Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
best_metric: null
best_model_checkpoint: null
epoch: double
eval_steps: int64
global_step: int64
is_hyper_param_search: bool
is_local_process_zero: bool
is_world_process_zero: bool
log_history: list<item: struct<epoch: double, step: int64, train/loss_cls_postfix: double, grad_norm: double, lea (... 170 chars omitted)
child 0, item: struct<epoch: double, step: int64, train/loss_cls_postfix: double, grad_norm: double, learning_rate: (... 158 chars omitted)
child 0, epoch: double
child 1, step: int64
child 2, train/loss_cls_postfix: double
child 3, grad_norm: double
child 4, learning_rate: double
child 5, loss: double
child 6, eval_loss: double
child 7, eval_loss_cls_postfix: double
child 8, eval_runtime: double
child 9, eval_samples_per_second: double
child 10, eval_steps_per_second: double
logging_steps: int64
max_steps: int64
num_input_tokens_seen: int64
num_train_epochs: int64
save_steps: int64
stateful_callbacks: struct<TrainerControl: struct<args: struct<should_epoch_stop: bool, should_evaluate: bool, should_lo (... 79 chars omitted)
child 0, TrainerControl: struct<args: struct<should_epoch_stop: bool, should_evaluate: bool, should_log: bool, should_save: b (... 55 chars omitted)
child 0, args: struct<should_epoch_stop: bool, should_evaluate: bool, should_log: bool, should_save: bool, should_t (... 19 chars omitted)
child 0, should_epoch_stop: bool
child 1, should_evaluate: bool
child 2, should_log: bool
child 3, should_save: bool
child 4, should_training_stop: bool
child 1, attributes: struct<>
total_flos: double
train_batch_size: int64
trial_name: null
trial_params: null
_from_model_config: bool
transformers_version: string
to
{'_from_model_config': Value('bool'), 'transformers_version': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
best_metric: null
best_model_checkpoint: null
epoch: double
eval_steps: int64
global_step: int64
is_hyper_param_search: bool
is_local_process_zero: bool
is_world_process_zero: bool
log_history: list<item: struct<epoch: double, step: int64, train/loss_cls_postfix: double, grad_norm: double, lea (... 170 chars omitted)
child 0, item: struct<epoch: double, step: int64, train/loss_cls_postfix: double, grad_norm: double, learning_rate: (... 158 chars omitted)
child 0, epoch: double
child 1, step: int64
child 2, train/loss_cls_postfix: double
child 3, grad_norm: double
child 4, learning_rate: double
child 5, loss: double
child 6, eval_loss: double
child 7, eval_loss_cls_postfix: double
child 8, eval_runtime: double
child 9, eval_samples_per_second: double
child 10, eval_steps_per_second: double
logging_steps: int64
max_steps: int64
num_input_tokens_seen: int64
num_train_epochs: int64
save_steps: int64
stateful_callbacks: struct<TrainerControl: struct<args: struct<should_epoch_stop: bool, should_evaluate: bool, should_lo (... 79 chars omitted)
child 0, TrainerControl: struct<args: struct<should_epoch_stop: bool, should_evaluate: bool, should_log: bool, should_save: b (... 55 chars omitted)
child 0, args: struct<should_epoch_stop: bool, should_evaluate: bool, should_log: bool, should_save: bool, should_t (... 19 chars omitted)
child 0, should_epoch_stop: bool
child 1, should_evaluate: bool
child 2, should_log: bool
child 3, should_save: bool
child 4, should_training_stop: bool
child 1, attributes: struct<>
total_flos: double
train_batch_size: int64
trial_name: null
trial_params: null
_from_model_config: bool
transformers_version: string
to
{'_from_model_config': Value('bool'), 'transformers_version': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Welcome, this is the data repository of scBaseTraj. This dataset contains more than 48 million trajectories spanning 71 tissues, with each trajectory covering an average of 9.3 consecutive states. Approximately three-quarters of trajectories are confined within a single CytoTRACE2 stage, corresponding to relatively stable cell states, while the remaining trajectories span multiple CytoTRACE2 stages and capture dynamic state transitions.
We used this dataset to train a temporal generative AI model, CellTempo, to forecast future cellular dynamics by representing cells as learned semantic codes and training an autoregressive generation decoder to predict ordered code sequences. It can forecast long-range cell-state transition trajectories and landscapes from snapshot data.
How to use this dataset: https://github.com/EperLuo/CellTempo
Preprint: https://www.biorxiv.org/content/10.64898/2026.02.08.704720v1
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