MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| cluster    |
|:-----------|
| education  |
| etc        |
| food       |
| health     |
| logistics  |
| nutrition  |
| protection |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| sub camp id   |
|:--------------|
| tek-007       |
| tek-001       |
| tek-005       |
| tek-014       |
| tek-006       |
| tek-010       |
| tek-011       |
| tek-013       |
| tek-034       |
| tek-035       |
| tek-025       |
| tek-026       |
| tek-048       |
| tek-028       |
| tek-033       |
| tek-015       |
| tek-038       |
| tek-039       |
| tek-018       |
| tek-045       |
| tek-051       |
| tek-046       |
| tek-024       |
| tek-050       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| applicant                        |
|:---------------------------------|
| over the rhine comm hsg          |
| homes on the hill cdc            |
| zanesville mha                   |
| the community builders           |
| ncjc housing & dev. fnd          |
| st. mary dev. corp.              |
| united labor agency housing_ inc |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|   Unnamed: 0 |
|-------------:|
|    0.169031  |
|    0.0448485 |
|    0.0260223 |
|    0.0255102 |
|    0.0552486 |
|    0.0280899 |
|    0.0647482 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a           |
|:------------|
| employee 3  |
| employee 4  |
| employee 5  |
| employee 6  |
| employee 7  |
| employee 8  |
| employee 9  |
| employee 10 |
| employee 11 |
| employee 12 |
| employee 13 |
| employee 14 |
| employee 15 |
| employee 16 |
| employee 17 |
| employee 18 |
| employee 19 |
| employee 20 |
| employee 21 |
| employee 22 |
| employee 23 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| groep   |
|:--------|
| electro |
| gsm     |
| tv      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0      |
|:----------------|
| bottoms         |
| cardigans       |
| cargo           |
| denim pants     |
| general apparel |
| hoodies         |
| jackets         |
| jumpsuits       |
| knits           |
| outerwear       |
| overshirts      |
| pants           |
| pullovers       |
| shirts          |
| shorts          |
| sweaters        |
| tank tops       |
| tees            |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| other notes                                                                                                                            |
|:---------------------------------------------------------------------------------------------------------------------------------------|
| described as p.(phe169*) (sleat) and p.(val170glyfs*29) (sun et al.2012)                                                               |
| this number was previously used for  a different mutation                                                                              |
| described as splice defect / p.(gly171thrfs*5)                                                                                         |
| not frameshift mutation. coding sequence deletions should not cross exon boundaries, see https://variantvalidator.org/recommendations/ |
| described as splice defect / frameshift / p.(gly296delinsglylyslyslysasnprogly)                                                        |
| rs1057516511 g>a mutation also described: likely pathogenic                                                                            |
| prediction using emboss (eg). this was previously a duplicate entry of cln2.130                                                        |
| not p.ala555pro as originally published                                                                                                |
| splice junction                                                                                                                        |
| nucleotide change entered on lovd                                                                                                      |
| at exon/intron boundary                                                                                                                |
| rs121908208 is g>c change                                                                                                              |
| in lovd as g.(6637735_6637891)_(6640145_6640428)del (grch37)                                                                           |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| 39            |
|:--------------|
| phantom       |
| spitfire      |
| young         |
| orion         |
| livingston    |
| shield        |
| merlin        |
| trojan        |
| yitpi         |
| peake         |
| scout         |
| mace          |
| sunmate       |
| magenta       |
| sabel cl plus |
| qal2000       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| kv name        |
|:---------------|
| asc centre     |
| bagalkot       |
| bambolim camp  |
| belgaum cantt  |
| bellary        |
| beml nagar     |
| bidar          |
| bijapur        |
| davangere      |
| dharwad        |
| donimalai      |
| drdo           |
| gulbarga       |
| hassan         |
| hebbal         |
| hospet         |
| hubli no.1     |
| hubli no.2     |
| iisc           |
| jalahalli no.1 |
| jalahalli no.2 |
| karwar         |
| kodagu         |
| koppal         |
| malleswaram    |
| mandovi ins    |
| mangalore no.1 |
| mangalore no.2 |
| meg & centre   |
| mysore         |
| nal            |
| ponda          |
| raichur        |
| railway colony |
| sambra afs     |
| shivamogga     |
| tumkur         |
| vasco no.1     |
| vasco no.2     |
| yelahanka afs  |
| yelahanka crpf |
| yelahanka rwf  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| cond                                               |
|:---------------------------------------------------|
| vf                                                 |
| gvf                                                |
| nef                                                |
| vf+                                                |
| mint                                               |
| ef                                                 |
| nvf                                                |
| aef                                                |
| f                                                  |
| scratches to edge_ otherwise better than very fine |
| eb_ ovf                                            |
| susp refixed_ gvf                                  |
| eb_ ogvf                                           |
| brooched                                           |
| reimpressed_ nvf                                   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| am   |
|:-----|
| mar  |
| apr  |
| may  |
| jun  |
| jul  |
| aug  |
| sep  |
| oct  |
| nov  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| f2_user_id   |
|:-------------|
| kmoye001     |
| lbatt500     |
| kskaws01     |
| lhuan500     |
| ckaul500     |
| jhebro01     |
| senfin01     |
| creeve03     |
| kbattl02     |
| jkrang02     |
| ichang01     |
| alee0007     |
| etorre09     |
| kmock001     |
| amurra03     |
| rmault01     |
| trichb01     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|           s |
|------------:|
|  0.0452763  |
|  0.00340571 |
|  0.0406979  |
|  0.0188424  |
| -0.0391872  |
|  0.0165794  |
| -0.0365552  |
|  0.0462744  |
|  0.0374645  |
| -0.00616486 |
|  0.0172058  |
|  0.0458363  |
|  0.0431375  |
| -0.0376318  |
| -0.0140394  |
| -0.010879   |
| -0.0363894  |
| -0.0306406  |
|  0.043549   |
|  0.0299532  |
| -0.00414277 |
|  0.0479815  |
| -0.041125   |
|  0.0465007  |
| -0.0173452  |
| -0.0490863  |
| -0.0466768  |
|  0.0304372  |
| -0.0407446  |
|  0.0294523  |
|  0.0495774  |
|  0.0305746  |
|  0.0135805  |
| -0.0244503  |
| -0.0442162  |
| -0.0285636  |
|  0.0343334  |
|  0.013996   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a                |
|:-----------------|
| anthony mccollum |
| brian kim        |
| samuel gens      |
| pryce pinney     |
| michael soklaski |
| zachary dahmen   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| top word pairs in tweet in g3   |
|:--------------------------------|
| bigdata_netsci                  |
| honor_estar                     |
| estar_cada                      |
| cada_semana                     |
| semana_aristeguienvivo          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| home  name     |
|:---------------|
| winchester 1   |
| kemptville 2   |
| vars 1         |
| kenmore 1      |
| casselman 1    |
| kenmore 2      |
| kemptville 1   |
| romsa 2        |
| kenmore 3      |
| williamsburg 1 |
| romsa 1        |
| gloucester 2   |
| gloucester 1   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| number         |
|:---------------|
| wacog004       |
| wacog001       |
| wacog-lp01     |
| seago14-l001   |
| wacog14-l002   |
| wacog14-l003   |
| wacog14-l004   |
| wacog14-l005   |
| wacog003       |
| lhmpo1401      |
| lhmpo1402      |
| cag-15l1       |
| lhmpo-15l1     |
| wacog002       |
| wacog14-l001   |
| wacog15-l001   |
| nacog005       |
| wacog16-l001   |
| wacog16-l002   |
| wacogadot-16l1 |
| wacogadot-17l1 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| enrollment tier       |
|:----------------------|
| employee + spouse     |
| employee + family     |
| employee only         |
| employee + child(ren) |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| lunch         |
|:--------------|
| 12:30-14:30   |
| 13:30-14:00   |
| 13:00-14:00   |
| 13:00-15:00   |
| 13:00-13:30   |
| 12:30-14:00   |
| 12:00-13:00   |
| 12:00-12:30   |
| 13:30-14:30   |
| 12:45-13:45   |
| 12:45-14:00   |
| 12:50-14:00   |
| 14:00-14:30   |
| 12:30-13:45   |
| 13:00 - 14:00 |
| 13:00 - 13:30 |
| 12:30-13:00   |
| 14:00-14:20   |
| 13:00-13:20   |
| 13:00 - 13:20 |
| 12:30-13:30   |
| 13:30-13:50   |
| 13:20-13:40   |
| 12:40-13:00   |
| 13:45-14:15   |
| 15:00-15:30   |
| 14:00-14:45   |
| 12.:30-13:30  |


label: 12.:30-13:30
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| b                                                                                                                       |
|:------------------------------------------------------------------------------------------------------------------------|
| initial recognition and changes in the fair value of biological assets and agricultural produce at the point of harvest |
| changes in the net realizable value of agricultural produce after harvest                                               |
| gross profit                                                                                                            |
| net gain from fair value adjustment on investment properties                                                            |
| gain from disposal of farmlands                                                                                         |
| general and administrative expenses                                                                                     |
| selling expenses                                                                                                        |
| other operating results, net                                                                                            |
| result from operations                                                                                                  |
| depreciation and amortization                                                                                           |
| ebitda (unaudited)                                                                                                      |
| adjusted ebitda (unaudited)                                                                                             |
| loss from joint ventures and associates                                                                                 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| ansvarig person         |
|:------------------------|
| maria lindblom          |
| helen askebro           |
| jan andersson           |
| maj sölvesdotter        |
| cecilia lidén           |
| inge bruce              |
| angelica wiklund        |
| björn larsson           |
| eva wikström            |
| birgitta lejman         |
| åsa himmelsköld         |
| aurelia esther emilton  |
| bengt robertson         |
| ann-lis söderberg       |
| jessica bräck           |
| agneta eklund           |
| staffan thore           |
| maud sehlstedt          |
| sune larsson            |
| nina anundsson          |
| erica hult              |
| annika brehmer          |
| ylva tottmar            |
| anna björkland          |
| charlotta gullbrandsson |
| karin lindblom          |
| marianne pira           |
| pernilla öhngren        |
| carina bäckström        |
| ingrid carlquist        |
| henrik andreasson       |
| anna karin lidehäll     |
| cecilia ulleryd         |
| christianne simson      |
| jonatan svensson        |
| mats holmberg           |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| committee_chair   |
|:------------------|
| program review    |
| new programs      |
| curriculum        |
| top tier          |
| appeals           |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a               |
|:----------------|
| 13*bay          |
| 14 bradford     |
| 15*brevard      |
| 16*broward      |
| 17 calhoun      |
| 18*charlotte    |
| 19 citrus       |
| 20*clay         |
| 21*collier      |
| 22 columbia     |
| 23*miami-dade   |
| 24 desoto       |
| 25 dixie        |
| 26*duval        |
| 27*escambia     |
| 28 flagler      |
| 29 franklin     |
| 30 gadsden      |
| 31 gilchrist    |
| 32 glades       |
| 33*gulf         |
| 34 hamilton     |
| 35 hardee       |
| 36 hendry       |
| 37*hernando     |
| 38 highlands    |
| 39*hillsborough |
| 40 holmes       |
| 41*indian river |
| 42 jackson      |
| 43 jefferson    |
| 44 lafayette    |
| 45*lake         |
| 46*lee          |
| 47*leon         |
| 48 levy         |
| 49 liberty      |
| 50 madison      |
| 51*manatee      |
| 52 marion       |
| 53 martin       |
| 54*monroe       |
| 55*nassau       |
| 56*okaloosa     |
| 57 okeechobee   |
| 58*orange       |
| 59*osceola      |
| 60*palm beach   |
| 61 pasco        |
| 62*pinellas     |
| 63*polk         |
| 64*putnam       |
| 65*st. johns    |
| 66*st. lucie    |
| 67*santa rosa   |
| 68*sarasota     |
| 69*seminole     |
| 70 sumter       |
| 71*suwannee     |
| 72*taylor       |
| 73 union        |
| 74*volusia      |
| 75*wakulla      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| row labels   |
|:-------------|
| 2009 q3 fte  |
| 2009 q4 fte  |
| 2010 q1 fte  |
| 2010 q2 fte  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| nom   |
|:------|
| henk  |
| anne  |
| alan  |
| tom   |
| dan   |
| alain |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| 15.a. who was the source of the behaviour?   |
|:---------------------------------------------|
| consultants                                  |
| nurses_other trainees_other                  |
| nurses                                       |
| other                                        |
| other trainees_other                         |
| nurses_other trainees                        |
| other trainees                               |
| consultants_nurses                           |
| consultants_other trainees                   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| sort by equipment type   |
|:-------------------------|
| truck/tractor            |
| trailer                  |
| uniloader                |
| truck 1 ton              |
| farm tractor             |
| equipment                |
| van                      |
| loader                   |
| truck 3/4 ton            |
| car                      |
| truck 2 ton              |
| forklift                 |
| truck small              |
| truck 1/2 ton            |
| water pump               |
| air compressor           |
| utility vehicle          |
| farm truck               |
| shop clean up            |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| nazwa własna   |
|:---------------|
| radomice       |
| cis b          |
| cis a          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a                     |
|:----------------------|
| positives_se_75nt_sum |
| negatives_se_75nt_sum |
| positives_se_50nt_sum |
| negatives_se_50nt_sum |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| color      |
|:-----------|
| 7_ 125_ 0  |
| 20_ 118_ 0 |
| 13_ 121_ 0 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| product groups                                               |
|:-------------------------------------------------------------|
| converted paper                                              |
| copying and graphic paper                                    |
| detergents for dishwashers                                   |
| footwear                                                     |
| furniture                                                    |
| growing media_ soil improvers and mulch                      |
| hand dishwashing detergents                                  |
| hard coverings                                               |
| hard surface cleaning products                               |
| indoor and outdoor paints and varnishes                      |
| industrial and institutional automatic dishwasher detergents |
| industrial and institutional laundry detergents              |
| laundry detergents                                           |
| lubricants                                                   |
| newsprint paper                                              |
| personal_ notebook and tablet computers                      |
| printed paper                                                |
| rinse-off cosmetics products                                 |
| televisions                                                  |
| textiles                                                     |
| tissue paper                                                 |
| tourist accommodation services                               |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| activity location    |
|:---------------------|
| community            |
| health facility      |
| schools              |
| idp camps            |
| refugee community    |
| idp & host community |
| school               |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| d               |
|:----------------|
| akt3            |
| axl             |
| c-met           |
| cdk4/cyclin d1  |
| cdk4/cyclin d3  |
| cdk6/cyclin d1  |
| cdk6/cyclin d3  |
| egfr            |
| erbb2/her2      |
| erk1            |
| erk2/mapk1      |
| erk5/mapk7      |
| erk5/mapk7 (cd) |
| erk7/mapk15     |
| gsk3a           |
| gsk3b           |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| telephone no   |
|:---------------|
| 01234 291000   |
| 01223 418492   |
| 01485 577775   |
| 01553 815499   |
| 01603 622001   |
| 01652 658151   |
| 01493 745888   |
| 01623 629200   |
| 01603 721542   |
| 01993 771155   |
| 01733 767366   |
| 01473 255885   |