MMTU-benchmark/MMTU huggingface dataset:
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |
|:--------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| NSW     |     530 |     126 |     191 |     891 |     242 |    8361 |    7162 |    4449 |    2715 |
| NSW     |     213 |      48 |     152 |     444 |      87 |    4703 |    3955 |    3060 |     894 |
| NSW     |     503 |     159 |     403 |     998 |     197 |    8241 |    6622 |    5561 |    1059 |
| NSW     |     771 |     158 |     109 |    1362 |     365 |   10730 |    9041 |    5889 |    3153 |
| NSW     |     466 |      55 |     293 |     921 |     133 |   13345 |   10967 |    8521 |    2446 |

Candidate column headers:
NLAS(Capability)
Population (all)
Population (15 and over)
NLAS($52K)
State/Territory
NLAS(65+)
NLAS(CALD)
NLAS(ATSI)
Population (65 and over)
Population (15-64)



headers: NLAS(Capability)
Population (all)
Population (15 and over)
NLAS($52K)
State/Territory
NLAS(65+)
NLAS(CALD)
NLAS(ATSI)
Population (65 and over)
Population (15-64)
label: State/Territory
NLAS(Capability)
NLAS(ATSI)
NLAS(CALD)
NLAS($52K)
NLAS(65+)
Population (all)
Population (15 and over)
Population (15-64)
Population (65 and over)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1           | Col_2   | Col_3     | Col_4          |
|--------:|:----------------|:--------|:----------|:---------------|
|     239 | Thilges_ Nevaeh |         |           |                |
|     155 | Thilges_ Hunter | Purple  |           | Grand Champion |
|     239 | Thilges_ Nevaeh | Red     |           |                |
|       7 | Kramer_ Evelyn  | Blue    |           |                |
|     239 | Thilges_ Nevaeh | Blue    | 6th Place |                |

Candidate column headers:
Division Placing
Exhibitor Number
Class Placing
Ribbon
Exhibitor Name



headers: Division Placing
Exhibitor Number
Class Placing
Ribbon
Exhibitor Name
label: Exhibitor Number
Exhibitor Name
Ribbon
Division Placing
Class Placing
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 |   Col_2 |   Col_3 |   Col_4 |
|:--------|--------:|--------:|--------:|--------:|
| Jul 13  |      48 |      26 |      45 |       0 |
| Dec 13  |     574 |     569 |     887 |      92 |
| Jun 13  |      57 |      44 |      34 |       0 |
| Sep 13  |     183 |     233 |      26 |      45 |
| Apr 13  |       0 |       0 |       0 |       0 |

Candidate column headers:
Month
Other
Referral
Direct
Organic



headers: Month
Other
Referral
Direct
Organic
label: Month
Direct
Referral
Organic
Other
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1                       | Col_2              | Col_3        | Col_4      |   Col_5 |
|--------:|:----------------------------|:-------------------|:-------------|:-----------|--------:|
|       9 | I - Inherently Governmental | US - United States | AL - Alabama | GADSDEN    |    2006 |
|      13 | I - Inherently Governmental | US - United States | AK - Alaska  | ANCHORAGE  |    2006 |
|       1 | C                           | US - United States | AL - Alabama | BIRMINGHAM |    2006 |
|       3 | I - Inherently Governmental | US - United States | AL - Alabama | BIRMINGHAM |    2006 |
|       1 | I - Inherently Governmental | US - United States | AL - Alabama | AUBURN     |    2006 |

Candidate column headers:
City
First Year on Inventory
State
Reason Code
Country Code
Total FTE



headers: City
First Year on Inventory
State
Reason Code
Country Code
Total FTE
label: Total FTE
Reason Code
Country Code
State
City
First Year on Inventory
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2   |   Col_3 |
|--------:|:--------|:--------|--------:|
|   -7.63 | FIG     | Other   |   40195 |
|  -32.27 | BIR     | Expense |   40186 |
|   75.73 | BIR     | Other   |   40185 |
|  -45.12 | DTC     | Expense |   40185 |
|  -45.12 | DTC     | Expense |   40182 |

Candidate column headers:
Company
Amt
DDate
Type



headers: Company
Amt
DDate
Type
label: Amt
Company
Type
DDate
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0           |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 | Col_6   | Col_7   | Col_8   |
|:----------------|--------:|--------:|--------:|--------:|--------:|:--------|:--------|:--------|
| Sierra Sinclair |      40 |      57 |      60 |      37 |     194 |         |         |         |
| Lane Greiwe     |      55 |      65 |      77 |      40 |     237 | 258.0   | 495.0   | 3.0     |
| Sage Miller     |      35 |      67 |      77 |      34 |     213 |         |         |         |
| Heath Starkey   |      75 |      51 |      77 |      48 |     251 |         |         |         |
| Rachel Sherman  |      65 |      56 |      70 |      35 |     226 |         |         |         |

Candidate column headers:
WEEK 1 TTL
Class 1 
Total
Class 2
Week 2  NAME
Reasons/Hol
Class 3
RANK
OVERALL



headers: WEEK 1 TTL
Class 1 
Total
Class 2
Week 2  NAME
Reasons/Hol
Class 3
RANK
OVERALL
label: Week 2  NAME
Class 1 
Class 2
Class 3
Reasons/Hol
Total
WEEK 1 TTL
OVERALL
RANK
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2   | Col_3   | Col_4   |
|--------:|:--------|:--------|:--------|:--------|
|       3 |         |         |         |         |
|       9 |         | 3.0     |         |         |
|       3 |         |         |         | 1.0     |
|       9 |         |         |         |         |
|       3 |         |         |         |         |

Candidate column headers:
Column17
Column19
Column2
Column12
Column13



headers: Column17
Column19
Column2
Column12
Column13
label: Column2
Column12
Column13
Column17
Column19
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2                | Col_3   | Col_4                | Col_5   |   Col_6 |   Col_7 |   Col_8 | Col_9               | Col_10   | Col_11   | Col_12              |   Col_13 |   Col_14 | Col_15         | Col_16         |
|--------:|:--------|:---------------------|:--------|:---------------------|:--------|--------:|--------:|--------:|:--------------------|:---------|:---------|:--------------------|---------:|---------:|:---------------|:---------------|
|      32 | PRD     | 9/17/2018 1:40:45 PM | ABSI    | 2UM1RGHWEBOSN4PMNL63 | SI      |   43191 |   43281 |   43270 | Alm. Brand Bank A/S | DK       | ABSI     | Alm. Brand Bank A/S |        0 |        0 | not applicable | not applicable |
|      32 | PRD     | 9/17/2018 1:40:45 PM | ABSI    | 2UM1RGHWEBOSN4PMNL63 | SI      |   43191 |   43281 |   43250 | Alm. Brand Bank A/S | DK       | ABSI     | Alm. Brand Bank A/S |        0 |        0 | not applicable | not applicable |
|      32 | PRD     | 9/17/2018 1:40:45 PM | ABSI    | 2UM1RGHWEBOSN4PMNL63 | SI      |   43191 |   43281 |   43251 | Alm. Brand Bank A/S | DK       | ABSI     |                     |        0 |        0 | not applicable | not applicable |
|      32 | PRD     | 9/17/2018 1:40:45 PM | ABSI    | 2UM1RGHWEBOSN4PMNL63 | SI      |   43191 |   43281 |   43272 | Alm. Brand Bank A/S | DK       | ABSI     | Alm. Brand Bank A/S |        0 |        0 | not applicable | not applicable |
|      32 | PRD     | 9/17/2018 1:40:45 PM | ABSI    | 2UM1RGHWEBOSN4PMNL63 | SI      |   43191 |   43281 |   43264 | Alm. Brand Bank A/S | DK       | ABSI     | Alm. Brand Bank A/S |        0 |        0 | not applicable | not applicable |

Candidate column headers:
ns1:execLei
ns1:auctionAvgDuration
ns1:periodEnd
ns1:rptCod
ns1:execMic
ns1:failedTrxVal
ns1:periodStart
ns1:rptDt
ns1:envText
ns1:ctryNca
ns1:rptRunDat
ns1:failedTrx
ns1:auctionNbr
ns1:rptClassification
ns1:micNm
ns1:venueName
ns1:micCd



headers: ns1:execLei
ns1:auctionAvgDuration
ns1:periodEnd
ns1:rptCod
ns1:execMic
ns1:failedTrxVal
ns1:periodStart
ns1:rptDt
ns1:envText
ns1:ctryNca
ns1:rptRunDat
ns1:failedTrx
ns1:auctionNbr
ns1:rptClassification
ns1:micNm
ns1:venueName
ns1:micCd
label: ns1:rptCod
ns1:envText
ns1:rptRunDat
ns1:execMic
ns1:execLei
ns1:rptClassification
ns1:periodStart
ns1:periodEnd
ns1:rptDt
ns1:venueName
ns1:ctryNca
ns1:micCd
ns1:micNm
ns1:failedTrx
ns1:failedTrxVal
ns1:auctionNbr
ns1:auctionAvgDuration
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0            | Col_1                  |   Col_2 | Col_3           |   Col_4 |
|:-----------------|:-----------------------|--------:|:----------------|--------:|
| Long's Peak Blue | Indica Dominant Hybrid |      95 | Garden Remedies |      50 |
| Sunburn          | Sativa                 |      95 | Curaleaf        |      50 |
| Grapefruit       | Sativa                 |      95 | Curaleaf        |      50 |
| Iron Triangle    | Indica Dominant Hybrid |      95 | Curaleaf        |      50 |
| Cornbread        | Indica                 |      95 | Garden Remedies |      50 |

Candidate column headers:
Cultivator
Price 3.5g
Description
Strain
Price 7g



headers: Cultivator
Price 3.5g
Description
Strain
Price 7g
label: Strain
Description
Price 7g
Cultivator
Price 3.5g
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1      | Col_2            | Col_3   |   Col_4 |   Col_5 |
|--------:|:-----------|:-----------------|:--------|--------:|--------:|
|       1 | BKERSFIELD | BECKS_ CRYSTAL L | II      |    7210 |       1 |
|       1 | BKERSFIELD | KEMNITZ_ CARL R  | IV      |   12534 |       1 |
|       1 | BKERSFIELD | PANCHAL_ HASIT   | II      |    9151 |       1 |
|       1 | BKERSFIELD | ASHER_ CURTIS M  | III     |    9459 |       1 |
|       1 | BKERSFIELD | COLEY_ SORAYA M  | IV      |   16744 |       1 |

Candidate column headers:
First Record
BASEPAY
TIMEBASE
AGL
NAME
CAMPUS



headers: First Record
BASEPAY
TIMEBASE
AGL
NAME
CAMPUS
label: First Record
CAMPUS
NAME
AGL
BASEPAY
TIMEBASE
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1             | Col_2     | Col_3   | Col_4             |   Col_5 |   Col_6 | Col_7   | Col_8            |
|--------:|:------------------|:----------|:--------|:------------------|--------:|--------:|:--------|:-----------------|
|       8 | Resistor          | 220 Ohm   | 1/4W    |                   |       2 |       3 |         | Resistors        |
|       1 | Resistor          | 10 K      | 1/4W    |                   |      14 |       3 | Div(2)  | Resistors        |
|      12 | Cap. Electrolytic | 1 uF      | 25V     | 1 uF 25V          |       1 |       5 |         | Capacitors       |
|      11 | Choke             | 47uH      |         | Yell-viol-blk     |       1 |       8 |         | Inductors/Quartz |
|       9 | Capacitor         | 10 nF 103 | MLC 50V | 10 nF 103 MLC 50V |       1 |       8 |         | Capacitors       |

Candidate column headers:
Value
Code
Designation
Notes
Component
Phase
Qty
Id
Category



headers: Value
Code
Designation
Notes
Component
Phase
Qty
Id
Category
label: Id
Component
Value
Notes
Code
Qty
Phase
Designation
Category
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1                 | Col_2   |   Col_3 |   Col_4 |
|--------:|:----------------------|:--------|--------:|--------:|
|       0 |                       |         |       0 |       0 |
|       0 |                       |         |       0 |       0 |
|       0 |                       |         |       0 |       0 |
|       0 |                       |         |       0 |       0 |
|       0 | Shopify & Paypal Fees |         |       0 |       0 |

Candidate column headers:
Sale profit/loss
Total profit/loss
Item description
Shipping profit/loss
Purchase price



headers: Sale profit/loss
Total profit/loss
Item description
Shipping profit/loss
Purchase price
label: Total profit/loss
Item description
Purchase price
Sale profit/loss
Shipping profit/loss
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |
|--------:|--------:|--------:|--------:|
|   40196 |       2 |       4 |      25 |
|   40228 |       6 |       8 |      22 |
|   40224 |       2 |       8 |      29 |
|   40227 |       5 |       8 |      23 |
|   40191 |       4 |       3 |      13 |

Candidate column headers:
DDay
WWeek
Attend
DDate



headers: DDay
WWeek
Attend
DDate
label: DDate
DDay
WWeek
Attend
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0                          | Col_1   | Col_2      | Col_3          |   Col_4 | Col_5                    | Col_6          |
|:-------------------------------|:--------|:-----------|:---------------|--------:|:-------------------------|:---------------|
| DHC-PRESSE                     | MME     | CHRISTINE  | HENTZE         |    2300 | CHAUNY                   | 03 23 52 11 75 |
| THIVOLLE                       | MR      | NICOLAS    | THIVOLLE CAZAT |    1300 | VIRIGNIN                 | 04 79 42 52 44 |
| CARREFOUR MARCHANDISE INTERNAT | MR      | VINCENT    | ROUSSEAU       |    1700 | MIRIBEL                  | 04 78 55 14 27 |
|                                | MR      | DANIEL     | PANETTO        |    1500 | AMBERIEU EN BUGEY        | 04 74 46 89 03 |
|                                | MR      | CHRISTOPHE | GELY           |    1400 | CHATILLON SUR CHALARONNE | 04 74 55 05 86 |

Candidate column headers:
TEL
RAISON_SOCIALE
NOM_GESTIONNAIRE
PRENOM
LOCALITE
CODE_POSTAL
CIVILITE



headers: TEL
RAISON_SOCIALE
NOM_GESTIONNAIRE
PRENOM
LOCALITE
CODE_POSTAL
CIVILITE
label: RAISON_SOCIALE
CIVILITE
PRENOM
NOM_GESTIONNAIRE
CODE_POSTAL
LOCALITE
TEL
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0             |   Col_1 |   Col_2 |   Col_3 | Col_4   | Col_5   |
|:------------------|--------:|--------:|--------:|:--------|:--------|
| Prodotti caseari  |    1992 |      15 |    4873 | Bertini | Nord    |
| Prodotti caseari  |    1993 |      56 |    8076 | Farace  | Sud     |
| Prodotti caseari  |    1993 |      41 |    8516 | Farace  | Ovest   |
| Prodotti agricoli |    1992 |      33 |    7191 | Farace  | Nord    |
| Prodotti caseari  |    1992 |      12 |    3338 | Farace  | Ovest   |

Candidate column headers:
Pezzi
Prodotto
Rappresentante
Vendite
Area
Anno



headers: Pezzi
Prodotto
Rappresentante
Vendite
Area
Anno
label: Prodotto
Anno
Pezzi
Vendite
Rappresentante
Area
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |    Col_2 |    Col_3 |
|--------:|--------:|---------:|---------:|
| 88.0952 |       1 | 0.793651 | 0.793651 |
| 66.6667 |       1 | 0.793651 | 0.793651 |
| 85.7143 |       1 | 0.793651 | 0.793651 |
| 71.4286 |       1 | 0.793651 | 0.793651 |
| 53.1746 |       1 | 0.793651 | 0.793651 |

Candidate column headers:
Percent
Frequency
Valid Percent
Cumulative Percent



headers: Percent
Frequency
Valid Percent
Cumulative Percent
label: Cumulative Percent
Frequency
Percent
Valid Percent
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0                  | Col_1   |   Col_2 |    Col_3 |   Col_4 | Col_5   |
|:-----------------------|:--------|--------:|---------:|--------:|:--------|
| SE Quadrant 0-10 Miles | H-3     |   41648 |  0.00892 | 0.00843 | No      |
| SW Quadrant 0-10 Miles | Co-60   |   41648 |  0.00286 | 0.00603 | No      |
| SE Quadrant 0-10 Miles | H-3     |   41960 |  0.0468  | 0.0103  | Yes     |
| NE Quadrant 0-10 Miles | H-3     |   41842 |  0.0392  | 0.0213  | No      |
| NW Quadrant 0-10 Miles | H-3     |   41836 | -0.00805 | 0.0168  | No      |

Candidate column headers:
Nuclide
SampleStd
Location
CollectDate
SampleCon
Sig



headers: Nuclide
SampleStd
Location
CollectDate
SampleCon
Sig
label: Location
Nuclide
CollectDate
SampleCon
SampleStd
Sig
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1     | Col_2   | Col_3     |     Col_4 |   Col_5 |
|:--------|:----------|:--------|:----------|----------:|--------:|
| UPS     | Taipei    | TPE     | Taipei    | 121.244   | 25.0883 |
| DHL     | Bahrain   | BAH     | Bahrain   |  50.6203  | 26.2736 |
| FedEx   | Frankfurt | FRA     | Frankfurt |   8.55765 | 50.0283 |
| FedEx   | Tokyo     | NRT     | Tokyo     | 140.379   | 35.7729 |
| UPS     | Koln Bonn | CGN     | Koln Bonn |   7.1327  | 50.8683 |

Candidate column headers:
City
Lat
Airport
Integrator
Long
Hub



headers: City
Lat
Airport
Integrator
Long
Hub
label: Integrator
Hub
Airport
City
Long
Lat
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0       |   Col_1 | Col_2   | Col_3   | Col_4   |   Col_5 |
|:------------|--------:|:--------|:--------|:--------|--------:|
| Pinot B     |     1   | glass   |         | 5.0     |    5    |
| Zinfandel 1 |     1   | glass   |         | 5.0     |    5    |
| Pinot A     |     1   | glass   |         | 5.0     |    5    |
| Pinot A     |     0.5 | glass   |         | 2.5     |    2.5  |
| Pinot B     |     1   | bottle  | 25.36   |         |   25.36 |

Candidate column headers:
Unit
Glass
Qty
Bottle
Wine
Ounces



headers: Unit
Glass
Qty
Bottle
Wine
Ounces
label: Wine
Qty
Unit
Bottle
Glass
Ounces
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0          |   Col_1 | Col_2   | Col_3   | Col_4   | Col_5     | Col_6     | Col_7         |   Col_8 |   Col_9 |
|:---------------|--------:|:--------|:--------|:--------|:----------|:----------|:--------------|--------:|--------:|
| De Ruijterkade |     139 |         |         | 1011AC  | Amsterdam | Amsterdam | Noord-Holland | 52.3775 | 4.90758 |
| De Ruijterkade |     113 |         |         | 1011AB  | Amsterdam | Amsterdam | Noord-Holland | 52.3777 | 4.90606 |
| De Ruijterkade |     112 | C       |         | 1011AB  | Amsterdam | Amsterdam | Noord-Holland | 52.3778 | 4.90581 |
| De Ruijterkade |     107 |         | 5.0     | 1011AB  | Amsterdam | Amsterdam | Noord-Holland | 52.3778 | 4.90539 |
| De Ruijterkade |     112 | D       |         | 1011AB  | Amsterdam | Amsterdam | Noord-Holland | 52.3778 | 4.90583 |

Candidate column headers:
huisletter
provincie
huisnummertoevoeging
woonplaats
openbareruimte
lat
lon
postcode
huisnummer
gemeente



headers: huisletter
provincie
huisnummertoevoeging
woonplaats
openbareruimte
lat
lon
postcode
huisnummer
gemeente
label: openbareruimte
huisnummer
huisletter
huisnummertoevoeging
postcode
woonplaats
gemeente
provincie
lat
lon
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 | Col_2     | Col_3   |
|--------:|--------:|:----------|:--------|
|  160313 |   42398 | Gar       | ExCL    |
|  160078 |   42378 | FGCU BLVD | Ref SA  |
|  160029 |   42375 | traffic   | Ref SA  |
|  160233 |   42390 | NV        | Ref SA  |
|  160322 |   42399 | NLV       | VRTP    |

Candidate column headers:
Clearance
Date
Location
Case#



headers: Clearance
Date
Location
Case#
label: Case#
Date
Location
Clearance
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1               | Col_2   |   Col_3 |
|--------:|:--------------------|:--------|--------:|
|   41365 | For the gardener    | Jerry   |   789.5 |
|   41369 | For the gardener    | Jerry   |    54   |
|   41355 | Houseplants Central | Rosa    |   345   |
|   41365 | Aalst Central Pond  | Jokas   |   768   |
|   41381 | The Black Tulip     | Jokas   |   876   |

Candidate column headers:
Date
Amounth
Sales
Name Client



headers: Date
Amounth
Sales
Name Client
label: Date
Name Client
Sales
Amounth
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 | Col_2   | Col_3       |   Col_4 |   Col_5 | Col_6   |   Col_7 |
|--------:|--------:|:--------|:------------|--------:|--------:|:--------|--------:|
|    2015 |       7 | 179-03  | Huiswaard   |   42187 |   42215 | 0.31    |      10 |
|    2015 |       2 |         | Huiswaard   |   42047 |   42075 |         |      23 |
|    2015 |       4 | 61-12   | Bierkade    |   42103 |   42131 | 0.68    |      20 |
|    2015 |       1 |         | Vroonermeer |   42019 |   42047 |         |      27 |
|    2015 |       8 | 179-09  | Huiswaard   |   42215 |   42243 | 0.48    |      15 |

Candidate column headers:
Eind datum
Omschrijving
Periode
Start datum
Jaar
Totaal N02
Concentratie NO2
Blauw code



headers: Eind datum
Omschrijving
Periode
Start datum
Jaar
Totaal N02
Concentratie NO2
Blauw code
label: Jaar
Periode
Blauw code
Omschrijving
Start datum
Eind datum
Totaal N02
Concentratie NO2
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1          |   Col_2 |   Col_3 |   Col_4 |       Col_5 |   Col_6 |   Col_7 |       Col_8 |       Col_9 |   Col_10 |   Col_11 |      Col_12 |   Col_13 |
|--------:|:---------------|--------:|--------:|--------:|------------:|--------:|--------:|------------:|------------:|---------:|---------:|------------:|---------:|
|     509 | Elgin          | 5235601 | 5151072 | 5024473 | 5.29626e+06 | 5185139 | 5187975 | 5.10023e+06 | 5.11208e+06 |  5123420 |  1401245 | 5.24429e+06 |  4887370 |
|     505 | Parkland       | 7767128 | 7358939 | 7177203 | 7.29383e+06 | 6293755 | 6330339 | 5.9292e+06  | 5.46513e+06 |  4811891 |  1255540 | 4.70407e+06 |  4020180 |
|     515 | Prairie State  | 2579202 | 2640923 | 2576408 | 3.0237e+06  | 2270252 | 2267229 | 2.22074e+06 | 2.38241e+06 |  3337211 |   740195 | 4.59002e+06 |  4163200 |
|     510 | South Suburban | 6851677 | 6073243 | 5923431 | 5.96898e+06 | 4866829 | 5784132 | 4.49691e+06 | 4.82264e+06 |  5495905 |   738949 | 5.32448e+06 |  3036720 |
|     518 | Carl Sandburg  | 5056499 | 4864446 | 4745888 | 4.25931e+06 | 3290144 | 2852495 | 2.56299e+06 | 2.43551e+06 |  2170086 |   407937 | 1.83367e+06 |  1268070 |

Candidate column headers:
FY 2017  Total Grants
FY 2009  Rescission
FY 2012  Total Grants
FY 2011  Total Grants
Dist.  No.
District/College
FY 2016  Total Grants
FY 2018  Total Grants
FY 2013  Total Grants
FY 2010  Total Grants
FY 2014  Total Grants
FY 2015 Total Grants
FY 2009  Total Grants
FY 2008 Total Grants



headers: FY 2017  Total Grants
FY 2009  Rescission
FY 2012  Total Grants
FY 2011  Total Grants
Dist.  No.
District/College
FY 2016  Total Grants
FY 2018  Total Grants
FY 2013  Total Grants
FY 2010  Total Grants
FY 2014  Total Grants
FY 2015 Total Grants
FY 2009  Total Grants
FY 2008 Total Grants
label: Dist.  No.
District/College
FY 2008 Total Grants
FY 2009  Total Grants
FY 2009  Rescission
FY 2010  Total Grants
FY 2011  Total Grants
FY 2012  Total Grants
FY 2013  Total Grants
FY 2014  Total Grants
FY 2015 Total Grants
FY 2016  Total Grants
FY 2017  Total Grants
FY 2018  Total Grants
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 |             Col_2 |     Col_3 |
|:--------|--------:|------------------:|----------:|
| 2014-01 |   37326 |   36511           | 4479.88   |
| 2015-03 | 4875355 |       1.27473e+06 |   35.403  |
| 2015-02 | 3600623 | -533874           |  -12.9127 |
| 2014-12 | 3742317 |  341495           |   10.0415 |
| 2014-09 | 2534544 |  299941           |   13.4225 |

Candidate column headers:
New gTLDs Registrations
New GTLD Growth
Month
New gTLD Growth (%)



headers: New gTLDs Registrations
New GTLD Growth
Month
New gTLD Growth (%)
label: Month
New gTLDs Registrations
New GTLD Growth
New gTLD Growth (%)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1   | Col_2   |   Col_3 |   Col_4 |   Col_5 |   Col_6 | Col_7   | Col_8   |   Col_9 |   Col_10 |   Col_11 |
|:--------|:--------|:--------|--------:|--------:|--------:|--------:|:--------|:--------|--------:|---------:|---------:|
| A       | A45     | A451    |  14.333 |   4.777 |      43 |       2 | ESL     | ESL     |  0.6666 |   0      |   0.6666 |
| A       | A45     | A451    |  12.334 |   4.111 |      37 |       2 | SOC     | SOC     |  0.6666 |   0      |   0.6666 |
| A       | A45     | A451    |  20.666 |   6.888 |      60 |       3 | PHYS    | PHYS    |  0.6666 |   0      |   0.6666 |
| A       | A45     | A451    |  12.666 |   4.222 |      39 |       1 | CINEM   | CINEM   |  0.3333 |   0      |   0.3333 |
| A       | A45     | A452    |  14.667 |   4.889 |      45 |       2 | COMPU   | COMPU   |  0.6666 |   0.6666 |   0      |

Candidate column headers:
INSTIT_INTENT_RECAT
DEPT_DIV
Sections1
FT_FTE_Faculty
YRQ
TotalEnrollments
PT/ML_FTE_Faculty
TotalFTES
altDeptDiv
SumOfFacultyFTEF
YR
AnnlzdFTES



headers: INSTIT_INTENT_RECAT
DEPT_DIV
Sections1
FT_FTE_Faculty
YRQ
TotalEnrollments
PT/ML_FTE_Faculty
TotalFTES
altDeptDiv
SumOfFacultyFTEF
YR
AnnlzdFTES
label: INSTIT_INTENT_RECAT
YR
YRQ
TotalFTES
AnnlzdFTES
TotalEnrollments
Sections1
DEPT_DIV
altDeptDiv
SumOfFacultyFTEF
FT_FTE_Faculty
PT/ML_FTE_Faculty
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1         |   Col_2 | Col_3    | Col_4    | Col_5   |   Col_6 | Col_7   |   Col_8 | Col_9               |
|--------:|:--------------|--------:|:---------|:---------|:--------|--------:|:--------|--------:|:--------------------|
|       2 | Ian Lawton    |     434 | 00:33:28 | 00:33:27 | M Sen   |       2 | Male    |       2 | Liverpool Harriers  |
|      48 | Ailbhe McDaid |     171 | 00:41:08 | 00:41:05 | f Sen   |       4 | Female  |       5 | Liverpool Harriers  |
|      14 | Dewi Roberts  |     148 | 00:36:52 | 00:36:52 | MV45    |       2 | Male    |      14 |                     |
|      12 | Luke Jockins  |     412 | 00:36:43 | 00:36:42 | M Sen   |       8 | Male    |      12 | Liverpool Harriers  |
|      31 | Adam Gordon   |      43 | 00:39:19 | 00:39:16 | MV40    |       5 | Male    |      30 | Helsby Running Club |

Candidate column headers:
Category
Gender Position
Club
Race No
Categ Pos
Net Time
Position
Name
Gender
Time



headers: Category
Gender Position
Club
Race No
Categ Pos
Net Time
Position
Name
Gender
Time
label: Position
Name
Race No
Time
Net Time
Category
Categ Pos
Gender
Gender Position
Club
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2         | Col_3        |   Col_4 |
|--------:|:--------|:--------------|:-------------|--------:|
|    2004 | Percent | United States | 1400 to 1799 |      20 |
|    2001 | Percent | United States | 2400 to 2999 |      18 |
|    1999 | Percent | United States | 1800 to 2399 |      29 |
|    2003 | Percent | United States | 4000 or more |       6 |
|    2002 | Percent | United States | 2400 to 2999 |      18 |

Candidate column headers:
Region
Year
Area
Type
Value



headers: Region
Year
Area
Type
Value
label: Year
Type
Region
Area
Value
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1   | Col_2   |
|:--------|:--------|:--------|
| FTA     | TIGER   |         |
| FHWA    | NSB     |         |
| FTA     | JARC    |         |
| FHWA    | FLHP    |         |
| FHWA    | RTP     |         |

Candidate column headers:
Min award
Acronym
Agency*



headers: Min award
Acronym
Agency*
label: Agency*
Acronym
Min award
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2   | Col_3               | Col_4   | Col_5   | Col_6   | Col_7   | Col_8                   |   Col_9 |   Col_10 |
|--------:|:--------|:--------|:--------------------|:--------|:--------|:--------|:--------|:------------------------|--------:|---------:|
|       0 | 0       | 0       | WarrantyEndDate     | IFC     | B       | AFTALE  | #REF!   | Single value : IfcDate  |       0 |        0 |
|       0 | 0       | 0       | ConstructionWork    | CUNECO  | A       | BASIS   | #REF!   | Single value:  IfcLabel |       0 |        0 |
|       0 | 0       | 0       | ManufactorModelType | IFC     | A       | BASIS   | #REF!   | Single value:  IfcLabel |       0 |        0 |
|       0 | 0       | 0       | ManufactorTelephone | CUNECO  | A       | BASIS   | #REF!   | Single value:  IfcLabel |       0 |        0 |
|       0 | B       | AFTALE  | SupplyAgreement     | CUNECO  | B       | AFTALE  | #REF!   | Single value:  IfcLabel |       0 |        0 |

Candidate column headers:
Kolonne6
Kolonne4
Kolonne11
Kolonne2
Kolonne14
Kolonne10
Kolonne1
Kolonne8
Kolonne9
Kolonne13
Kolonne3



headers: Kolonne6
Kolonne4
Kolonne11
Kolonne2
Kolonne14
Kolonne10
Kolonne1
Kolonne8
Kolonne9
Kolonne13
Kolonne3
label: Kolonne1
Kolonne2
Kolonne3
Kolonne4
Kolonne6
Kolonne8
Kolonne9
Kolonne10
Kolonne11
Kolonne13
Kolonne14
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |
|:--------|--------:|--------:|--------:|--------:|--------:|--------:|
| Sep     | 9371.52 | 45346.4 | 45863.1 | 4992.4  | 31337.4 |  111663 |
| Feb     | 8868.58 | 41797.9 | 51312.5 | 4328.27 | 29025.9 |  108568 |
| Jan     | 9238.7  | 43580.4 | 51368.9 | 4322.93 | 30444.9 |  111257 |
| Jul     | 9279.68 | 43347.5 | 49105.8 | 4643.51 | 30543.5 |  113587 |
| Jun     | 9125.01 | 41484.4 | 50098.1 | 4594.44 | 31734.1 |  109055 |

Candidate column headers:
North America 2012
Africa 2012
Region
Australia 2012
Asia 2012
South America 2012
Europe 2012



headers: North America 2012
Africa 2012
Region
Australia 2012
Asia 2012
South America 2012
Europe 2012
label: Region
Africa 2012
Europe 2012
North America 2012
South America 2012
Australia 2012
Asia 2012
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2    | Col_3   | Col_4   | Col_5   | Col_6                      | Col_7   |
|--------:|:--------|:---------|:--------|:--------|:--------|:---------------------------|:--------|
|    2011 | Feb     | 2011-Feb | 2011-1  | NSW     | PHN101  | Central and Eastern Sydney | 371     |
|    2011 | Feb     | 2011-Feb | 2011-1  | NSW     | PHN101  | Central and Eastern Sydney | 14      |
|    2011 | Feb     | 2011-Feb | 2011-1  | NSW     | PHN103  | Western Sydney             | 0       |
|    2011 | Feb     | 2011-Feb | 2011-1  | NSW     | PHN102  | Northern Sydney            | 26      |
|    2011 | Feb     | 2011-Feb | 2011-1  | NSW     | PHN102  | Northern Sydney            | np      |

Candidate column headers:
Number-Practices
Year-Month-Num
Payment-Year
Payment-Month
Quarter
PHN-Name
PHN-Code
PHN-State



headers: Number-Practices
Year-Month-Num
Payment-Year
Payment-Month
Quarter
PHN-Name
PHN-Code
PHN-State
label: Payment-Year
Payment-Month
Quarter
Year-Month-Num
PHN-State
PHN-Code
PHN-Name
Number-Practices
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   |   Col_2 | Col_3                | Col_4   |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |   Col_19 |
|--------:|:--------|--------:|:---------------------|:--------|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|       1 | ZH      |      11 | Ottenbach            | Total   |    1440 |   2.987 |    2075 |     431 |   0.804 |    66684 |    53300 |    0.456 |    41642 |    33400 |    0.452 |    75652 |    60250 |    0.441 |    46715 |
|       1 | ZH      |      51 | Bachenb|lach         | Total   |    2265 |   4.077 |    1800 |     429 |   0.796 |    64438 |    53600 |    0.424 |    40722 |    34133 |    0.423 |    73169 |    60600 |    0.41  |    45668 |
|       1 | ZH      |      39 | Thalheim an der Thur | Total   |     494 |   0.803 |    1627 |     375 |   0.82  |    60476 |    51850 |    0.43  |    37434 |    31967 |    0.427 |    69474 |    58900 |    0.414 |    42404 |
|       1 | ZH      |      58 | Glattfelden          | Total   |    2607 |   3.068 |    1177 |     339 |   0.758 |    56411 |    49800 |    0.407 |    35320 |    31444 |    0.403 |    64988 |    56800 |    0.398 |    40143 |
|       1 | ZH      |      62 | Kloten               | Total   |   11248 |  15.778 |    1403 |     308 |   0.812 |    56426 |    48100 |    0.432 |    36566 |    30394 |    0.439 |    63787 |    53700 |    0.415 |    40804 |

Candidate column headers:
stpf
gini_steink
mean_steink
gini_steinka
mean_steinka
median_reink
snpstbetr
gdenr
mean_stbetr
mean_reinka
mean_reink
median_steink
ktnr
median_steinka
Einheit
gini_reink
ktname
median_stbetr
gini_stbetr
gdename



headers: stpf
gini_steink
mean_steink
gini_steinka
mean_steinka
median_reink
snpstbetr
gdenr
mean_stbetr
mean_reinka
mean_reink
median_steink
ktnr
median_steinka
Einheit
gini_reink
ktname
median_stbetr
gini_stbetr
gdename
label: ktnr
ktname
gdenr
gdename
Einheit
stpf
snpstbetr
mean_stbetr
median_stbetr
gini_stbetr
mean_steink
median_steink
gini_steink
mean_steinka
median_steinka
gini_steinka
mean_reink
median_reink
gini_reink
mean_reinka