tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 687033d1594919978-how-to-make-a-cell-reference-inside-of-array-comming-from-data-model-example
Sheet: DB

IDUser | Name | Reputation |  | IDBook | Title | Price | Editor |  | IDReview | IDUser | IDBook | Stars | Comment
1 | [NAME] | 9 |  | 1 | Book 1 | 19 | Editor 1 |  | 1 | 1 | 3 | 4 | I like it
2 | Michale | 7 |  | 2 | Book 2 | 10 | Editor 1 |  | 2 | 2 | 2 | 1 | I hate it
3 | [NAME] | 7 |  | 3 | Book 3 | 39 | Editor 2 |  | 3 | 2 | 3 | 4 | I recommend it
 |  |  |  | 4 | Book 4 | 10 | Editor 3 |  | 4 | 3 | 1 | 2 | I do not like
 |  |  |  |  |  |  |  |  | 5 | 3 | 4 | 5 | I love it
 |  |  |  |  |  |  |  |  | 6 | 3 | 2 | 3 | Normal book
 |  |  |  |  |  |  |  |  | 7 | 1 | 1 | 5 | I love it
Pivot Table |  |  | Pivot Table (converted to formulas)
Row Labels | Sum of Stars |  | Row Labels
Book 1 | 7 |  |  |  |  | Book 1 |  |  |  | [NAME] is working fine
Editor 1 | 7 |  |  |  |  |  |  |  |  | I am trying to do the same in the H12 [NAME] but I cannot find the way to make it work…
Book 2 | 4
Editor 1 | 4
Book 3 | 8
Editor 2 | 8
Book 4 | 5
Editor 3 | 5
Spreadsheet: 682381d1592056637-find-date-of-which-employee-satisfies-two-conditions-sample-2
Sheet: BONOUS

CODE | NET LIVES | NET FP | DATE(YYYYMMDD) |  |  | CHANGE DATE
 |  |  |  |  |  | AS REQUIRED
192 |  |  | 20190828 |  |  | 20190401 | 20200331
727 |  |  | 20200214
888 |  |  | 20190614
 |  |  |  |  |  | PROJECT : IF AN EMPLOYEE (SHOWN IN SHEET NBD COLUMN "EM-CODE) COMPLETES SIX UNIQUE ORDERS(UNIQUE ORDER MEANS: ' ORDER-NO' WITH UNIQUE' DOB')
 |  |  |  |  |  | AND SUM OF ALL ORDER PREM IS >=50000 IN GIVEN DATE RANGE .EMPLOYEE IS ELIGIBLW FOR BONUS FROM DATE ON WHICH HE SATISFIES TWO CONDITIONS.
 |  |  |  |  |  | SOLUTION REQUIRED : I WANT TO DISPLAY DATE IN COL. [NAME] YYYYMMDD FORMAT(AS SHOWN IN COL. 'C' OF 'NBD' SHEET) ON WHICH EMPLOYEE SATISFY ABOVE MENTIONED
 |  |  |  |  |  | TWO CONDITIONS. SO THAT I CAN CALCULATE BONOUS FROM THAT DATE.
 |  |  |  |  |  | EXPECTED RESULT IS GIVEN IN COL. D
Spreadsheet: 176399d1345837294-pivottable-group-relevant-data-from-across-multiple-rows-under-a-single-row-label-example
Sheet: Example

How you have the data :
Value | To | From |  | <<< Original Data
100 | [NAME] | [NAME]
50 | [NAME] | [NAME]
30 | Sandra | [NAME]
20 | [NAME] | [NAME]
How you coud have the data:
Name | Total To | Total From | Total Balance | <<< Merged Data, Duplicates Removed, Lookup and [NAME] via the SUMIF function in each column
[NAME]
[NAME]
Sandra
[NAME]
The trick is: how do you merge the names from the "To" and "From" columns so that new entries in the original table get dynamically updated and duplicates automatically removed?
Spreadsheet: 861541d1709415239-tracking-last-30-days-blood-readings
Sheet: 2024

2024
date | day | time | Sys | [NAME] | Pul | Sugar
 |  | 10:33:00 | 130 | 88 | 62 | 173
 |  | 10:47:00 | 136 | 80 | 60 | 203
 |  | 10:09:00 | 144 | 72 | 58 | 216
 |  | 10:11:00 | 131 | 76 | 65 | 163
 |  | 10:15:00 | 128 | 72 | 61 | 155
 |  | 10:15:00 | 130 | 76 | 60 | 178
 |  | 10:50:00 | 126 | 78 | 59 | 181
 |  | 10:50:00 | 143 | 81 | 57 | 174
 |  | 10:03:00 | 128 | 79 | 59 | 175
 |  | 10:19:00 | 122 | 77 | 62 | 166
 |  | 10:11:00 | 128 | 80 | 56 | 172
 |  | 10:18:00 | 135 | 74 | 59 | 180
 |  | 10:33:00 | 135 | 72 | 63 | 180
 |  | 10:20:00 | 147 | 76 | 66 | 189
 |  | 11:22:00 | 129 | 77 | 56 | 160
 |  | 10:29:00 | 131 | 78 | 60 | 225
 |  | 10:09:00 | 129 | 80 | 56 | 185
 |  | 10:12:00 | 140 | 81 | 56 | 174
 |  | 10:12:00 | 125 | 81 | 64 | 192
 |  | 11:32:00 | 120 | 75 | 62 | 169
 |  | 12:00:00 | 101 | 78 | 67 | 148
 |  | 10:32:00 | 115 | 73 | 63 | 167
 |  | 10:10:00 | 126 | 76 | 60 | 195
 |  | 10:38:00 | 118 | 74 | 62 | 170
 |  | 10:57:00 | 128 | 81 | 64 | 195
 |  | 10:35:00 | 132 | 75 | 63 | 157
 |  | 11:31:00 | 136 | 84 | 63 | 200
 |  | 10:57:00 | 116 | 74 | 68 | 176
 | Fri | 11:30:00 | 124 | 71 | 63 | 162
 | Sat | 10:32:00 | 126 | 79 | 65 | 191
Spreadsheet: 781404d1653304251-using-filter-function-with-array-criterias-daf-minifs-randarray-aligw
Sheet: Sheet1

 | Name | Value |  |  |  | Paste Value | Ideal output
 | a | 510
 | a | 962
 | a | 584
 | a | 780
 | a | 130
 | a | 824
 | a | 405
 | a | 547
 | b | 310
 | b | 9
 | b | 331
 | b | 443
 | b | 647
 | b | 149
 | c | 513
 | c | 592
 | c | 749
 | c | 126
 | c | 161
 | c | 394
 | c | 344
 | c | 39
 | c | 496
 | c | 540
 | c | 769
 | d | 723
 | d | 761
 | d | 868
 | d | 509
 | d | 401
 | d | 379
 | d | 971
 | e | 319
 | e | 478
 | e | 979
 | e | 918
 | e | 406
 | e | 969
 | e | 240
 | f | 3
 | f | 649
 | f | 841
 | f | 691
 | f | 436
 | f | 397
 | f | 742
 | f | 860
 | f | 711
 | f | 369
 | f | 543
 | f | 896
 | f | 601
 | f | 904
 | g | 962
 | g | 499
 | g | 182
 | g | 503
 | g | 622
 | g | 19
 | g | 383
 | g | 111
 | g | 304
 | g | 716
 | g | 61
 | g | 538
 | g | 834
 | g | 816
Spreadsheet: 690627d1597209295-formula-for-producing-customers-statement-customers-statement
Sheet: Customer Statement

Date | Invoice | Debit | Credit | Cumulative Balance |  | 100 |  |  | [NAME] | Customer  Name
 |  |  |  |  |  |  |  |  | 100 | Ok Mart
2020-08-01 00:00:00 | 1 |  |  |  |  |  |  |  | 101 | [NAME]
2020-08-10 00:00:00 | 1 |  |  |  |  |  |  |  | 102 | Ok Mart
2020-08-11 00:00:00 | 3 |  |  |  |  |  |  |  | 103 | ZIMNAT
2020-08-12 00:00:00 | 3
Spreadsheet: 599524d1543161174-subtract-1-if-cell-contains-text-isnumber_etaf
Sheet: Sheet1

ITEMS | QUANITY | CUSTOMER |  | TOTAL | AVAILABLE
G2/8/2/25 |  | BOB
G2/8/2/25 |  | [NAME]
G2/8/2/25 |  | BOB
G2/8/2/25 |  | [NAME]
G2/8/2/25
G2/8/2/25
G2/8/2/25
G2/8/2/25 |  | BOB
G2/8/2/25
G2/8/2/25
G2/8/2/25
G2/8/2/25 |  | BOB
G2/8/2/25
G2/8/2/25
G2/8/2/25 |  | BOB
G2/8/2/25
G2/8/2/25
G2/8/2/25
G2/8/2/25
G2/8/2/25
G2/8/2/25
Spreadsheet: 44737d1243507118-linking-drop-down-boxes-mock-up-drop-down
Sheet: Formula

SUBSTRUCTURE | Concrete | TYPE | Limecrete | Lime Pozzolana | Portland | Portland-pozzolana | [NAME] | Light Aggregate
 |  | Limecrete | 1 Lime: 1.5 Sand | 3 Lime: 1 Sand: 2 Aggregate | 1 Cement: 1.5 Sand: 3 Aggregate | 1 Cement: 3 Sand: 3 Aggregate | 1 Sulphur: 3 Sand: 3 Aggregate | 1 Cement: 1.5 Sand: 3 Aggregate
 |  | Lime pozzolana | 1 Lime: 1.5 Sand | 2 Lime: 1 Sand: 3 Aggregate | 1 Cement: 2 Sand: 3 Aggregate | 1 Cement: 3 Sand: 6 Aggregate | 1 Sulphur: 3 Sand: 4 Aggregate | 1 Cement: 2 Sand: 3 Aggregate
 |  | Portland | 1 Lime: 3 Sand |  | 1 Cement: 2 Sand: 4 Aggregate |  | 1 Sulphur: 3 Sand: 5 Aggregate | 1 Cement: 2 Sand: 4 Aggregate
 |  | Portland-pozzolana |  |  | 1 Cement: 3 Sand: 6 Aggregate |  |  | 1 Cement: 3 Sand: 6 Aggregate
 |  | [NAME]
 |  | Light aggregate
 | 3
 |  | Limecrete | Lime pozzolana | Portland | Portland-pozzolana | [NAME] | Light aggregate
 |  | 1 Lime: 2 Sand: 4 Aggregate | 3 Lime: 1 Sand: 2 Aggregate 25% Fly Ash | 1 Cement: 1 Sand: 2 Aggregate | 1 Cement: 3 Sand: 3 Aggregate 25% Fly Ash | 1 Sulphur: 3 Sand: 3 Aggregate | 1 Cement: 1.5 Sand: 3 Aggregate
 |  | 1 Lime: 4 Sand: 6 Aggregate | 3 Lime: 1 Sand: 2 Aggregate 50% Fly Ash | 1 Cement: 1.5 Sand: 3 Aggregate | 1 Cement: 3 Sand: 3 Aggregate 50% Fly Ash | 1 Sulphur: 3 Sand: 4 Aggregate | 1 Cement: 2 Sand: 3 Aggregate
 |  |  | 3 Lime: 1 Sand: 2 Aggregate 25% Burnt Clay | 1 Cement: 2 Sand: 4 Aggregate | 1 Cement: 3 Sand: 3 Aggregate 25% Burnt Clay | 1 Sulphur: 3 Sand: 5 Aggregate | 1 Cement: 2 Sand: 4 Aggregate
 |  |  | 3 Lime: 1 Sand: 2 Aggregate 50% Burnt Clay | 1 Cement: 2.5 Sand: 5 Aggregate | 1 Cement: 3 Sand: 3 Aggregate 50% Burnt Clay |  | 1 Cement: 3 Sand: 6 Aggregate
 |  |  | 2 Lime: 1 Sand: 3 Aggregate 25% Fly Ash | 1 Cement: 3 Sand: 6 Aggregate | 1 Cement: 3 Sand: 6 Aggregate 25% Fly Ash
 |  |  | 2 Lime: 1 Sand: 3 Aggregate 50% Fly Ash | 1 Cement: 4 Sand: 8 Aggregate | 1 Cement: 3 Sand: 6 Aggregate 50% Fly Ash
 |  |  | 2 Lime: 1 Sand: 3 Aggregate 25% Burnt Clay |  | 1 Cement: 3 Sand: 6 Aggregate 25% Burnt Clay
 | #N/A |  | 2 Lime: 1 Sand: 3 Aggregate 50% Burnt Clay |  | 1 Cement: 3 Sand: 6 Aggregate 50% Burnt Clay
 |  | Portland | 1 Cement: 1 Sand: 2 Aggregate | 0.209
 |  |  | 1 Cement: 1.5 Sand: 3 Aggregate | 0.159
 |  |  | 1 Cement: 2 Sand: 4 Aggregate | 0.129
 |  |  | 1 Cement: 2.5 Sand: 5 Aggregate | 0.109
 |  |  | 1 Cement: 3 Sand: 6 Aggregate | 0.096
 |  |  | 1 Cement: 4 Sand: 8 Aggregate | 0.08
Spreadsheet: 846236d1697514452-listng-team-wise-names-from-a-list-listing-names-in-teams
Sheet: Sheet1

Sno | Name | Department | Designation | Category | Team |  |  | Required Result
1 | Naveena | Administration | Accountant | A | IPL-D
2 | Nandini | Housekeeping | Supervisor - Housekeeping | B | IPL-C |  |  | IPL-A | IPL-B | IPL-C | IPL-D
3 | Satish | Operations | Supervisor - Operations | D | IPL-B |  |  | Sunil | Satish | Nandini | Naveena
4 | Sunil | Inpark Sales | Assistant Manager - Revenue Operations | D | IPL-A |  |  | Ravikiran | Madhu | Basavaraj | Manjunath
5 | Basavaraj | Inpark Sales | Executive - Inpark Sales | E | IPL-C |  |  | Raju | Girish | Devaraju | Meghana
6 | Manjunath | Maintenance | Manager - Maintenance | F | IPL-D |  |  | Veena | Srikanth | Venkatesh | Venkatesh
7 | Devaraju | Maintenance | Assistant Manager - Maintenance | G | IPL-C
8 | Girish | Maintenance | Assistant Manager - Stores | G | IPL-B
9 | Madhu | Maintenance | Executive - Maintenance | G | IPL-B
10 | Raju | Maintenance | Executive - Maintenance | G | IPL-A |  |  |  | IPL-B | IPL-C | IPL-D | IPL-E
11 | Ravikiran | Maintenance | Assistant Manager -Maintenance | G | IPL-A |  |  | Sunil | Satish | Nandini | Naveena | Test 1
12 | Meghana | Marketing | Digital Marketing Specialist | H | IPL-D |  |  | Raju | Girish | Basavaraj | Manjunath | Test 2
13 | Veena | Operations | Assistant Manager-Snow Park Operations | I | IPL-A |  |  | Ravikiran | Madhu | Devaraju | Meghana
14 | [NAME] (FnB) | Restaurant | Assistant [NAME] | J | IPL-B |  |  | Veena | [NAME] (FnB) | Venkatesh H P | Venkatesh B
15 | Venkatesh H P | Restaurant | Executive - F & B | J | IPL-C
16 | Venkatesh B | Sales | Area Sales Manager (Off: [PHONE]) | K | IPL-D
 | Test 1 |  |  |  | IPL-E
 | Test 2 |  |  |  | IPL-E
Spreadsheet: 61446d1262286662-index-match-function-addcomment
Sheet: Test

Company Number | Department Number | Unit | Work Location Code | Supervisor | Employee Full Name | Base Role | New Hire? | Term? | Effective Date | Overtime | Normal Hours Worked
567 | 9895 | Accounting | 505 | Example Supervisor | Example Employee 4 | Accounting | No |  | 2009-12-05 00:00:00 | 1.5 | 60
567 | 9895 | Accounting | 505 | Example Supervisor | Example Employee 1 | Accounting | No |  | 2009-12-05 00:00:00 | 2.8 | 71
567 | 9895 | Accounting | 505 | Example Supervisor | Example Employee 2 | Accounting | No |  | 2009-12-05 00:00:00 |  | 80
567 | 9895 | Accounting | 505 | Example Supervisor | Example Employee 3 | Accounting | No |  | 2009-12-05 00:00:00 |  | 75
Spreadsheet: 205171d1357753773-search-for-a-string-in-an-entire-column-and-copy-a-range-of-cells-from-corresponding-row-sample
Sheet: Sheet1

[NAME] | good | new | 34523 | us
jade | bad | new | 2354 | uk
[NAME] | good | old | 2113 | aus
tom | good | old | 6442 | ger
billy | normal | new | 63434 | fra
bob | bad | new | 642342 | uae
Spreadsheet: 89336d1289137147-sort-using-formulas-book1
Sheet: Sheet1

Position | Team | Points | [NAME] | Won | Tied | Lost | Goals For | Goals Against | Goal Differential
1 | TeamA | 6 | 2 | 2 | 0 | 0 | 6 | 0 | 6
2 | TeamB | 6 | 2 | 2 | 0 | 0 | 7 | 2 | 5
3 | TeamC | 6 | 2 | 2 | 0 | 0 | 2 | 0 | 2
4 | TeamD | 6 | 2 | 2 | 0 | 0 | 5 | 1 | 4
5 | TeamE | 4 | 2 | 1 | 1 | 0 | 5 | 3 | 2
6 | TeamF | 4 | 2 | 1 | 1 | 0 | 2 | 0 | 2
7 | TeamG | 4 | 2 | 1 | 1 | 0 | 6 | 3 | 3
8 | TeamH | 3 | 2 | 1 | 0 | 1 | 4 | 5 | -1
9 | TeamI | 3 | 2 | 1 | 0 | 1 | 3 | 4 | -1
10 | TeamJ | 3 | 2 | 1 | 0 | 1 | 2 | 2 | 0
11 | TeamK | 3 | 2 | 1 | 0 | 1 | 2 | 3 | -1
12 | TeamL | 3 | 2 | 1 | 0 | 1 | 4 | 3 | 1
13 | TeamM | 3 | 2 | 1 | 0 | 1 | 3 | 3 | 0
14 | TeamN | 1 | 2 | 0 | 1 | 1 | 0 | 3 | -3
15 | TeamO | 1 | 2 | 0 | 1 | 1 | 1 | 3 | -2
16 | TeamP | 1 | 2 | 0 | 1 | 1 | 1 | 4 | -3
17 | [NAME] | 0 | 2 | 0 | 0 | 2 | 0 | 2 | -2
18 | TeamR | 0 | 2 | 0 | 0 | 2 | 1 | 3 | -2
19 | TeamS | 0 | 2 | 0 | 0 | 2 | 2 | 4 | -2
20 | TeamT | 0 | 2 | 0 | 0 | 2 | 1 | 9 | -8
Spreadsheet: 494426d1482370627-3-deep-countifs-lodd-we-remember
Sheet: Sheet1

 | Line of Duty Deaths: 137
 | 9/11 related illness: 2
 | Aircraft accident: 1
 | Animal related: 1
 | Assault: 2
 | Automobile accident: 23
 | Drowned: 2
 | Duty related illness: 1
 | Fall: 1
 | Gunfire: 62
 | Gunfire (Accidental): 2
 | Heart attack: 6
 | Motorcycle accident: 7
 | Stabbed: 1
 | Struck by train: 1
 | Struck by vehicle: 9
 | Vehicle pursuit: 4
 | Vehicular assault: 12
 | By Month
 | 178956970 days, 15:04:59.999000
 | 178956970 days, 14:10:00.001000
 | 178956970 days, 13:16:00.002000
 | 178956970 days, 12:02:00.996000
 | 178956970 days, 11:07:00.004000
 | 178956970 days, 10:10:01.001000
 | 178956970 days, 9:19:00.999000
 | 178956970 days, 8:10:00.001000
 | 178956970 days, 7:16:00.002000
 | 178956970 days, 6:17:00.003000
 | 178956970 days, 5:21:00.996000
 | 178956970 days, 4:04:00.999000
 | By State
 | Alabama: 1
 | Alaska: 1
 | Arizona: 2
 | Arkansas: 2
 | California: 11
 | Colorado: 4
 | Florida: 5
 | Georgia: 8
 | Illinois: 4
 | Indiana: 1
 | Iowa: 5
 | Kansas: 3
 | Louisiana: 9
 | Maryland: 3
 | Massachusetts: 2
 | Michigan: 6
 | Mississippi: 1
 | Missouri: 2
 | New Jersey: 2
 | New Mexico: 3
 | New York: 4
 | North Carolina: 2
 | North Dakota: 1
 | Ohio: 5
 | Oregon: 1
 | Pennsylvania: 3
 | Puerto Rico: 4
 | South Carolina: 1
 | Tennessee: 5
 | Texas: 19
 | Tribal Police: 1
 | U.S. Government: 6
 | Utah: 3
 | Virginia: 3
 | Washington: 1
 | West Virginia: 1
 | Wisconsin: 2
 | By [NAME]
 | Male: 132
 | Female: 5
 | Statistics
 | Average age: 41
 | Average tour of duty: 13 years, 3 months
 | [NAME] of Duty Deaths: 33
 | viewAccidental: 1
 | Asphyxiation: 1
 | Assault: 2
 | Automobile accident: 1
 | Drowned: 1
 | Gunfire: 10
 | Gunfire (Accidental): 2
 | Heat exhaustion: 11
 | Stabbed: 1
 | Struck by vehicle: 3
Spreadsheet: 693617d1599058502-calculating-total-which-includes-text-example-3
Sheet: Sheet1

Actual Total | Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | Group 6 | Group 7 | Group 8 |  |  |  |  |  |  |  |  | Total
8216.2 | 4.2 | 63 | 448 | 170 x 17 |  |  | 271 x 17 | 12 x 17
 |  |  |  |  |  |  |  |  | 4.2 | 63 | 448 | 2890 | 0 | 0 | 4607 | 204
Spreadsheet: 1ef50df9-fd95-4868-9812-e00d22eb4d07
Sheet: 14c

 | Swap Dealers/MSPs4 |  | Others
Product1 | Cleared2 | Uncleared | Cleared
Index Tranche | - | 186 | - | 100
Index | 5409 | 2570 | 5041 | 2552
OTHER* | 5 | 194 | 9 | 140
TOTAL | 5414 | 2950 | 5050 | 2792
*OTHER variable includes exotic credit products, swaptions, and total return swaps.
Spreadsheet: 41017d1239429522-solved-lost-which-formula-to-use-in-a-dynamic-data-retrival-excel-help
Sheet: Sheet1

 | 07:00:00 | 08:00:00 | 09:00:00 | 10:00:00 | 11:00:00 | 12:00:00 | 13:00:00 |  |  | To [NAME]: | 1
Task 1 | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | Sophie | Sophie |  |  | Name
Task 2 | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME]
Task 3 | Sophie | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME]
Task 4 | [NAME] | [NAME] | [NAME] | Sophie | Sophie | [NAME] | [NAME]
 |  |  |  |  |  |  |  | etc…
 |  |  |  |  |  |  |  |  |  | To match Task: | 2
 |  |  |  |  |  |  |  |  |  | Task 1
 |  |  |  |  |  |  |  |  |  | To match time: | 2
 |  |  |  |  |  |  |  |  |  | 07:00:00
Name
[NAME]
[NAME]
[NAME]
[NAME]
Sohpie
Spreadsheet: 122851d1317207241-excel-2007-using-indirect-formula-properly-stock-dummy
Sheet: Sales

Product | Code | Date | Price | Quantity | Product  Total |  | Sales Total |  |  | Stock Update
Cards | TF05 | 2011-09-25 00:00:00 | 1 | 1
Cards | DC52 | 2011-09-25 00:00:00 | 0.4 | 1
Cards | TF03 | 2011-09-25 00:00:00 | 4 | 1
Spreadsheet: 7fc4328f-a42d-483e-b55b-748893221663
Sheet: OUTPUT

WOODWORKING EMISSIONS CALCULATOR REVISION C JULY 2007 - OUTPUT SCREEN
 |  |  | Instructions:  Enter emission source / facility data on the "INPUT" tab/screen.  The air emission results and summary of input data are viewed / printed on the "OUTPUT" tab/screen.  The different tabs are on the bottom of this screen.
 |  |  | This spreadsheet is for your use only and should be used with caution. DENR does not guarantee the accuracy of the information contained.  This spreadsheet is subject to continual revision and updating.  It is your responsibility to be aware of the most current information available.  DENR is not responsible for errors or omissions that may be contained herein.
SOURCE / FACILITY / USER INPUT SUMMARY (FROM INPUT SCREEN)
COMPANY: |  |  |  |  |  |  |  |  |  | FACILITY ID NO.:
 |  |  |  |  |  |  |  |  |  | PERMIT NUMBER:
EMISSION SOURCE DESCRIPTION: |  |  |  |  |  |  |  |  |  | FACILITY CITY:
 |  |  |  |  |  |  |  |  |  | FACILITY COUNTY:
EMISSION SOURCE ID NO.: |  |  |  |  |  |  |  |  |  | WOODWASTE CHARACTERIZATION
SPREADSHEET PREPARED BY: |  |  |  |  |  |  |  |  |  | PLANING
TYPE OF WOOD: |  |  |  |  |  |  |  |  |  | SHAVING/CHIPPING
 |  |  |  |  |  |  |  |  |  | ROUGH SAWING
 |  |  |  |  |  |  |  |  |  | FINE SAWING
 |  |  |  |  |  |  |  |  |  | MILLING (& HOG)
 |  |  |  |  |  |  |  |  |  | MOLDING
 |  |  |  |  |  |  |  |  |  | SANDING
CRITERIA AIR POLLUTANT EMISSIONS INFORMATION - WOODWORKING
 |  |  |  |  |  | ACTUAL EMISSIONS |  | POTENTIAL EMSSIONS |  |  |  | CONTROL EFFICIENCY
 |  |  |  |  |  | (AFTER CONTROLS / LIMITS) |  | (BEFORE CONTROLS / LIMITS) |  | (AFTER CONTROLS / LIMITS)
AIR POLLUTANT EMITTED |  |  |  |  |  | lb/hr | tons/yr | lb/hr | tons/yr | lb/hr | tons/yr | %
PARTICULATE MATTER (PM)
PARTICULATE MATTER<10 MICRONS (PM10)
PARTICULATE MATTER<2.5 MICRONS (PM2.5)
Spreadsheet: louise_kitchen__23837__2QYTD Orig w notes
Sheet: HPL

HPL
Mid Year 2001
Originator |  | Commodity |  | Team |  | Total |  | %Total
Gottlob |  | Gas |  | HPL |  | 5749630
Riley |  | Gas |  | HPL |  | 1546776
Morris |  | Gas |  | HPL |  | 723434
[NAME] |  | Gas |  | HPL
Hall |  | Gas |  | HPL |  | 216719
Lamphier |  | Gas |  | HPL
Martinez |  | Gas |  | HPL |  | 112510
Snieder |  | Gas |  | HPL
[NAME] |  | Gas |  | HPL |  | 43010
[NAME] |  | Gas |  | HPL |  | 37419
[NAME] |  | Gas |  | HPL |  | 13663
Weissman |  | Gas |  | HPL |  | 12580
Lofton |  | Gas |  | HPL |  | 10149
Peyton |  | Gas |  | HPL |  | 2431
[NAME] |  | Gas |  | HPL |  | -2974
[NAME] |  | Gas |  | HPL |  | -42841
Papayoti |  | Gas |  | HPL
Total
Spreadsheet: 52b24530-76e9-4d81-8cf9-42e1ed65151e
Sheet: Sheet1

Form No. SE101 04/17/09 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | KANSAS STATE DEPARTMENT OF EDUCATION
FY 2014 Application for |  |  |  | Application Due Date: April 30, 2014
Special Education |  |  |  | Please provide one application for each child          for whom you are requesting funds.
Catastrophic State Aid
For services provided from July 1, 2013 to June 30, 2014 for:
Student ID (KSDE KIDS ID number) |  |  |  | Student Name (First)        (MI)          (Last)
Interlocal / USD Name |  | Int/USD # |  | Contact Person
Address |  |  |  | Address
City |  | Zip |  | City              Zip
Area Code - Telephone Number |  |  |  | Area Code - Telephone Number
1.  Describe the student's unique or severe physical, mental, social, emotional or educational characteristics that require additional costs.
2.  Justify the cost of the services/placement, as specified in all of the student’s IEPs in effect during the current school year, explaining why the student needed the services/placement. Enter in KAN_Service the student’s current up-to-date MIS record which lists all services provided, including the anticipated frequency, location, and duration of each service for those expenditures itemized on page 2 .
3.  If your district is contracting with another school district or private agency for provision of the services/ placement as specified in all IEPs addressing services in the current school year,  justify the cost of contracting with the other school district or private agency. List the name of all contracted agencies, hospitals, schools, mental heath centers etc.
Spreadsheet: 713617d1610827136-extract-values-in-column-c-and-column-e-into-column-g-based-on-values-in-column-f-extract-values_sample3_index_pull
Sheet: Sheet1

# | Names - I | Code | Names - II | Code - II | Names | Desired Outcome
1 | [NAME], Sahaeer | 30 | Aadigan, [NAME] | 80 | [NAME], Sahaeer
2 | [NAME], Cadmus | 80 | Cadle, Tbra | 65 | Aadigan, [NAME]
3 | Abrahamo, [NAME] | 56 | Cadmus, [NAME] | 80 | [NAME], Aadigan
4 | [NAME], Cadorette | 12 | [NAME], Tbrahame Jr. | 85 | Sharper-Averyer Jr., [NAME]
5 | Abrahim, Cadwallader | 25 | Cadoret, Abrahamo | 56 | [NAME], Sharper-Averyer Jr.
6 | Abrahon, Cady | 45 | Cadorette, [NAME] | 12
7 | Sharper-Averyer Jr., [NAME] | 105 | Cadwalader, Tbrahem | 50
8 | Sharper-Averyer, Jr., [NAME] | 20 | Cadwallader, Abrahim | 25
9 | Sharper-Averyer, [NAME] | 40 | Cady, Abrahon | 45
10 | [NAME], Aadigan | 80 | Sahaeer, [NAME] | 30
11 | Tbra, [NAME] | 65 | [NAME], Sharper-Averyer | 40
12 | Tbrahame Jr., [NAME] | 85 | [NAME], Sharper-Averyer Jr. | 105
13 | Tbrahem, [NAME] | 50 | [NAME], Sharper-Averyer Jr. | 20
Spreadsheet: 745286d1629831742-using-index-and-match-to-find-sum-sampledata
Sheet: Sheet1

County | # of building type | type |  | County | # of buildings
Lafayette | 985 | office-clerical |  | Lafayette | ?
Lafayette | 373 | warehouse |  | Franklin | ?
Lafayette | 974 | residential-attached |  | Liberty | ?
Lafayette | 839 | hotel
Lafayette | 936 | office-retail-low
Lafayette | 236 | agriculture
Lafayette | 966 | residential-detached
Franklin | 858 | residential-semidetached
Franklin | 827 | flex-space
Franklin | 465 | office-retail-high
Franklin | 165 | office-retail-mid
Franklin | 151 | nursing-retirement
Franklin | 563 | light-manufacturing
Franklin | 356 | office-clerical
Franklin | 383 | warehouse
Franklin | 524 | residential-attached
Franklin | 743 | hotel
Franklin | 238 | office-retail-low
Franklin | 531 | agriculture
Franklin | 8 | prison
Franklin | 385 | residential-detached
Liberty | 558 | flex-space
Liberty | 140 | nursing-retirement
Liberty | 72 | heavy-manufacturing
Liberty | 566 | light-manufacturing
Liberty | 461 | office-retail-low
Liberty | 479 | residential-semidetached
Liberty | 765 | office-clerical
Liberty | 165 | warehouse
Liberty | 68 | residential-attached
Liberty | 221 | hotel
Liberty | 12 | prison
Liberty | 291 | office-retail-mid
Liberty | 230 | agriculture
Liberty | 913 | residential-detached
Spreadsheet: 8803a7e3-f9a1-4fe8-a5ce-a72411033a65
Sheet: Sheet1

Battle ID | Date & Time | Side | Victory | Score | Opp. Score | Opp. ID | Opp. Name | Scenario ID | Front | Start Date | End Date | Name EN | Name FR
40289598 | 2014-03-01 01:23:38 | AXIS | 0 | 4 | 6 | 1240562 | Big Taz | 6150 | mediterranean | 1944-08-25 | 1944-08-25 | [NAME] | [NAME]
40289896 | 2014-03-01 01:45:14 | ALLIES | 0 | 4 | 6 | 1240562 | Big Taz | 6150 | mediterranean | 1944-08-25 | 1944-08-25 | [NAME] | [NAME]
40348995 | 2014-03-04 10:06:01 | ALLIES | 1 | 6 | 2 | 580443 | jomatuom | 6749 | western | 1944-06-06 | 1944-06-06 | [NAME] | [NAME]
40351705 | 2014-03-04 15:49:50 | ALLIES | 0 | 4 | 7 | 541608 | Exocet | 7668 | pacific | 1945-02-26 | 1945-03-06 | The Meat Grinder | Le Hachoir à viande
40352072 | 2014-03-04 16:31:16 | AXIS | 1 | 7 | 4 | 541608 | Exocet | 7668 | pacific | 1945-02-26 | 1945-03-06 | The Meat Grinder | Le Hachoir à viande
40386620 | 2014-03-06 20:16:33 | ALLIES | 0 | 2 | 5 | 198562 | Georgiev | 6148 | western | 1944-07-25 | 1944-07-26 | Operation Cobra | Opération Cobra
40386895 | 2014-03-06 20:37:46 | AXIS | 0 | 2 | 5 | 198562 | Georgiev | 6148 | western | 1944-07-25 | 1944-07-26 | Operation Cobra | Opération Cobra