tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 871072d1717510896-how-to-extract-data-from-sheet-if-row-is-marked-for-extraction-extract_data
Sheet: Source of data

 | Name | Duration | Unit | Value | Uncertainty | Value | Uncertainty | Extract
 | A | 68.9 | a
 |  |  |  | 5139 | 2 | 0.3 | 0.02
 |  |  |  | 5263.36 | 0.09 | 31.55 | 0.23 | x
 |  |  |  | 5320.12 | 0.14 | 68.15 | 0.23 | x
 | B | 159200 | a
 |  |  |  | 4074 | 0 | 1.44e-05 | 2.1e-06
 |  |  |  | 4309 | 2 | 0.0009
 |  |  |  | 4404 | 2 | 0.0003
 |  |  |  | 4411 | 2 | 0.0004
 |  |  |  | 4457 | 2 | 0.0028 |  | x
Spreadsheet: 76924d1277383247-separating-the-data-in-a-row-sample-codednames
Sheet: Sheet1

data | CODE | Name | Check
Z588EBSIBASHIS MONDAL | Z588EB | SIBASHIS MONDAL | Yes
O2U5CGAkshat Shah | O2U5CG | Akshat Shah
[NAME] | DUTARX | Akshat Shah
BN0FUEpremchand gupta | BN0FU | Epremchand gupta
[NAME] | RLX2N2 | [NAME]
[NAME] | [NAME] | AHENDRA PRATAP SINGH | Yes
[NAME] | [NAME] | AHENDRA PRATAP SINGH | Yes
MSEHHERavi A.S | MSEHHE | [NAME]
MSEHHERavi A.S | MSEHHE | [NAME]
MSEHHERavi A.S | MSEHHE | [NAME]
MSEHHERavi A.S | MSEHHE | [NAME]
MSEHHERavi A.S | MSEHHE | [NAME]
F3IHEDAjay Mathur | F3IHED | [NAME]
[NAME] | [NAME] | Ritika [NAME]
[NAME] | [NAME] | Ritika [NAME]
[NAME] | [NAME] | Ritika [NAME]
Spreadsheet: 776485d1649757727-how-to-copy-a-particular-cell-when-certain-conditions-are-met-test1
Sheet: Sheet1

AMT_NEW | NOTE_NEW |  |  | AMT_OLD | NOTE_OLD
1050 |  |  |  | 65 | cleared on 08.07.2021
109 |  |  |  | 540.5 | cleared on 13.04.2021
267 |  |  |  | 32 | cleared on 21.06.2020
65431 |  |  |  | 777.77 | cleared on 13.04.2021
548 |  |  |  | 898 | cleared on 10.11.2020
65460 |  |  |  | 636.3 | cleared on 18.03.2019
523 |  |  |  | 1800.2 | cleared on 23.12.2021
2225 |  |  |  | 1520.4 | cleared on 17.10.2021
41056 |  |  |  | 876.18 | cleared on 06.07.2019
365.3 |  |  |  | 20223.4 | cleared on 13.04.2021
17.2 |  |  |  | 15 | cleared on 13.04.2021
555 |  |  |  | 8910.1 | cleared on 13.04.2021
540.5 |  |  |  | 33.24 | cleared on 20.08.2018
636.5 |  |  |  | 773.8 | cleared on 15.12.2020
 | Since A14 has a match in Column E (cell E3), then I would like the adjacent cell (F3) to be copied to cell B14.
 | I need a formula in Column B that will search to see if there are any matches in Column A with Column E. If so, I would like the adjacent cell in Column F copied to [NAME]
Spreadsheet: 185235d1349594002-how-to-filter-the-names-from-a-list-of-names-without-duplicates-new-microsoft-excel-worksheet
Sheet: Sheet1

Date | Loan Number | Processor | Loan status | Critical errors | Non Critical errors |  |  |  | Processor | Total Loans completed | Fail count | Pass Count | No of Critical errors | No of Non critical errors
2012-10-02 00:00:00 | 1000307078 | [NAME] | Pass | No | No
2012-10-02 00:00:00 | 2345680888 | Venita Sinto | Fail | No | Yes
2012-10-02 00:00:00 | 5430301701 | [NAME] | Fail | Yes | No
2012-10-02 00:00:00 | 7874757583 | [NAME] | Fail | Yes | No
2012-10-02 00:00:00 | 5250301381 | [NAME] | Fail | No | Yes
2012-10-02 00:00:00 | 6729810910 | Renita Jorge | Fail | No | Yes
2012-10-02 00:00:00 | 5430301699 | [NAME] | Fail | No | Yes
2012-10-02 00:00:00 | 9837798120 | Renita Jorge | Fail | No | Yes
2012-10-02 00:00:00 | 5350303313 | [NAME] | Fail | Yes | Yes
2012-10-02 00:00:00 | 3872912009 | [NAME] | Fail | No | Yes
2012-10-02 00:00:00 | 5430301721 | [NAME] | Fail | Yes | Yes
2012-10-02 00:00:00 | 4628299018 | [NAME] | Fail | No | Yes
2012-10-02 00:00:00 | 1000307288 | [NAME] | Fail | No | Yes
2012-10-02 00:00:00 | 7228910911 | [NAME] | Pass | No | No
Spreadsheet: 752478d1634784860-reference-to-calculate-subtotal-each-8th-row-sample
Sheet: Sheet1

 | Date
 | 2018-04-02 00:00:00 | 1 |  | =IF(MOD(ROW()-1,8),C2,SUM(#REF!))
 |  | 2 |  | =IF(MOD(ROW()-1,8),C3,SUM(#REF!))
 |  | 3 |  | =IF(MOD(ROW()-1,8),C4,SUM(#REF!))
 |  | 4 |  | =IF(MOD(ROW()-1,8),C5,SUM(#REF!))
 |  | 5 |  | =IF(MOD(ROW()-1,8),C6,SUM(#REF!))
 |  | 6 |  | =IF(MOD(ROW()-1,8),C8,SUM(#REF!))
 |  | 7 |  | =IF(MOD(ROW()-1,8),C7,SUM(#REF!))
 |  |  |  | =IF(MOD(ROW()-1,8),C9,SUM(D2:D8))
 |  | 9 |  | =IF(MOD(ROW()-1,8),C10,SUM(#REF!))
 |  | 10 |  | =IF(MOD(ROW()-1,8),C11,SUM(#REF!))
 |  | 11 |  | =[NAME]!))
 |  | 12 |  | =IF(MOD(ROW()-1,8),C13,SUM(#REF!))
 |  | 13 |  | =IF(MOD(ROW()-1,8),C14,SUM(#REF!))
 |  | 14 |  | =IF(MOD(ROW()-1,8),C15,SUM(#REF!))
 |  | 15 |  | =IF(MOD(ROW()-1,8),C16,SUM(#REF!))
 |  |  |  | =IF(MOD(ROW()-1,8),C17,SUM(D2:D8))
 |  | 17 |  | etc.
 |  | 18
 |  | 19
 |  | 20
 |  | 21
 |  | 22
 |  | 23
Spreadsheet: 764262d1642669761-formula-calculation-with-multiple-conditions-sample-78-ans
Sheet: Sheet1

 |  |  |  | First 12 Days Interest Free |  |  |  |  |  | 0 | 0.1 | 0.13 | 0.16
 |  |  |  |  |  |  |  |  |  | 12 | 21 | 60
 |  |  | Amount | Days | Interest Days | 0.1 | 0.13 | 0.16
 |  |  | 2298100 | 73
 |  |  | 2298100 | 59
 |  |  | 2298100 | 20
 |  |  | 2298100 | 12
Spreadsheet: 732881d1621285557-need-help-parsing-data-problem1
Sheet: Sheet1

Category ID | Object.subs
[PHONE] | 6270.924 - Benefit Recovery
[PHONE] | 6210.005 - Bonuses
[PHONE] | 6210.008 - Contract  Service/Labor
[PHONE] | 6210.950 - Accruals - Salaries & Bene 6250.951 - Accruals - Pumping
[PHONE] | 6270.850 - Compression 6270.751 - Compression Fees
[PHONE] | 6215.120 - Compressor Parts 6230.231 - Compressors
[PHONE] | 6230.932 - Compressor/Cooler/VRU-S
[PHONE] | 6230.931 - Compressor/Cooler/VRU-V
[PHONE] | 6270.946 - Compressor Rental - BOPL 6230.930 - Compressor/cooler/VRU-B
[PHONE] | 6270.951 - Accrual - Power 6230.934 - Compression Op Lse Accru
[PHONE] | 6270.772 - AB & BC - Carbon Tax
[PHONE] | 6270.952 - Accrual - Fuel  6260.950 - Accruals - Electricity
[PHONE] | 6230.256 - Chemical Injection System
[PHONE] | 6270.864 - Chemical Methanol
[PHONE] | 6270.860 - Chemical Natural Gas Dehy 6270.702 - Chemicals
[PHONE] | 8270.862 - Chemical Natural Gas Swee
[PHONE] | 6270.954 - Accrual - Chemicals
[PHONE] | 6220.173 - Contract Service
[PHONE] | 6270.742  - Cathodic Protection
[PHONE] | 6215.952 - Accruals - Maintenance 6215.954 - Accruals - Repairs 6215.950 - Accruals - Repairs & Maint
[PHONE] | 6245.953 - Accruals - trucking
[PHONE] | 6271.744 - Contract Labor
[PHONE] | 6271.741 - Bottomhole Pumps
[PHONE] | 6271.692 - Coiled Tubing
[PHONE] | 6271.950 - Accruals - Workovers
[PHONE] | 6250.490 - Administration Fee
[PHONE] | 6250.952 - Accruals - Lease & Admin
[PHONE] | 6270.817 - Admin Fees
[PHONE] | 6265.950 - Accrual Account 6265.951 - Accruals - Overhead
[PHONE] | 6235.950 - Accruals - EH&S
[PHONE] | 6270.955 - Accruals - Non-Operated
[PHONE] | 6255.550 - Communications/Network 6255.544 - Communications-Telepho 6255.935 - Comm/Network/Teleph
[PHONE] | 6270.950 - Accrual
Spreadsheet: 815192d1674807076-compond-interest-inflation-estimate-increaseinjuly
Sheet: Sheet1

 |  | Annual Increase in July |  |  |  |  |  |  | Annual Budget
Country | Budget category | 2023 | 2024 | 2025 |  |  | Country | Cost Type | 2023 | 2023 | 2025 |  |  | Country | Cost Type | 2023 | 2023 | 2025
Sweden | A | 0.02 | 0.015 | 0.022 |  |  | Sweden | A | 10000 | 12000 | 14000 |  |  | Sweden | A
Sweden | B | 0.01 | 0.008 | 0.005 |  |  | Sweden | B | 5000 | 6000 | 7000 |  |  | Sweden | B
Italy | A | 0.015 | 0.013 | 0.017 |  |  | Italy | A | 7000 | 8000 | 9000 |  |  | Italy | A
Italy | B | 0.017 | 0.014 | 0.015 |  |  | Italy | B | 3000 | 5000 | 4000 |  |  | Italy | B
USA | A | 0.025 | 0.028 | 0.025 |  |  | USA | A | 20000 | 30000 | 25000 |  |  | USA | A
USA | B | 0.028 | 0.027 | 0.027 |  |  | USA | B | 18000 | 17000 | 22000 |  |  | USA | B
Argentina | A | 0.005 | 0.006 | 0.01 |  |  | Argentina | A | 9000 | 11000 | 10000 |  |  | Argentina | A
Argentina | B | 0.008 | 0.01 | 0.012 |  |  | Argentina | B | 7000 | 8000 | 8500 |  |  | Argentina | B
Spreadsheet: 788066d1658246538-count-of-cell-highlighting-over-multiple-columns-per-name-count-of-red-highlights-per-name
Sheet: Result

Name | Count of Red Highlighted Cells
Emma
Anna
Nina
Sara
[NAME]
Elena
[NAME]
Giulia
Stella
Anouk
[NAME]
[NAME]
Elin
Elina
Emily
[NAME]
[NAME]
Sofia
Chiara
[NAME]
Liv
[NAME]
Valentina
Yara
Alessia
Alexandra
Alina
Charlotte
Emilie
Eva
[NAME]
Isabelle
Lilly
Luisa
Mara
[NAME]
Selina
Tamara
Zoé
Setareh
[NAME]
Shangavi
Shanila
Shaniqua
[NAME]
Shannon
Shashvina
Shaymaa
[NAME]
Shaynah
[NAME]
Shejla
Shenuki
[NAME]
Shiloh
Amina
Andrina
Annabelle
Annika
Bracha
Aurelia
Ava
Carlotta
Céline
Chloé
[NAME]
Liya
Lorena
Luciana
[NAME]
[NAME]
Marina
[NAME]
[NAME]
[NAME]
[NAME]
[NAME]
Meret
Naila
Noa
[NAME]
Paulina
Rahel
[NAME]
[NAME]
Salomé
Sofía
Sofija
Valeria
Yasmin
Zoe
[NAME]
[NAME]
[NAME]
[NAME]
Alena
[NAME]
[NAME]
Amalia
[NAME]
Anika
Anisa
Anissa
[NAME]
Antonia
Ariane
[NAME]
Aura
[NAME]
Ayla
Belma
[NAME]
Catarina
Cecilia
Chaya
Chayenne
Colette
Defne
Dunja
Eileen
Eléni
[NAME]
Estelle
Esther
Fabienne
Fay
Irina
Jaël
Jelena
[NAME]
Line
[NAME]
Rebekka
Rhea
Riona
Rita
Vanja
Victoria
Viktoria
[NAME]
[NAME]
Yasmine
[NAME]
Aakavi
Aaliyah
Aileen
[NAME]
[NAME]
Aimy
Aisha
Aita
Aiyana
Alyssia
Amalia-Rose
Amanda
Amandine
Amane
Amani
Amaya
Amber
Ambra
Amelia
Amila
Artina
[NAME]
[NAME]
Ashmitha
Asude
[NAME]
Aulona
Aurélie
Avigail
Aviya
[NAME]
Axelin
Ayana
Ayleen
[NAME]
Aysu
Azel
Azra
Bavidha
[NAME]
Bejda
[NAME]
[NAME]
Berfin
Beverly
Beyzanur
Bianca
Blinera
[NAME]
Brittany
Bruna
Buna
Buratha
[NAME]
Camilla
Carina
Carlina
Carmel
Carmela
Caroline
[NAME]
Cassie
Caterina
Catharina
Cécile
Celeste
Cheimaa
Chloe
Chloë
Christina
[NAME]
Clare
Diola
Diona
[NAME]
Dominika
Dorela
Elda
Eldina
Elfie
Elida
Elif
Eline
Elischewa
Elise
Elisena
Elle
Elle-Morgane
Elli
Ellie
Ellis
Élodie
Eloisa
Estere
[NAME]
Eugenia
Eva-Luna
Evangeline
Eve
[NAME]
Evelyne
Evin
[NAME]
Eidi
Elá
Elainne
[NAME]
Haticenur
Haxhere
Helen
Helene
[NAME]
Hina
Hind
[NAME]
Iiris
Ilarja
Ilayla
Ivy
Jadanan
Jade
[NAME]
Jamelia
Jamelle
Jamila
[NAME]
Jornelly
Josefa
[NAME]
Josephina
[NAME]
Joséphine
Josra
Keira
[NAME]
Kemelin
[NAME]
Kerschma
Keysha
[NAME]
Kiana
[NAME]
Kishajini
Leena
[NAME]
[NAME]
[NAME]
Liina
Liis
Lilia
Liliana
Lilja
[NAME]
Lilo
[NAME]
[NAME]
[NAME]
Linnea
Linoa
Lira
Lirinesa
[NAME]
[NAME]
Loan
[NAME]
[NAME]
Lorin
[NAME]
Lorinda
[NAME]
Lorisa
Lotta
Louine
Lova
Luanda
Lucía
[NAME]
Luzia
Luzie
[NAME]
[NAME]
Lydia
Lyn
[NAME]
[NAME]
[NAME]
[NAME]
Magalie
Magdalena
Mahault
Mahima
Mahisa
Mahlet
Maibert
Maïmouna
Maira
[NAME]
Elvira
[NAME]
Emel
Emilija
Endry
Enea
Enni
Enya
Eriona
Eslem
Masa
Mathilde
Matilde
Nadja
Naemi
Naida
Naira
Naja
Nashota
Nastasia
Nastasja
Natalia
Nathalie
Naya
Nila
Niobe
Nirina
Nithusa
[NAME]
Nivetha
Noée
[NAME]
Noëlle
Perel
Perl
Semina
Senay
Senia
Serena
[NAME]
Shkurte
Shukri
Sidra
Spreadsheet: 18784acb-be4b-4697-9801-f0c1c1815fda
Sheet: Sheet1

Unnamed: 0 | SUN EXPOSURE FORM: | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12 | Unnamed: 13 | Unnamed: 14 | Unnamed: 15 | Unnamed: 16 | Unnamed: 17 | Unnamed: 18 | Unnamed: 19 | Unnamed: 20 | Unnamed: 21 | Unnamed: 22 | Unnamed: 23 | Unnamed: 24 | Unnamed: 25 | Unnamed: 26 | Unnamed: 27 | Unnamed: 28 | Unnamed: 29 | Unnamed: 30 | Unnamed: 31 | Unnamed: 32 | Unnamed: 33 | Unnamed: 34 | Unnamed: 35 | Unnamed: 36
 | Study ID #:  _______________________________Date:______________   [NAME]:  Spring 2010  /  Fall 2010 Think about your daily activities and your typical schedule: how much time did you                                                                                                                  spend in the sun during each time period and what were you wearing (see key)?
 | Date:
 | Day of the week:
 |  | Yesterday |  |  |  |  | 2 days ago |  |  |  |  | 3 days ago |  |  |  |  | 4 days ago |  |  |  |  | 5 days ago |  |  |  |  | 6 days ago |  |  |  |  | 7 days ago
7am-11am | Activity:
 | Minutes:
 | Clothes: | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E
 | Activity:
 | Minutes:
 | Clothes: | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E
11am-3pm | Activity:
 | Minutes:
 | Clothes: | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E
 | Activity:
 | Minutes:
 | Clothes: | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E
3pm-7pm | Activity:
 | Minutes:
 | Clothes: | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E
 | Activity:
 | Minutes:
 | Clothes: | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E
 | Sunscreen?
 | Where? (circle) | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E | A | B | C | D | E
 | SPF?
 | Location (if other than SLO)
Spreadsheet: 92191d1291833560-excel-2007-pull-data-from-multiple-sheet-example
Sheet: 5

Qualification Tensile Inspection
Supplier | ABC
 | ([NAME]) | (in)
[NAME]: | 8
Length: | 200
 |  |  |  | Date Tested: | 2010-11-08 00:00:00
 | Tested at Location: | ABC |  | Tested by:
Cavity | Tensile Strength (N) | [NAME] (lbf) | Insertion Force (N) | Moisture | Comments
1 | 259 |  |  | 2.7
2 | 311 |  |  | 2.3
3 | 332 |  |  | 2
4 | 323
5 | 288
6 | 272
7 | 378
8 | 229
9 | 322
10 | 421
11 | 339
12 | 366
13 | 374
14 | 265
15 | 315
16 | 326
17 | 338
18 | 302
19 | 331
20 | 300
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
Average |  |  | Average
Min |  |  | Min
[NAME] |  |  | [NAME]
[NAME] |  |  | [NAME]
Spreadsheet: 120996d1315748536-timesheet-and-subtracting-constants-hours_temp-1-
Sheet: Sheet1

 |  |  | SUNDAY |  | MONDAY |  | TUESDAY |  | WEDNESDAY |  | THURSDAY |  | FRIDAY |  | SATURDAY |  | TOTAL | CARRIED
 | P | WK | START | FINISH | START | FINISH | START | FINISH | START | FINISH | START | FINISH | START | FINISH | START | FINISH
 | 4 | 14 | 09:05:00 | 17:50 | 09:00:00 | 17:45:00 | 08:45:00 | 18:00:00 | 00:00:00 | 00:00:00 | 08:45:00 | 17:15:00 | 07:45:00 | 14:15:00 | 00:00:00 | 00:00:00
HRS PAID
 |  | 15 | 00:00:00 | 00:00:00 | 07:45:00 | 16:15:00 | 07:15:00 | 16:15:00 | 07:45:00 | 13:45:00 | 00:00:00 | 00:00:00 | 07:45:00 | 17:15:00 | 08:45:00 | 17:15:00
HRS PAID
 |  | 16 | 09:00:00 | 17:00:00 | 07:45:00 | 16:15:00 | 00:00:00 | 00:00:00 | 07:45:00 | 15:15:00 | 07:45:00 | 16:15:00 | 07:45:00 | 16:30:00 | 00:00:00 | 00:00:00
HRS PAID
 | 5 | 17 | 00:00:00 | 00:00:00 | 07:15:00 | 16:00:00 | 07:15:00 | 15:00:00 | 08:30:00 | 17:00:00 | 08:30:00 | 17:00:00 | 00:00:00 | 00:00:00 | 09:00:00 | 17:15:00
HRS PAID
 |  | 18 | 08:30:00 | 16:15:00 | 00:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 07:45:00 | 16:15:00 | 00:00:00 | 00:00:00
HRS PAID
 |  | 19
HRS PAID
 |  | 20
HRS PAID
Spreadsheet: 142095d1329651298-get-rid-of-value-book1
Sheet: Sheet1

Segmentation no | Family code | Product code
05|05
30
[NAME]
80|A10
36|36
36|36
54
54
52|A18
52
52
Spreadsheet: 86344d1286350043-excel-2007-return-team-name-via-max-in-range-maxpointsteam
Sheet: Sheet1

Week | [NAME] | BROWNSTOWN |  | [NAME] | Team |  | [NAME] | Week | Team
1 | 190 | 142 |  | 190 | [NAME] |  | 238 | 4 | BROWNSTOWN
2 | 229 | 209 |  | 229 | [NAME]
3 | 218 | 184 |  | 218 | [NAME]
4 | 225 | 238 |  | 238 | BROWNSTOWN
5 | 0 | 0 |  | 0
6 | 0 | 0 |  | 0
7 | 0 | 0 |  | 0
8 | 0 | 0 |  | 0
9 | 0 | 0 |  | 0
10 | 0 | 0 |  | 0
11 | 0 | 0 |  | 0
12 | 0 | 0 |  | 0
13 | 0 | 0 |  | 0
14 | 0 | 0 |  | 0
15 | 0 | 0 |  | 0
16 | 0 | 0 |  | 0
17 | 0 | 0 |  | 0
 | 862 | 773
Spreadsheet: 45070d1243863560-using-boolean-to-compile-knowledge-flags
Sheet: Asia & The Pacific

Country | Sent | Received | Location | Contact | Notes
Australia
Cambodia
China
Fiji
Hong Kong (1)
Hong Kong (2)
India
Indonesia
Japan
Korea
Malaysia
Myanmar
New Zealand
Pakistan (1)
Pakistan (2)
Philippines
Singapore
[NAME]
Thailand
Vietnam
Spreadsheet: 680549d1591085047-formula-to-calculate-minutes-with-conditions-of-data-from-another-sheet-time-different
Sheet: Time Diff

Sign On & Depart |  | Arrival & Start |  | Start & Depart
convert hours and minutes to round up to the nearest minute
calculate what is |  | delay time | on time | early time
Spreadsheet: 122014d1316551995-indirect-erep_1gb-tigrovi_bkp
Sheet: KrofNiCa13

Ambient on/off
eRepublik – Online Strategy Game
Sep20 Day 1,400 of the New World
Home
My Land
Wars
Market
Marketplace
Job market
Monetary market
Companies for sale
Community
World Map
News
My Party
Elections
Military Unit
Country administration
Invite friends
Badges
Get Gold & Extras
[NAME]
[NAME]
27
60
Eat food
Gold2.24 Gold
BAM411 BAM
HRK1625 HRK
Missions
X
You don't have enough Gold to complete the task Get Gold
Completed!
2011-10-03 00:00:00
2011-10-03 00:00:00
2011-10-03 00:00:00
Reward
Ok
Congratulations, your rank is now
Rank Bonus on attack +5%
Energy bar
Ok
Strength
Experience points
Health
Rank points
Gold
Energy bar
[NAME]
Free Lands
Scope
Bazooka
Weapons
Weapons
Weapons
Food units
Food units
Food units
Grain Farms
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Experience level: 27
Experience points: 10027
Next level at: 15000
• Working, training and fighting decreases health
[NAME], health boxes, food, hospitals restore health.
This action restores as much health as possible by eating Food from your Storage.
You can recover up to: 450 / 450 Health
100 more recoverable Health in: 00:59:59
Food limit reached
You have reached your food consumption limit.
You can recover 100 health every hour.
100 more recoverable Health in: [NAME]
Buy health kit Close
KrofNiCa13
offlineKrofNiCa13
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Experience level 29
22,467 / 25,000
Location: PolandPoland, Mazovia Citizenship: CroatiaCroatia
eRepublik birthday
Oct 13, 2010
National rank: 442
Congress Member
[NAME] eHrvatskeSavez Komunista eHrvatske Far-left Wing
Member
HV 1.GB Tigrovi HV 1.GB Tigrovi
No media activity
Friends(522)
Mata_pavin
Dino_zippo
mlatonja
ivansplit
che-krtica
dmitar01
iqxy55
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Voluhare
view all
Land of KrofNiCa13: 22 buildings
Visit
Achievements
hard worker
11
Hard Worker
Worked 30 days in a row
congressman
1
Congress Member
Won the Congress elections
president
Country President
Win the Presidential elections
media mogul
Media Mogul
Reach 1000 subscribers to your newspaper
battle hero
1
Battle Hero
Reach the highest war influence in a battle
campaign hero
Campaign hero
Reach the highest war influence in a campaign
resistance hero
2
Resistance Hero
Started a resistance war and liberated 2 regions.
super soldier
13
Super Soldier
Advance 250 strength points
society builder
Society Builder
Invite 10 people to eRepublik and help them reach level 10
mercenary
Mercenary
Defeat 25 enemies for 50 different countries
Coming soon
Coming soon
Military Skills
Strength
3,474.65
Super soldier:3,474 / 3,500
Rank
Field Marshal **Field Marshal **
Rank points:
2,108,900 / 2,185,000
Forum   |   [NAME]   |   Blog   |   Press   |   Contact   |   [NAME]   |   Terms of Service   |   Privacy  |   Affiliates  |   [NAME]
Follow us: FacebookFacebook TwitterTwitter
Copyright © 2007-2011 eRepublik
Spreadsheet: 823742d1680118586-index-match-across-multiple-sheets-w-multi-row-answer-indexmatch-multi-row2
Sheet: servers

NAME | IP Address | AET | PORT | TYPE | Serv.#
Server Five | 10.10.42.10 | SERVER_NUM_FIVE | 1042 | MKT | 5
Server Six | 10.10.42.11 | SERVER_NUM_SIX | 4000 | MKT | 6
Server Seven | 10.10.42.10 | SERVER_NUM_SEVEN | 1033 | MKT | 7
Server Eight | 10.10.42.11 | SERVER_NUM_EIGHT | 4000 | MKT | 8
Server Nine | 10.10.42.10 | SERVER_NUM_NINE | 1031 | MKT | 9
Server Ten | 10.10.42.11 | SERVER_NUM_TEN | 4000 | MKT | 10
Server Eleven | 10.10.42.10 | SERVER_NUM_ELEVEN | 1030 | MKT | 11
Server Twelve | 10.10.42.11 | SERVER_NUM_TWELVE | 4000 | MKT | 12
Server Thriteen | 10.10.42.10 | SERVER_NUM_3TEEN | 1032 | MKT | 13
Server Fourteen | 10.10.42.11 | SERVER_NUM_4TEEN | 4000 | MKT | 14
Server Fifteen | 10.10.42.10 | SERVER_NUM_5TEEN | 1051 | MKT | 15
Server Sixteen | 10.10.42.11 | SERVER_NUM_6TEEN | 4000 | MKT | 16
Server Seventeen | 10.10.42.10 | SERVER_NUM_7TEEN | 1050 | MKT | 17
Server Eighteen | 10.10.42.11 | SERVER_NUM_8TEEN | 4000 | MKT | 18
Server Nineteen | 10.10.42.10 | SERVER_NUM_9TEEN | 1035 | MKT | 19
Server Twenty | 10.10.42.11 | SERVER_NUM_TWENTY | 4000 | MKT | 20
Server Twentyone | 10.10.42.10 | SERVER_NUM_2ONE | 1036 | MKT | 21
[NAME] | 10.10.42.11 | SERVER_NUM_2TWO | 4000 | MKT | 22
[NAME] | 10.10.42.10 | SERVER_NUM_2THREE | 1037 | MKT | 23
Which Person?
Person18
[NAME] settomgs:
NAME | IP Address | AET | PORT
Server Five | 10.10.42.10 | SERVER_NUM_FIVE | 1042
Server Six | 10.10.42.11 | SERVER_NUM_SIX | 4000
Server Seven | 10.10.42.10 | SERVER_NUM_SEVEN | 1033
Server Eight | 10.10.42.11 | SERVER_NUM_EIGHT | 4000
Server Nine | 10.10.42.10 | SERVER_NUM_NINE | 1031
Server Ten | 10.10.42.11 | SERVER_NUM_TEN | 4000
Server Eleven | 10.10.42.10 | SERVER_NUM_ELEVEN | 1030
Server Twelve | 10.10.42.11 | SERVER_NUM_TWELVE | 4000
Server Twenty | 10.10.42.11 | SERVER_NUM_TWENTY | 4000
Server Twentyone | 10.10.42.10 | SERVER_NUM_2ONE | 1036
[NAME] | 10.10.42.11 | SERVER_NUM_2TWO | 4000
Spreadsheet: 3f363f16-3687-4c5b-9aea-19271c65881c
Sheet: Table S1 Primers

 |  |  |  |  | Forward |  | Reverse
 |  |  | quantitative PCR
 |  |  | Glossina morsitans
 |  |  | 28s rRNA |  | GTGATATAATGTGCCCAGTG |  | TGGTATCTGCATGAGTTGTT
 |  |  | Milk gland protein 1 |  | CTGGATCTTGACCCGTGAAC |  | GGGGAAGTGATGTTCCTTGA
 |  |  | Milk gland protein 2 |  | ACCTATGATGTTACTGAATACTT |  | TTGAGCGAATTGTCTGTAG
 |  |  | Milk gland protein 3 |  | ACCTACTTGTCCAATGTT |  | GTTACGGCGATGTCATAT
 |  |  | Milk gland protein 4 |  | AGAGCCATCAAGGAACAC |  | AGCGAATTGTCTGTAGCA
 |  |  | Milk gland protein 5 |  | CTACCTTTGGCAGATTTG |  | GCTTAGAATTGACGAACAT
 |  |  | Milk gland protein 6 |  | GACAAGGTGTTCACTAAG |  | CCAAGAGTAGGGAAGAAT
 |  |  | Milk gland protein 7 |  | AGAAATGTATGCCCTGAA |  | TGAGTGTAGACATTGTAGAA
 |  |  | Milk gland protein 8 |  | CGTTCTTGAGGAAGTCATC |  | ATCATTGGGAAT CATTTCGTAGA
 |  |  | Milk gland protein 9 |  | GTTGCTGGCGATTATACC |  | TCAAAGACATCAGTGGAGTA
 |  |  | Milk gland protein 10 |  | CGTAACAAGTCCTATAACATCTA |  | ATGGCGTATTCGTAGTTG
 |  |  | Acid sphingomyelinase 1 |  | AAGACCATGAAGCAGAAT |  | CCGTTGTTGTTGTTAGAG
 |  |  | Transferrin |  | CATCCACGCTAACAACTA |  | AAGTAGGTTCCACAACAG
 |  |  | Peptidoglycan recognition protein-lb |  | [NAME] |  | GAACGATCACAAACGCAGAA
 |  |  | Cu/Zn superoxide dismutase |  | TGAGAAAACCTCGAAAATTG |  | ATGCCAGTGAGATAAACTCG
 |  |  | Catalase |  | GATTCAGGTGGGTAAGTTGA |  | ACAGACGTCCCTGTAACATC
 |  |  | Tubulin |  | CCATTCCCACGTCTTCACTT |  | GACCATGACGTGGATCACAG
 |  |  | Glossina fuscipes
 |  |  | Milk gland protein 2 |  | CTCAATTCTTCCCCAGCA |  | GAATTCTTTGACGGCAGT
 |  |  | Milk gland protein 5 |  | GCTGCTGTTGACCAATTT |  | GGCCCAGGTCTTGTAAAA
 |  |  | NADH subunit 2 |  | TTCAACACTTTTAAGGTTTCTCA |  | AAGGGAGGAAGACCTCCTAA
 |  |  | Glossina pallidipes
 |  |  | Milk gland protein 3 |  | CTTCCCCCATGACTTTAT |  | ACCGAAGACAGCAAAAGA
 |  |  | Milk gland protein 4 |  | TACTTCGGCCGCTTCTAC |  | AGTTTGGACTTCGTTGGG
 |  |  | 28S |  | AGGCCATTTACCCAAAGA |  | AGTTTACCACCCACTTAG
 |  |  | Sarcophaga crassipalpis
 |  |  | Milk gland protein-like protein |  | TATGTGGATGAGGAACATGA |  | GTAAAGGAGGGCGTTAATTT
 |  |  | Rp49 |  | AAGAAGCGCACCAAGCACTTCATC |  | ACTTCTTGAAGCCAGTTGGGAGCA
 |  |  | siRNA
 |  |  | GFP |  | CUUGACUUCAGCACGUGUCUUGUAGUU |  | CUACAAGACACGUGCUGAAGUCAAG
 |  |  | Drop |  | GAAGCUACACUAAAGUUACUUAACCUU |  | GGUUAAGUAACUUUAGUGUAGCUTC
 |  |  | Ladybird late
 |  |  | Nubbin |  | AGUUAAAUUUAAAGCUGCCUCCUCCAC |  | TCAAUUUAAAUUUCGACGGAGGAGG
 |  |  | MGP5 |  | UAGGGAAGAAGCAACAGCGUUGUCCUU |  | GGACAACGCUGUUGCUUCUUCCCTA
 |  |  | MGP7 |  | ACAUCAUAAGAUUUCAAGAUUUCGGUU |  | CCGAAAUCUUGAAAUCUUAUGAUGT
 |  |  | MGP8 |  | UGUUCCAAAUAUUUGCUUAAGGUGGUU |  | CCACCUUAAGCAAAUAUUUGGAACA
 |  |  | MGP9 |  | UGAAGAAUUCAGGAGUGAACAAUUGUU |  | CAAUUGUUCACUCCUGAAUUCUUCA
 |  |  | Teashirt |  | UGAACUAGCCACACCCUUGUUCUGCCA |  | GCAGAACAAGGGUGUGGCUAGUUCA