings and areas | weather [13,12,360,720]; yield [360,720] |
era5_land |
Monthly 0.5-degree weather before crop-active packing | [12,360,720] per variable/year |
gdhy |
Four-crop annual yield with reordered longitudes | [360,720] |
ndvi |
Monthly and crop-active NDVI, availability and quality fractions | [12,360,720] |
gpp |
Monthly and crop-active GPP totals, daily rates and valid-area fractions | [12,360,720] |
lai |
Legacy monthly and crop-active LAI used for cohort support and comparisons | [12,360,720] |
seas5 |
Optional ensemble-mean seasonal weather on its native 1-degree grid | [6,180,360,3] per initialization |
The 0.5-degree latitude and longitude arrays are shipped with the products:
latitude increases from -89.75 to 89.75, longitude from -179.75 to 179.75.
Do not replace crop-active slots by calendar-month indices. src_rel.npy,
month_rel.npy, valid_rel.npy, and weight_rel.npy define the packed support;
invalid slots and missing values must be masked before model training.
The mapping arrays have shape [12,360,720]: month_rel stores calendar
months 1--12 (0 for padding), src_rel stores zero-based source indices
0--11 (255 for padding), and valid_rel is 1 for active slots. Active months
are packed in ascending calendar-month order. weight_rel is monthly growing
area divided by the sum of growing areas over valid months at that grid cell.
NDVI is dimensionless, yield is t/ha, and the selected GPP state is the daily
mean rate in gC m-2 day-1, not the monthly total.
ERA5-Land channel order is also supplied as variable_order.txt:
d2m, t2m, stl1, stl2, swvl1, swvl2, swvl3, ssrd, pev, u10, v10, sp, tp.
These are physical processed arrays; model normalization must be fitted on
the corresponding training partition, not all years.
Optional seasonal forecasts
SEAS5 contains 432 monthly initializations (1981--2016), six lead months and
25-member ensemble means. Channels are t2m, tp, ssrd, in K, m/day, and
J/m2/day. Latitude descends from 89.5 to -89.5; longitude increases from
0.5 to 359.5. These files are native-grid forecasts, not already
crop-aligned or bias-corrected model inputs. Use the separate SEAS5 crop-weather
interface and training-only calibration before comparisons. The main benchmark
uses realized ERA5-Land weather and is a retrospective conditional assessment.
Evaluation protocol
The companion protocol.json defines the temporal splits. Model fitting through
2001, 2005 and 2009 is paired with evaluation in 2002--2004, 2006--2008 and
2010--2016, respectively. The three blocks comprise 13 evaluation years.
Seeds are 42, 45 and 48; primary nominal hidden fractions are 10%, 30%, 50%
and 70%. Future hidden vegetation is a target, not a permitted model input.
Scope
All selected numeric arrays are preserved byte-for-byte. Duplicate smoke-test directories, intermediate caches, private machine-specific audit logs, model weights, papers and raw-provider archives are excluded. Generated release checksums replace private audit paths, not the original processing history. The full numeric release does not by itself supply every legacy training cache or establish a fresh full-benchmark retraining reproduction. The companion repository documents the verified sample inference and reconstruction stages.
Sources and terms
See sources.json for original provider records, versions, and attribution,
and TERMS.md before reuse. This release does not place all upstream
products under a new blanket license. In particular, rEC-LUE GPP is a
model-derived productivity product, not an independent direct measurement.
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