Datasets:

samuki-hf
/

meco-char

Languages:

Tags:

eye-tracking

reading-times

psycholinguistics

meco

License:

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xet

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train

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Float value 1.541000 was truncated converting to int64
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2015, in array_cast
                  return array.cast(pa_type)
                         ~~~~~~~~~~^^^^^^^^^
                File "pyarrow/array.pxi", line 1147, in pyarrow.lib.Array.cast
                File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 412, in cast
                  return call_function("cast", [arr], options, memory_pool)
                File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
                File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
                  result = GetResultValue(
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Float value 1.541000 was truncated converting to int64
              
              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/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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fixid int64	x int64	y int64	letter string	letternum int64	start float64	stop float64	dur int64	right_bounded_time int64
1	152	150	r	105	1	149	149	3,031
2	143	96	n	2	165	317	153	153
3	240	89	o	8	342	481	140	140
4	266	92	a	10	624	738	115	115
5	338	95	v	14	924	1,108	185	185
7	494	94	n	24	1,443	1,787	345	345
8	574	94	g	29	1,809	1,993	185	185
9	663	99	e	35	2,017	2,411	395	673
10	632	94	e	33	2,421	2,698	278	278
11	701	97	n	37	2,718	2,991	274	274
12	766	102	n	41	3,010	3,414	405	405
13	852	114	r	46	3,435	3,619	185	185
14	897	109	g	49	3,770	3,862	93	93
15	957	131	r	155	4,028	4,652	625	9,335
16	1,056	104	t	59	4,677	5,160	484	1,219
17	1,065	105	.	60	5,313	6,047	735	735
18	1,182	100	a	67	6,076	6,411	336	336
19	1,248	98	o	71	6,427	6,592	166	166
26	1,692	93	i	99	8,351	8,465	115	4,000
27	1,669	90	h	97	8,474	8,823	350	350
29	476	104	e	23	9,091	9,225	135	499
30	270	93	a	10	9,262	9,390	129	236
31	204	98	t	6	9,411	9,517	107	107
47	312	152	t	115	13,092	13,145	54	486
48	190	149	k	107	13,171	13,249	79	268
49	160	136	r	105	13,268	13,456	189	189
50	291	125	e	12	13,488	13,651	164	164
51	492	141	g	126	13,696	13,966	271	271
52	570	152	g	131	13,990	14,134	145	145
53	684	148	d	138	14,253	14,379	127	127
54	762	150	o	143	14,402	14,573	172	172
55	884	151	b	150	14,598	14,711	114	712
56	516	148	h	128	14,759	14,888	130	443
57	443	153	u	123	14,911	15,086	176	176
58	522	157	h	128	15,107	15,243	137	137
59	827	158	d	147	15,288	15,442	155	155
60	953	149	r	155	15,466	15,635	170	170
61	1,113	137		165	15,664	15,780	117	996
62	1,365	122	v	78	15,944	16,065	122	122
63	1,382	119	o	80	16,072	16,235	164	164
64	1,447	125		84	16,253	16,412	160	160
65	1,576	122		92	16,445	16,743	299	299
66	1,722	127	,	101	16,771	16,904	134	134
67	244	180		215	16,991	17,079	89	294
68	163	181	o	209	17,103	17,307	205	205
69	308	175	s	114	17,344	17,467	124	515
70	135	181		208	17,494	17,745	252	252
71	270	194	s	216	17,792	17,930	139	139
72	356	211	e	221	17,954	18,118	165	165
73	467	202	e	228	18,151	18,289	139	139
74	633	215	a	239	18,410	18,685	276	276
75	782	203	d	248	18,853	18,984	132	132
76	841	206	c	252	19,002	19,199	198	198
77	1,074	189	o	266	19,231	19,409	179	179
78	1,144	185		271	19,428	19,645	218	218
79	1,254	190	a	278	19,667	19,882	216	1,690
80	1,450	160	n	186	19,912	20,125	214	214
81	1,495	162	n	189	20,147	20,393	247	408
82	1,445	162	n	186	20,411	20,571	161	161
83	1,667	171	v	199	20,608	20,955	348	348
84	1,767	168	n	205	20,984	21,144	161	468
85	1,616	171	e	196	21,171	21,342	172	172
86	1,773	174	n	205	21,366	21,500	135	135
87	801	226	e	249	21,572	21,607	36	36
88	190	255	s	317	21,676	21,874	199	199
89	377	256	i	329	21,914	22,018	105	105
90	484	260	e	335	22,169	22,308	140	140
91	569	261	s	341	22,331	22,521	191	191
92	708	256	n	349	22,555	22,876	322	322
93	815	244	,	356	22,902	23,099	198	198
94	917	252	o	363	23,125	23,366	242	242
95	1,055	247		371	23,393	23,596	204	706
96	852	252	o	358	23,623	23,781	159	329
97	809	260	,	356	23,802	23,971	170	170
98	942	254	n	364	23,998	24,170	173	173
99	1,080	253	e	373	24,205	24,454	250	250
100	1,184	246	n	379	24,476	24,701	226	226
101	1,231	244	l	382	24,718	24,907	190	190
102	1,353	232		390	24,933	25,121	189	189
103	1,473	228	l	291	25,149	25,261	113	2,066
104	1,588	205		299	25,417	25,486	70	70
105	1,310	241	e	387	25,525	25,624	100	981
106	1,050	254		371	25,654	25,917	264	264
107	1,138	257	a	376	25,940	26,002	63	441
108	936	265	n	364	26,042	26,227	186	186
109	1,057	254		371	26,253	26,444	192	192
110	1,145	254	r	377	26,464	26,639	176	176
111	1,367	245	i	391	26,676	26,867	192	192
112	1,431	237	v	395	26,888	27,032	145	276
113	1,392	240	n	392	27,046	27,176	131	131
114	1,443	237	v	395	27,194	27,373	180	434
115	1,626	211	o	301	27,399	27,581	183	183
116	1,680	218	p	304	27,596	27,666	71	71
117	518	309	r	438	27,744	27,832	89	1,097
118	196	310	a	417	27,874	28,007	134	283
119	153	307	l	415	28,019	28,167	149	149
120	214	304	r	419	28,197	28,448	252	252
121	277	312	j	423	28,471	28,627	157	157
122	390	306		430	28,769	28,988	220	220
123	502	314	v	437	29,021	29,116	96	96

End of preview.

MECO character-level eye-tracking data

Preprocessed fixation data derived from the MECO (Multilingual Eye-tracking Corpus) reading-time dataset, character-indexed.

Folder	Description
`processed/`	Flat per-language analysis files: `<lang>.csv` (one row per fixation across all readers/trials) and `stimulus_<lang>.csv` (per-character stimulus table). Consumed by REPLICA's `replica fetch meco_char`.
`meco_preproc/<lang>/`	Corrected per-(reader, text) fixation CSVs (linebreak/quote fixes applied).
`meco_preproc_raw/<lang>/`	Raw per-(reader, text) fixation CSVs before corrections.
`raw_texts/`	Extracted stimulus text per language (`raw_text_<lang>.csv`).
`visualizations/<lang>/`	PNG fixation visualizations for a sample of (reader, trial) pairs.
`meco_source/`	MECO source CSVs (`passage_data_version1.3.csv`, `sentences.csv`, `supp_texts.csv`).

File naming for fixation CSVs: <lang>/<lang>_<subject>_<trial>.csv.

Languages (13)

Dutch (du), Estonian (ee), English (en), Finnish (fi), German (ge), Greek (gr), Hebrew (he), Italian (it), Korean (ko), Norwegian (no), Russian (ru), Spanish (sp), Turkish (tr).

Source

Derived from MECO (Siegelman et al., 2022, Expanding horizons of cross-linguistic research on reading). Refer to the original MECO license and terms for the underlying data.

Downloads last month: 1,359

Total file size:

172 MB

MECO character-level eye-tracking data

Contents

Languages (13)

Source