diff --git a/tests/dataset_writing.py b/tests/dataset_writing.py
new file mode 100644
index 00000000..6bc67148
--- /dev/null
+++ b/tests/dataset_writing.py
@@ -0,0 +1,112 @@
+import glob
+import torch
+
+torch.set_default_dtype(torch.float64)
+
+from hippynn.databases.h5_pyanitools import PyAniFileDB
+from hippynn.databases import NPZDatabase
+from hippynn.tools import is_equal_state_dict
+
+
+# compare if databases are equal, split by split
+def eqsplit(db1, db2, raise_error=True):
+    return is_equal_state_dict(db1.splits, db2.splits, raise_where=raise_error)
+
+
+if __name__ == "__main__":
+    CLEANUP = True  # delete datasets afterwards
+    # Example dataset
+    location = "../../datasets/new_qm9_clean.npz"
+    seed = 1
+    num_workers = 0
+    db_info = {}
+    db1 = NPZDatabase(
+        file=location,
+        seed=seed,
+        num_workers=num_workers,
+        allow_unfound=True,
+        **db_info,
+        inputs=None,
+        targets=None,
+    )
+
+    # test remove_high_property
+    db1.remove_high_property("E", species_key="Z", atomwise=False, norm_per_atom=True, std_factor=5)
+
+    # throw stuff away
+    db1.make_random_split("random stuff", 0.99)
+    del db1.splits["random stuff"]  # remove something at random
+
+    new_ani_file = "TEST_clean_ani1x.h5"
+    new_npz_file = "TEST_clean_ani1x.npz"
+
+    # Divide up the dataset in a bunch of splits.
+    db1.make_random_split("first", 0.5)
+    db1.make_random_split("second", 0.2)
+    db1.make_random_split("third", 3)  # integer
+    db1.split_the_rest("remaining")
+    # write an npz version and reload it.
+    db1.write_npz(file=new_npz_file, record_split_masks=True, overwrite=True)
+    db3 = NPZDatabase(file=new_npz_file, seed=seed, num_workers=num_workers, allow_unfound=True, inputs=None, targets=None, auto_split=True, **db_info)
+
+    # write an h5 version and reload it.
+    db1.write_h5(split=True, h5path=new_ani_file, species_key="Z", overwrite=True)
+    db2 = PyAniFileDB(
+        file=new_ani_file,
+        species_key="Z",
+        seed=seed,
+        num_workers=num_workers,
+        allow_unfound=True,
+        **db_info,
+        inputs=None,
+        targets=None,
+        auto_split=True,
+    )
+    new_ani_filetwo = "TEST_clean_ani1x_2.h5"
+    # trim this dataset earlier than others.
+    db2.trim_by_species("Z")
+    # write and load new database.
+    db2.write_h5(split=True, h5path=new_ani_filetwo, species_key="Z", overwrite=True)
+    db4 = PyAniFileDB(
+        file=new_ani_filetwo,
+        species_key="Z",
+        seed=seed,
+        num_workers=num_workers,
+        allow_unfound=True,
+        **db_info,
+        inputs=None,
+        targets=None,
+        auto_split=True,
+    )
+
+    for i, d in enumerate([db1, db2, db3, db4]):
+        print("sorting", i)
+        d.sort_by_index()
+        print("trimming", i)
+        d.trim_by_species("Z", keep_splits_same_size=True)  # can do either way if disable caching test.
+
+    # "sys_number" comes from h5 format databases, but not present in others.
+    for d in [db2, db4]:
+        for s in d.splits:
+            del d.splits[s]["sys_number"]
+
+    db1.add_split_masks()  # this first didn't ever get split masks! add them now.
+
+    print("NPZ Equality:", eqsplit(db1, db3))
+    print("H5 Equality:", eqsplit(db2, db4))
+    print("NPZ-H5 Equality1:", eqsplit(db1, db2))
+    print("NPZ-H5 Equality2:", eqsplit(db2, db3))
+
+    # test caching routine.
+    db2p = db2.make_database_cache(file="TEST_CACHE_FROMH5.npz", overwrite=True, quiet=True)
+    print("H5 to cache Equality:", eqsplit(db2, db2p))
+    db3p = db3.make_database_cache(file="TEST_CACHE_FROMNPZ.npz", overwrite=True, quiet=True)
+    print("NPZ to cache Equality:", eqsplit(db3, db3p))
+
+    for ext in ["npz", "h5"]:
+
+        for file in glob.glob(f"./TEST*.{ext}"):
+            print(file)
+            import os
+
+            os.remove(file)