tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: a5265094-148d-4962-a196-d02a66d4ec3a
Sheet: Sheet1

Table S1. Summary of the read mapping and coverage using [NAME] for all samples included in this study.
Strain | Sal I | Belem | C08 | C127 | C15 | M08 | M19 | M15 | Brazil | Mauritania I | North Korea | India VII
# Read Pairs | 215743944 | 81446663 | 231291984 | 211061945 | 79414201 | 215643747 | 85703544 | 76891834 | 133804387 | 130982283 | 132927529 | 95900222
Mapped on P. vivax (Sal I) | 3016831 | 65608265 | 37780042 | 107565481 | 15436467 | 48635340 | 44691688 | 45493468 | 34418484 | 59153748 | 42242940 | 10961475
% mapped on P. vivax
Uniquely mapped on P. vivax | 2956886 | 63743582 | 37060560 | 105516889 | 15112744 | 47672895 | 43697609 | 44450256 | 33772104 | 58197023 | 41486970 | 10741576
% unique
After removal of duplic. read pairs | 2926207 | 61411645 | 14773862 | 61052160 | 11022204 | 32737022 | 17788686 | 11154838
% non-duplicated
Average nucleotide coverage | 21 | 454 | 110 | 437 | 78 | 235 | 128 | 82 | 197 | 340 | 245 | 32
% genome covered >= 20 | 0.5406 | 0.966 | 0.9472 | 0.9829 | 0.9593 | 0.9651 | 0.9797 | 0.9656 | 0.9758 | 0.9834 | 0.9814 | 0.7536
Spreadsheet: 878765d1726047947-sort-columns-with-adjecent-cells-xfarberka-test2-15-spill-365-aligw
Sheet: Sheet1

 | SOURCE
A | Internet & Technology Law | 0.25 | Corporate Law | 15% | Finance Law | 15% | Intellectual Property Law | 15% | Labor & Employment Law | 15% | Tax Law | 15%
B | Litigation Support Services | 30% | Civil Rights Law | 20% | Labor & Employment Law | 20% | Real Estate Law | 20% | Corporate Law | 20%
C | Accounting | 10% | Corporate Law | 10% | Finance & Accounting Outsourcing (FAO) | 10% | HR Consulting | 10% | HR Outsourcing | 10% | Tax Law | 10% | Back Office Outsourcing | 5% | Bankruptcy Law | 5% | Compliance Consulting | 5% | Document Digitization & Management | 5% | Finance Law | 5% | Intellectual Property Law | 5% | Labor & Employment Law | 5% | Real Estate Law | 5%
 | TARGET
A | Accounting |  | Back Office Outsourcing |  | Bankruptcy Law |  | Compliance Consulting |  | Corporate Law | 15% | Civil Rights Law |  | Document Digitization & Management |  | Finance Law | 15% | Finance & Accounting Outsourcing (FAO) |  | HR Consulting |  | HR Outsourcing |  | Intellectual Property Law | 15% | Internet & Technology Law | 15% | Labor & Employment Law | 15% | Litigation Support Services |  | Real Estate Law |  | Tax Law | 15%
B | Accounting |  | Back Office Outsourcing |  | Bankruptcy Law |  | Compliance Consulting |  | Corporate Law | 20% | Civil Rights Law | 20% | Document Digitization & Management |  | Finance Law |  | Finance & Accounting Outsourcing (FAO) |  | HR Consulting |  | HR Outsourcing |  | Intellectual Property Law |  | Internet & Technology Law |  | Labor & Employment Law | 20% | Litigation Support Services | 30% | Real Estate Law | 20% | Tax Law
C | Accounting | 10% | Back Office Outsourcing | 5% | Bankruptcy Law | 5% | Compliance Consulting | 5% | Corporate Law | 10% | Civil Rights Law |  | Document Digitization & Management | 5% | Finance Law | 5% | Finance & Accounting Outsourcing (FAO) | 10% | HR Consulting | 10% | HR Outsourcing | 10% | Intellectual Property Law | 5% | Internet & Technology Law |  | Labor & Employment Law | 5% | Litigation Support Services |  | Real Estate Law | 5% | Tax Law | 0.1
Spreadsheet: 826376d1681977167-help-with-a-cumulative-percentage-formula-example-8
Sheet: Sheet1

English Literature grade breakdown % and Count by Gender |  |  |  |  |  | English Literature grade breakdown % and Count by PP |  |  |  |  |  | English Literature grade breakdown % and Count by SEND
Grade | Female | Female | Male | Male |  | Grade | Non PP | Non PP | PP | PP |  | Grade | EHCP | EHCP | Non SEND | Non SEND | SEND | SEND
1 | 0.5 | 5 | 0.5 | 5 |  | 1 | 0.2 | 2 | 0.8 | 8 |  | 1 | 0 |  | 0.6 | 6 | 0.4 | 4
1- | 0 |  | 1 | 3 |  | 1- | 0 |  | 1 | 3 |  | 1- | 0 |  | 0.6666666666666666 | 2 | 0.3333333333333333 | 1
1+ | 0.2857142857142857 | 4 | 0.7142857142857143 | 10 |  | 1+ | 0.3571428571428572 | 5 | 0.6428571428571429 | 9 |  | 1+ | 0 |  | 0.7857142857142857 | 11 | 0.2142857142857143 | 3
2 | 0.45 | 9 | 0.55 | 11 |  | 2 | 0.4 | 8 | 0.6 | 12 |  | 2 | 0 |  | 0.9 | 18 | 0.1 | 2
2- | 0.5 | 4 | 0.5 | 4 |  | 2- | 0.375 | 3 | 0.625 | 5 |  | 2- | 0 |  | 0.875 | 7 | 0.125 | 1
2+ | 0.2941176470588235 | 10 | 0.7058823529411765 | 24 |  | 2+ | 0.3823529411764706 | 13 | 0.6176470588235294 | 21 |  | 2+ | 0.02941176470588235 | 1 | 0.8529411764705882 | 29 | 0.1176470588235294 | 4
3 | 0.5652173913043478 | 13 | 0.4347826086956522 | 10 |  | 3 | 0.4347826086956522 | 10 | 0.5652173913043478 | 13 |  | 3 | 0 |  | 1 | 23 | 0
3- | 0.3571428571428572 | 5 | 0.6428571428571429 | 9 |  | 3- | 0.5714285714285714 | 8 | 0.4285714285714285 | 6 |  | 3- | 0 |  | 1 | 14 | 0
3+ | 0.6 | 9 | 0.4 | 6 |  | 3+ | 0.2666666666666667 | 4 | 0.7333333333333333 | 11 |  | 3+ | 0 |  | 0.9333333333333333 | 14 | 0.06666666666666667 | 1
4 | 0.5 | 4 | 0.5 | 4 |  | 4 | 0.625 | 5 | 0.375 | 3 |  | 4 | 0 |  | 1 | 8 | 0
4- | 1 | 1 | 0 |  |  | 4- | 0 |  | 1 | 1 |  | 4- | 0 |  | 1 | 1 | 0
4+ | 0.8 | 4 | 0.2 | 1 |  | 4+ | 0.6 | 3 | 0.4 | 2 |  | 4+ | 0 |  | 0.8 | 4 | 0.2 | 1
5 | 0.5 | 1 | 0.5 | 1 |  | 5 | 0.5 | 1 | 0.5 | 1 |  | 5 | 0 |  | 1 | 2 | 0
5- | 0 |  | 1 | 1 |  | 5- | 0 |  | 1 | 1 |  | 5- | 0 |  | 1 | 1 | 0
6 | 1 | 1 | 0 |  |  | 6 | 1 | 1 | 0 |  |  | 6 | 0 |  | 1 | 1 | 0
U | 0.5652173913043478 | 13 | 0.4347826086956522 | 10 |  | U | 0.1739130434782609 | 4 | 0.8260869565217391 | 19 |  | U | 0.1739130434782609 | 4 | 0.3478260869565217 | 8 | 0.4782608695652174 | 11
Total | 0.456043956043956 | 83 | 0.5439560439560439 | 99 |  | Total | 0.3681318681318682 | 67 | 0.6318681318681318 | 115 |  | Total | 0.02747252747252747 | 5 | 0.8186813186813187 | 149 | 0.1538461538461539 | 28
[NAME] | Female | Male | All |  |  | [NAME] | Non PP | PP | All |  |  | [NAME] | EHCP | Non SEND | SEND | All
9-4 % | 0.133 | 0.071 | 0.099 |  |  | 9-4 % | 0.149 | 0.07 | 0.099 |  |  | 9-4 % | 0 | 0.113 | 0.038 | 0.099
9-5% | 0.024 | 0.02 | 0.022 |  |  | 9-5% | 0.03 | 0.018 | 0.022 |  |  | 9-5% | 0 | 0.027 | 0 | 0.022
9-7% | 0 | 0 | 0 |  |  | 9-7% | 0 | 0 | 0 |  |  | 9-7% | 0 | 0 | 0 | 0
Spreadsheet: 805328d1668622455-excel-compare-data-and-rank-in-order-book1_upd-1-
Sheet: Consolidated

 | Value | Manager |  |  |  |  |  |  |  |  |  | Manager 1 |  |  |  |  |  |  |  |  |  | Manager 1
No of cases handled (highest) |  |  |  |  |  |  |  |  |  |  |  | Manager 2 |  |  |  |  |  |  |  |  |  | Manager 2
No of case reviewed (highest) |  |  |  |  |  |  |  |  |  |  |  | Manager 3 |  |  |  |  |  |  |  |  |  | Manager 3
Late cases (lowest)
HIPAA Errors (lowest)
No of cases handled
 |  | 25
 |  | 15
No of case reviewed
 |  | 124
 |  | 100
Late cases
 |  | 15
 |  | 34
HIPAA Errors
 |  | 16
 |  | 56
Spreadsheet: 825167d1681154219-exclude-data-in-hidden-columns-max-weekly-history-jv
Sheet: Max Weekly History

 | Hide | Hide |  |  |  | Hide |  |  |  | Subtoal is not working
 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | Average | [NAME]
January |  | 13233 | 18452 | 19680 | 17116 | 20057 | 14686 | 22123 | 21873
February |  | 14313 | 18349 | 23201 | 18748 | 25222 | 16415 | 25552 | 21405
March |  |  | 2985 | 18239 | 21288 | 23740 | 19442 | 25408 | 19795
April |  | 9654 | 20663 | 21242 | 21606 |  | 17455 | 29419 | 18390
May |  |  | 9352 | 19960 | 22784 |  | 19807 | 21765
June |  | 13424 | 23641 | 19838 | 18177 |  | 20825 | 26414
July |  | 2842 | 24023 | 18444 | 19010 |  | 22693 | 23504
August |  | 16090 | 21851 | 18371 | 21742 | 15875 | 23989 | 26046
September |  |  | 23335 | 22760 | 22080 | 16808 | 24765 | 22784
October | 4628 | 16987 | 20848 | 18539 | 21922 | 14112 | 6828 | 26938
November | 11504 | 16059 | 22338 | 16328 | 25503 | 15988 | 7044 | 24686
December |  |  | 19893 | 17895 | 25880 | 14490 | 7595 | 29300
 |  | I would like to hide Rows 2015, 2016 and 2020 to see the new Average and [NAME] values in colums K and L respectively.
Spreadsheet: 216091d1361577591-checkboxes-keep-printing-even-though-ive-set-them-to-not-srf_forms
Sheet: ADMIN

True | x | <--Tax
True | x
True | x
True
True
True
True
True
True
True
True
True
True
True
True
False |  | <-- Gratuity
True
True
True
True
True
True
True
True
True
True
True
True
True
True
Spreadsheet: 874762d1721287935-help-with-forecasting-formula-based-on-first-month-of-sale-formula-help
Sheet: Sheet1

TEST |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | MONTHLY SPLIT
STATUS | Group | LEAVE BLANK | DA Code | Description | Family | LEAVE BLANK | LEAVE BLANK | COGS Unit Cost | Unit Price (Wholesale) | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | [NAME] | 12 month FCST Units | 2025  Total Forecast Value  (NOD) | Launch Month | [NAME] (Month 1 = 40%, Month 2 = 40%, Month 3 = 20%) | Forward Weighting | 2025-01-01 00:00:00 | 2025-02-01 00:00:00 | 2025-03-01 00:00:00 | 2025-04-01 00:00:00 | 2025-05-01 00:00:00 | 2025-06-01 00:00:00 | 2025-07-01 00:00:00 | 2025-08-01 00:00:00 | 2025-09-01 00:00:00 | 2025-10-01 00:00:00 | 2025-11-01 00:00:00 | 2025-12-01 00:00:00
 | CATEGORY 1 |  |  | Test Product 1 | TEST FAMILY 1 |  |  | 1 | 1000 |  |  |  |  |  |  |  |  |  |  |  |  | 2000 |  | 2025-02-01 00:00:00 | 0.6 |  | 0
 | CATEGORY 2 |  |  | Test Product 2 | TEST FAMILY 2 |  |  | 1 | 1000 |  |  |  |  |  |  |  |  |  |  |  |  | 2000 |  | 2025-02-01 00:00:00 | 0.6
 | CATEGORY 3 |  |  | Test Product 3 | TEST FAMILY 3 |  |  | 1 | 1 |  |  |  |  |  |  |  |  |  |  |  |  | 1
 | CATEGORY 4 |  |  | Test Product 4 | TEST FAMILY 4 |  |  | 1 | 1 |  |  |  |  |  |  |  |  |  |  |  |  | 1
 | CATEGORY 5 |  |  | Test Product 5 | TEST FAMILY 5 |  |  | 1 | 1 |  |  |  |  |  |  |  |  |  |  |  |  | 1
 | CATEGORY 6 |  |  | Test Product 6 | TEST FAMILY 6 |  |  | 1 | 1 |  |  |  |  |  |  |  |  |  |  |  |  | 1
 | CATEGORY 7 |  |  | Test Product 7 | TEST FAMILY 7 |  |  | 1 | 1 |  |  |  |  |  |  |  |  |  |  |  |  | 1
 | CATEGORY 8 |  |  | Test Product 8 | TEST FAMILY 8 |  |  | 1 | 1 |  |  |  |  |  |  |  |  |  |  |  |  | 1
Spreadsheet: 177333d1346265929-sumproduct-and-sumifs-using-multiple-criteria-across-worksheets-sumifs
Sheet: 2

 |  |  | 10
 |  |  |  |  | No Condition |  |  |  |  | Condition2
Spreadsheet: 807225d1669810129-if-or-and-formula-help-costing-test
Sheet: Sheet1

 |  | Dimension (mm) |  |  | Description |  | Cost
 | Dim 'A' | 300 |  |  | Dim A, B, C are all under 300mm | 0 | 30
 | Dim 'B' | 250 |  |  | Dim A or B or C are equal to or greater than 300mm AND Dim A and B and C are less than 500mm | 300 | 60
 | Dim 'C' | 250 |  |  | Dim A, B, C are all equal to or greater than 500mm AND less than or equal to 750mm | 500 | 90
 |  |  |  |  | Dim A or B or C are greater than 750mm | 751 | POA
 |  |  |  |  |  | Cost
Spreadsheet: 104473d1302069344-excel-2007-having-trouble-using-the-index-and-match-functions-example
Sheet: Salzman, CC

[NAME]
800m | 2011-02-15 00:00:00 | 2011-02-19 00:00:00 | 2011-03-05 00:00:00 | 2011-03-05 00:00:00 | 2011-03-19 00:00:00 | 2011-03-26 00:00:00 | 2011-03-26 00:00:00 | 2011-03-29 00:00:00 | 2011-03-29 00:00:00
Lap 1 | x | 73 | 74.1 | 73.1 | 68.5 | 72 | 70.5 | 69.3 | 70.4
Lap 2 | x | 79 | 81.3 | 77 | 76.6 | 81.7 | 81.1 | 79 | 77.5
Time | 00:02:42 | 00:02:32 | 00:02:36.090000 | 00:02:30.170000 | 00:02:25.100000 | 00:02:33.700000 | 00:02:31.600000 | 00:02:29.270000 | 00:02:27.900000
1200m
Lap 1
Lap 2
Lap 3
Time
1600m | 2011-02-05 00:00:00 | 2011-02-23 00:00:00
Lap 1 | 82.4 | x
Lap 2 | 89.1 | x
Lap 3 | 94.7 | x
Lap 4 | 97.5 | x
Time | 00:06:04.400000 | 00:05:58
3200m | 2011-02-19 00:00:00
Lap 1 | 87
Lap 2 | 90
Lap 3 | 97
Lap 4 | 100
Lap 5 | 102
Lap 6 | 106
Lap 7 | 100
Lap 8 | 97
Time | 00:13:07.050000
 | Official Meet |  |  | Grade | 10 |  | Event |  | 800m | 1200m | 1600m | 3200m
 | Official Meet Relay |  |  | Years | 2 |  | Date
 | Time Trial |  |  | Meets |  |  | Season Best
Spreadsheet: 707215d1607099886-getting-information-for-a-table-book1
Sheet: filter

Name | xxx1 | xxx2 | xxx2 | xxx3 | xxx4 | xx5 | xx6 | xxx7 | [NAME]
[NAME] | 16166 | 17.29 | 1740 | 100.66 | 987 | 94 | 230 | 316 | 12
Bathgate | 7413 | 14.37 | 1100 | 76.54 | 484 | 132 | 67 | 73 | 1
Barrow | 7317 | 11.42 | 1015 | 88.86 | 416 | 68 | 171 | 157 | 3
Spreadsheet: 160517d1339096471-dynamic-named-range-based-on-data-selection-charts-example
Sheet: Sheet1

Company Name | Shipment Weight | [NAME] | Shipment Type | From | To | FCL/LCL | Base Rate | FSC | Pierpass | Overweight | Prepull | Entry Date | Use?
Coast Bridge Logistics | 605 |  |  |  |  |  |  |  |  |  |  | 2012-05-01 00:00:00
Coast Bridge Logistics | 5000 |  |  |  |  | FCL | 300 | 7 |  |  |  | 2012-05-02 00:00:00 | Yes
Coast Bridge Logistics | 23 |  |  |  |  | LCL | 15 | 5 |  |  |  | 2012-05-03 00:00:00 | No
Coast Bridge Logistics | 144 |  |  |  |  | FCL | 67 | 5 |  |  |  | 2012-05-04 00:00:00 | No
Coast Bridge Logistics | 1500 |  |  |  |  | FCL | 120 | 6 |  |  |  | 2012-05-05 00:00:00 | Yes
American Logistics International | 300000 |  |  |  |  | FCL | 80000 | 8000 |  |  |  | 2012-05-06 00:00:00 | Maybe
American Logistics International | 400000 |  |  |  |  | LCL | 90000 | 8000 |  |  |  | 2012-05-07 00:00:00 | Yes
American Logistics International | 500000 |  |  |  |  | FCL | 70000 | 8000 |  |  |  | 2012-05-08 00:00:00 | Maybe
Select Company
Coast Bridge Logistics |  |  |  |  |  |  |  |  | Pie Data
 |  |  |  |  |  |  |  |  | FCL
 |  |  |  |  |  |  |  |  | LCL
 |  |  |  |  |  |  |  |  | Name:
Spreadsheet: 378079d1424265164-deferring-revenue-course-revenue
Sheet: Courses

Course Name | Name | Start Date | No of Weeks | Price | Week 1 | Week 2 | Week 3 | Week 4 | Week 5 | Week 6 | Week 7 | Week 8 | Week 9 | Week 10 | Week 11 | Week 12
Course 1 | [NAME] | 2015-01-01 00:00:00 | 6 | 15
Course 1 | [NAME] | 2015-01-01 00:00:00 | 6 | 15
Course 2 | [NAME] | 2015-02-01 00:00:00 | 6 | 25
Course 2 | [NAME] | 2015-02-01 00:00:00 | 6 | 25
Course 2 | [NAME] | 2015-02-01 00:00:00 | 6 | 25
Course 3 | [NAME] | 2015-02-21 00:00:00 | 4 | 30
Course 3 | Alf Jones | 2015-02-21 00:00:00 | 4 | 30
Course 3 | [NAME] | 2015-02-21 00:00:00 | 4 | 30
Spreadsheet: 729819d1619369422-index-and-filter-functions-workaround-to-sort-filter-and-format-within-array-example
Sheet: Slide 8 LT

 |  |  |  | Filter function will automatically list team members
 |  |  |  | Conditional Formatting will fill in colors.
 |  |  |  | Cannot edit anything below. Must go to master spreadhseet for any edits
 |  |  |  | Crtl+V Paste Option: Keep Source Formatting
 |  |  |  |  |  |  |  |  |  |  |  |  |  | What I want to do | And also be able to sort values based on color or value
 | Name | Title | Diversity | Level | Time in Level (Yrs.) | Time with WRK (Yrs.) | Risk of Loss | Developmental Areas | Core Strengths | Potential Next Role | Move time |  |  | Name | Diversity | Level | Time in Level (Yrs.) | Time with WRK (Yrs.) | Risk of Loss | Developmental Areas | Core Strengths | Potential Next Role | Move time
 |  |  |  |  |  |  |  |  |  |  |  |  |  | A Worker 1
 |  |  |  |  |  |  |  |  |  |  |  |  |  | B Worker 2
 |  |  |  |  |  |  |  |  |  |  |  |  |  | C Worker 3
 |  |  |  |  |  |  |  |  |  |  |  |  |  | D Worker 4
Spreadsheet: 268708d1380746653-help-to-amend-sumproduct-formula-to-minus-depending-on-entry-in-another-column-test-page
Sheet: Summary

 | KEY | KEY | KEY | KEY | KEY | KEY |  |  |  | Daily totals
 | PP1 | PP2 | PP3 | PP4 | PP5 | PP6 | MTR | COT | RECON
April | 5 | 8 | 1 | 1 |  |  |  |  |  |  |  |  | IF(SUMPRODUCT(('Metering Jobs'!$P$2:$P$20000=B9)*('Metering Jobs'!$B$2:$B$20000=Summary!$M$8)*('Metering [NAME])))
May | 1 | 4 | 1 | 1 | 2 | 0 | 0 | 0
June | 0
July | 0
August | 0
September | 0
October | 0
November | 0
December | 0
January | 0
February | 0
March | 0
Spreadsheet: 39050d1237279518-calculating-turn-around-time-complex-turn-around-time-calculation
Sheet: Sheet3

 | Date received | Time received | Date completed | Time completed by | TAT Outcome (manual) Yes / No | TAT outcome using [NAME] Yes / No | 1 | 2 | 3 | 4 | 5 | 6 | 7 | TAT outcome using [NAME] Yes / No | TAT target | < =2 hours (working hours only)
Scenario 1 | 2009-03-11 00:00:00 | 12:00:00 | 2009-03-11 00:00:00 | 14:00:00 | Yes |  | YES | NO | NO | NO | NO | NO |  |  | Day begin | 06:00:00
Scenario 2 | 2009-03-11 00:00:00 | 13:00:00 | 2009-03-11 00:00:00 | 16:00:00 | Yes |  | NO | YES | NO | NO | NO | NO |  |  | Day end | 14:30:00
Scenario 3 | 2009-03-11 00:00:00 | 13:00:00 | 2009-03-12 00:00:00 | 06:30:00 | Yes |  | NO | NO | YES | NO | NO | NO
Scenario 4 | 2009-03-13 00:00:00 | 13:00:00 | 2009-03-16 00:00:00 | 06:30:00 | Yes |  | NO | NO | NO | YES | NO | NO |  |  | Working days: | Monday to Friday
Scenario 5 | 2009-03-14 00:00:00 | 13:00:00 | 2009-03-15 00:00:00 | 13:00:00 | Yes |  | NO | NO | NO | NO | YES | NO
Scenario 6 | 2009-03-14 00:00:00 | 13:00:00 | 2009-03-16 00:00:00 | 08:00:00 | Yes |  | NO | NO | NO | NO | NO | YES |  |  | Holidays | 2009-04-10 00:00:00 | Good Friday
Scenario 7 | 2009-04-09 00:00:00 | 13:00:00 | 2009-04-14 00:00:00 | 06:30:00 | Yes |  | NO | NO | NO | NO | NO | NO |  |  |  | 2009-04-13 00:00:00 | easter Monday
 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 2009-12-25 00:00:00 | Christmas
Scenarios explained
Scenario 1 | Simple scenario where difference of completed & received time is checked to see if it is <= 2 hours
Scenario 2 | work received near to end of day, however job completed the same day, altough after office hours, hence it is considered as TAT met, since the client will consider his job was completed in 1.5 hours
Scenario 3 | work received 1.5 hours before End of day (EOD), officially my 2 hours completes the next day, hence TAT is met, if job completed latest by 6.30 am
Scenario 4 | job received on Friday at 1.00 pm and thus the TAT will be met if job is completed the next working day within a total time of 2 hours
Scenario 5 | if job is received on weekend and completed on weekend iteself, then regardless of time, job is considered within TAT
Scenario 6 | if job is received on weekend and completed on the next working day within a total time difference of 2 hours, it is considered TAT is met
Scenario 7 | if job is on the eve of a holiday almost at the end of the day, and if the job is completed within a total of 2 hours on the next working day, then TAT is met
Note : Altough we follow workdays and work hours, our customers may send jobs any time and any day.  To meet peak season workloads, we often work after hours and sometimes on weekends too.  To the customer, the committed TAT is 2 hours (working hours)
 |  |  |  |  | 06:30:00
Spreadsheet: 830681d1685030739-calculate-time-in-excel-pacecalcuator
Sheet: Table 1 (3)

2021 年星期三馬拉松訓練班測試成績及訓練圈速
教練  ：  馮華添(Tim)，韋杰(Henry)，陳仁賢(仁哥)
No. | Name | 2.4K | 400m | 10K | half-Marathan | Marathon | Marathon in Mins | Marathon Per Km | Marathon  400 m
 | [NAME] | 00:08:49.978000 | 01:28.3 | 00:41:06.029000 | 01:27:12.989000 | 02:59:54.038000
1 | [NAME] | 00:08:50
Pace | time/distance
Running time | pace*distance |  |  |  |  |  |  |  |  |  | Remarks
4 |  | using .22 m/sec time 400 m = 88.33 sec |  |  |  |  |  |  |  |  |  | Based on the result of 2.4 km/h, convert into km/sec
5 |  | How can I convert 88.33 into 00:01:28 |  |  |  |  |  |  |  |  |  | Calculate the split of 400 m/hr
6 |  | How can I make one formula for the result
7 |  |  |  |  |  |  |  |  |  |  | 400 m/h
8 |  |  |  |  |  |  |  |  |  |  | 10 k/h
9 |  |  |  |  |  |  |  |  |  |  | 21.0975 km | Half-Marathon
10 |  |  |  |  |  |  |  |  |  |  | 42.195 km/h | Maraathon
11 |  |  |  |  |  |  |  |  |  |  | Reference : | https://en.run-motion.com/average-speed-calculator-pace/
12 |  |  |  |  |  |  |  |  |  |  |  | [NAME] time into Ｍinute in Excel,.multiple by 1440
13 |  |  |  |  |  |  |  |  |  |  |  | [NAME] time into Second in Excel,.multiple by 86401
14
15 |  |  |  |  |  |  |  |  |  |  | Pace | time/distance
16 |  |  |  |  |  |  |  |  |  |  | Running time | pace*distance
17
18
19
20
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
Spreadsheet: 733100d1621392666-getting-the-total-orders-of-each-hour-from-many-days-order-by-hours-of-the-day-bvj-
Sheet: option 1

Time of day | Total orders | 2020-01-05 00:00:00 | 2020-01-06 00:00:00 | 2020-01-07 00:00:00
12am |  | 4 | 4 | 2
1am |  | 3 | 2 | 3
2am |  | 3 | 3 | 1
3am |  | 2 | 5 | 1
4am |  | 4 | 6 | 2
5am |  | 4 |  | 2
6am |  | 6 | 2 | 2
7am |  | 2 | 5 | 2
8am |  | 3 | 1 | 1
9am |  | 7 | 8 | 9
10am |  | 12 | 8 | 13
11am |  | 10 | 7 | 16
12pm |  | 19 | 20 | 22
1pm |  | 28 | 17 | 20
2pm |  | 18 | 15 | 21
3pm |  | 19 | 17 | 28
4pm |  | 9 | 20 | 29
5pm |  | 22 | 23 | 25
6pm |  | 7 | 32 | 32
7pm |  | 8 | 18 | 13
8pm |  | 11 | 12 | 16
9pm |  | 3 | 8 | 10
10pm |  | 5 | 13 | 7
11pm |  | 1 | 9 | 9
Spreadsheet: 732718d1621182248-if-index-match-with-multiple-criterias-ifindexmatch
Sheet: Sheet1

 | Type of Insurance
Example 1 | Art
Example 2 | Crime
Example 3 | Transport
Example 4 | Transport
Example 5 | Crime
Example 6 | D&O
Example 7 | D&O
Example 8 | Art
Example 9 | Transport
Example 10 | Transport
Spreadsheet: 823113d1679715637-summarizing-a-workbook-lego-replacement-parts
Sheet: Set # 7256

Checked | Qty | Part# | Colour | Description
 | 1 | 3022 | [NAME] | Plate 2 x 2
 | 2 | 2444 | [NAME] | Plate, Modified 2 x 2 with [NAME]
 | 1 | 2431 | [NAME] | Tile 1 x 4
 | 1 | 3024 | Dark Red | Plate 1 x 1
 | 2 | 44302 | Light Grey | Hinge Plate 1 x 2 Locking with 2 Fingers on End (Undetermined Type)
 | 1 | 3068b | Light Grey | Tile 2 x 2 with Groove
 | 1 | 3044b | [NAME] | Slope 45 2 x 1 Double with Inside Bar
 | 1 | 4589 | Trans Blue | Cone 1 x 1 without Top Groove
 | 1 | 2412b | Yellow | Tile, Modified 1 x 2 Grille with Bottom Groove / Lip
Spreadsheet: 49967d1249392383-count-and-total-the-unknown-matching-data-occurences-across-multi-worksheets-dummy
Sheet: Nov

bird | 2
bird | 2
bird | 2
bird | 2
bird | 2
bird | 2
bird | 2
bird | 2
bird | 2
bird | 2
bird | 2
cat | 103
cat | 103
cat | 103
cat | 103
cat | 103
cat | 102
cat | 102
cat | 102
cat | 102
dog | 802
dog | 802
dog | 802
dog | 802
dog | 802
dog | 802
dog | 802
dog | 804
dog | 804
dog | 804
dog | 804
Spreadsheet: 6dec14f5-ca39-4d75-9bc8-69162d3b6a00
Sheet: Sheet1

PROG ID | ATHLETE ID | ATHLETE FIRST | ATHLETE LAST | NATIONALITY | START NUMBER | RUN | T1 | BIKE | T2 | RUN.1 | POSITION | TOTAL TIME
259074 | 9358 | Lynette | Fischer | RSA |  | 00:43:32 | 00:00:00 | 01:24:05 | 00:00:00 | 00:23:48 | 1 | 02:31:25
Spreadsheet: harry_arora__12154__Plan_IncomeSheets_Presto
Sheet: Options

Income Statement - ($000s)
 |  |  | 2001 Forecast |  | 2002 Plan |  | [NAME]
Originations |  | $ | 0 | $ | 0 | $
Trading |  |  | 0 |  | 0
FV-Investments |  |  | 0 |  | 0
Accrual |  |  | 0 |  | 0
Operating Expenses |  |  | 0 |  | 0
Other |  |  | 0 |  | 0
Total Margin |  | $ |  | $ |  | $
Capital Charge |  |  | 0 |  | 0
Total Margin Net of Capital Charge |  | $ |  | $ |  | $
Compensation/Taxes and Benefits |  |  |  |  |  |  |  |  |  |  | 1000
Employee Expense
Travel/Entertainment |  |  |  |  |  |  |  |  |  |  | -1
Consulting |  |  | 0
Office
Controllable Infrastructure |  |  | 34.86
System Development |  |  | 0 |  | 0
Insurance |  |  | 0 |  | 0
Analyst Associates (includes Comp, Taxes & Benefits and Allocation) |  |  | 62.4
Other Expense
Outside Legal |  |  | 0
Outside Tax |  |  | 0 |  | 0
Depreciation & Amortization |  |  | 0 |  | 0
Total Direct Expenses |  | $ |  | $ |  | $
Other Allocated Expenses |  | $ |  | $ |  | $
Pre-Tax Income |  | $ |  | $ |  | $
Headcount
Executive |  |  | 1 |  | 1
Director |  |  | 0 |  | 1
Manager |  |  | 2 |  | 4
Analyst and Associates |  |  | 0 |  | 1
Support Departments |  |  | 0 |  | 0
Total Headcount
2002 Plan (in thousands) |  |  | Team |  | ENA Total
Direct Expense Headcount Ratio |  | $ |  | $
Productivity [NAME] |  | $ |  | $
ROCE
Spreadsheet: 868246d1714991406-how-to-calculate-an-automatic-payrise-every-x-months-after-a-variable-employee-start-date-excel-forum-sample-data-aligw
Sheet: Sheet1

 | Raise rate | 0.1
 | Months raise applies after starting month, and then every, how many months? | 8
 | Months | Start | Salary | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 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
 | Employee 1 | 3 | 5000
 | Employee 2 | 4 | 6000
 | Employee 3 | 6 | 7000
 | Employee 4 | 7 | 4000
 | Employee 5 | 9 | 12000
 | Employee 6 | 11 | 5500