Minor change to Reverse Beacon Network data files

While working on a script to process the RBN data files last week, I discovered that the raw historical data maintained by the RBN itself have not been properly filtered to remove duplicated lines. I have therefore replaced the data files I use with new versions in which the duplicated lines have been removed. This is a minor change (on a recent day, there were a total of 1,194,001,674 lines in the database, 1,188,655,665 of these were unique, implying that 5,346,009 additional lines were present, for a duplication rate of about 0.5%).

Estimating CW Activity from RBN data, 2009 to 2020

It is possible to generate a metric of CW activity from RBN data. Extending the analysis in the linked post to 2020, we first check that the data have not changed in a way that would vitiate the result:

As in prior years, the overall shape of the data seems to be robust.

So, extending the prior analysis to include 2020, we obtain this graph:

Unsurprisingly, we see that a marked increase in CW activity (as measured by the defined metric) occurred in 2020. Because activity on all HF bands seems to have increased, it seems fair to ascribe the overall increase substantially to the COVID-19 pandemic rather than merely the improvement in propagation above 14 MHz that characterised the last quarter of 2020. It will be interesting to see if this increase is sustained in 2021.

Perhaps the most important result from this exercise, though, is the continued lack of evidence of any substantive long-term decline in the number of calls active on CW.

Evaluating Station Contributions to the Reverse Beacon Network: 2020

Applying the algorithm described here to the Reverse Beacon Network data for 2020 (1.1GB; MD5: 966cc0317bc0e94d70d3b9a553215702) we obtain the following tables for the stations that, on the basis of that algorithm, made the highest-valued contributions to the RBN in 2020:

Band	Position	Call	Value
ALL	1	OE9GHV	396,913
ALL	2	OK2EW	337,335
ALL	3	OH6BG	329,298
ALL	4	DL3DTH	316,445
ALL	5	WZ7I	315,386
ALL	6	KM3T	304,869
ALL	7	DO4DXA	289,372
ALL	8	DL9GTB	285,668
ALL	9	HA1VHF	278,712
ALL	10	W1NT-6	265,115

Band	Position	Call	Value
10m	1	G0LUJ	38,448
10m	2	DL3DTH	35,759
10m	3	KU7T	29,824
10m	4	OH6BG	25,361
10m	5	DO4DXA	23,999
10m	6	WZ7I	19,598
10m	7	KM3T	14,227
10m	8	DJ9IE	13,544
10m	9	EA8BFK	12,229
10m	10	G4ZFE	12,090

Band	Position	Call	Value
12m	1	CX6VM	11,101
12m	2	DL3DTH	5,763
12m	3	OH6BG	4,571
12m	4	EA8/DF4UE	3,642
12m	5	LZ4UX	3,494
12m	6	G0LUJ	2,956
12m	7	EA5WU	2,423
12m	8	JH7CSU1	2,292
12m	9	CT7ANO	2,187
12m	10	DJ9IE	2,101

Band	Position	Call	Value
15m	1	CX6VM	42,323
15m	2	VU3KAZ	28,040
15m	3	EA8/DF4UE	22,155
15m	4	OH6BG	21,016
15m	5	EA8BFK	18,996
15m	6	DL3DTH	13,416
15m	7	JH7CSU1	13,302
15m	8	W3UA	13,042
15m	9	DO4DXA	12,982
15m	10	KO7SS	12,850

Band	Position	Call	Value
17m	1	CX6VM	25,584
17m	2	OH6BG	23,968
17m	3	EA8/DF4UE	18,929
17m	4	WZ7I	17,580
17m	5	DL3DTH	17,170
17m	6	KM3T	15,229
17m	7	W1NT-6	14,918
17m	8	EA5WU	11,954
17m	9	OE9GHV	11,417
17m	10	KO7SS	10,529

Band	Position	Call	Value
20m	1	DL9GTB	124,911
20m	2	OE9GHV	109,617
20m	3	WZ7I	108,177
20m	4	KM3T	106,772
20m	5	OH6BG	103,200
20m	6	K1TTT	94,867
20m	7	VE2WU	94,197
20m	8	W1NT-6	86,002
20m	9	W3UA	84,003
20m	10	LZ7AA	83,671

Band	Position	Call	Value
30m	1	OH6BG	38,419
30m	2	SE5E	31,793
30m	3	DL3DTH	30,380
30m	4	OE9GHV	26,680
30m	5	W1NT-6	26,291
30m	6	OL7M	25,808
30m	7	EA5WU	24,246
30m	8	OK2EW	23,308
30m	9	UA4M	21,772
30m	10	F6IIT	21,634

Band	Position	Call	Value
40m	1	OE9GHV	126,261
40m	2	LZ7AA	98,606
40m	3	OL7M	93,747
40m	4	WZ7I	91,648
40m	5	DO4DXA	87,750
40m	6	KM3T	87,222
40m	7	W1NT-6	79,517
40m	8	N5RZ	77,767
40m	9	W3UA	74,512
40m	10	SE5E	73,488

Band	Position	Call	Value
80m	1	OE9GHV	84,977
80m	2	OK2EW	67,364
80m	3	DO4DXA	63,885
80m	4	DL3DTH	54,162
80m	5	DR4W	51,626
80m	6	SM6FMB	48,184
80m	7	LZ7AA	45,539
80m	8	DE1LON	42,187
80m	9	SM7IUN	41,695
80m	10	HB9BXE	41,349

Band	Position	Call	Value
160m	1	OK2EW	105,478
160m	2	HA1VHF	100,279
160m	3	DL3DTH	38,666
160m	4	DO4DXA	28,590
160m	5	AC0C	27,544
160m	6	OH6BG	20,963
160m	7	OE9GHV	19,965
160m	8	VE6JY	19,006
160m	9	KM3T	18,945
160m	10	UA4M	18,240

HF Beacons and the Reverse Beacon Network, 2020

Here is  a table of the twenty fixed-frequency stations most often posted by the RBN in 2020:

Position	Station	Frequency (kHz)	Number of Posts
1	CS3B	14100	113,851
2	YV5B	14100	98,980
3	YU7QF	14017	85,288
4	I1MMR	7026	83,120
5	AA1K	1821	81,490
6	OH2B	14100	63,044
7	W6WX	14100	62,000
8	OP5K	7017	55,551
9	HB4FV/B	10134	48,303
10	UA3KW	14006	47,479
11	SP3CW	3565	45,430
12	4X6TU	18110	45,235
13	DK5JPL	3541	43,544
14	EW7LO	7008	42,240
15	OP5K	7018	40,034
16	CS3B	18110	39,162
17	4X6TU	14100	38,866
18	DK4AN	3569	37,837
19	SQ6JAN	3565	37,424
20	DJ6UX	7039	35,894

Notes:

1. Frequencies are rounded to the nearest kHz;
2. I am unsure how the U.S. stations in the list can be legal, since the FCC's regulations appear to limit [unattended] HF beacons to a portion of 10m;
3. It is my memory that the original HF beacons were all located on 28 MHz, so that listeners could be made aware of an opening. It is noticeable that not a single one of the stations on the list above is on 10m: the vast majority are on bands that can reasonably be expected to support some kind of non-local propagation at almost all times (which is probably the very reason that they are posted by the RBN so often -- but one does wonder what the putative purpose of such a beacon is);
4. The two entries for OP5K probably reflect errors in the reporting of frequency by the RBN stations; the actual frequency was probably at or close to 7017.5 kHz
   

Below are figures showing, for each of the stations in the table above, the signal strength as reported by the ten RBN stations that most frequently posted each individual beacon station.

In the following figures:

1. The ordinate for each of the strip charts ranges between 0 dB and the value shown as FSD (i.e., full scale deflection) near the bottom right-hand corner; in this case, the maximum value of each strip is therefore 80 dB.
2. The value plotted in this manner is the value denoted SNR by the RBN.  Remember that the RBN has an odd definition of SNR.
3. The abscissa is divided into a number of bins of equal duration. On each plot there are 100 such bins; because the duration covered by each plot is one year, each bin therefore covers about 3½ days.
4. At the bottom of each strip chart is a coloured bar. Each bin in these bars is coloured so as to represent the total number of times that the RBN station spotted the beacon in the period covered by the bin.The colour legend for each figure is to the right of the figure.
5. For the period covered by each 3½-day bin, the lower quartile of SNR readings is coloured grey, the upper quartile is coloured white, and the middle two quartiles are coloured blue.
6. The vertical order of the various RBN stations is determined solely by the chronological order in which each station first spotted the beacon.

Summary File for RBN data, 2009 to 2020

The complete set of RBN data for 2009 to the end of 2020, after uncompression, exceeds 100GB in size. As not all analyses need the complete dataset, I have constructed a summary file (rbn-summary-data.xz) that contains an overview of the data and which is sufficient for many kinds of analysis that do not depend on the details of individual posts to the RBN. (The basic script used to generate this summary file may be found here; the actual summary file is created by running this basic script for each individual year from 2009 to 2020 and concatenating the results after removing the header line from all except the first year.)

The summary file, after being uncompressed, comprises a single large table of values separated by white space. The name of each column (there are twelve columns in all) is on the first row. The columns are:

1. band: a string that identifies the band pertaining to this row. Typical values are "15m" or "160m"; if a row contains data that are not distinguished by band, then the characters "NA" are used.
2. mode: a string that identifies the mode pertaining to this row. Typical values are "CW" or "RTTY"; if a row contains data that are not distinguished by mode, then the characters "NA" are used.
3. type: a single character that identifies whether the data on this row are for a period of a year ("A"), a month ("M") or a day ("D").
4. year: the numeric four-digit value of the year to which the current row pertains.
5. month: the numeric value of the month (January = 1, etc.) of the data in this row. If the data are of type A or D, then this element has the value "NA".
6. doy: the numeric value of the day number of the year (January 1st = 1, etc.). The maximum value in each year is 366 (even if the year is not a leap year). In the event that the year is not a leap year, the data in columns 7, 8 and 9 will be set to 0 when doy is 366. If the data are of type A or M, then this element has the value "NA".
7. posts: the total number of posts recorded by the RBN for the band, mode and period identified by the first six columns. 
8. calls: the total number of distinguishable calls recorded by the RBN for the band, mode and period identified by the first six columns. 
9. posters: the total number of distinguishable posters recorded by the RBN for the band, mode and period identified by the first six columns. 
10. scatter: the value of a scatter metric that characterises the geography of the RBN for the band, mode and period identified by the first six columns. The scatter metric is the sum of all possible distance pairs of good posters (measure in km), divided by the number of distance pairs.
11. good posters: the total number of distinguishable posters recorded by the RBN for the band, mode and period identified by the first six columns, and for which location data are available from the RBN.
12.  grid metric: the total number of G(15, 100) grid cells that contain good posters.

For example, the first two lines of the summary file are (presented here as a table, in order to make it easier to view on mobile devices):

band	NA
mode	NA
type	A
year	2009
month	NA
doy	NA
posts	5007040
calls	143724
posters	151
scatter	5541
good_posters	150
grid_metric	22

This tells us that the first line of actual data in the file comprises annual data for the year 2009, with no separation by band or mode. In 2009, we see that there were 50,007,040 posts of 143,724 callsigns by 151 posters; the scatter metric, which is a measure of the geographic dispersion of the posters on the RBN., was 5,541; 150 different posters contributed the data, spread across 22 distinct G(15, 100) grid cells.

The summary file allows rather rapid analysis of many RBN overview statistics. For example, a plot of the daily number of posts covering the period from the inception of the RBN to the end of 2020 --  

-- can be generated on an ordinary desktop PC in a few seconds. From this plot, for example, we can immediately see that the largest number of daily posts occurred during the 2020 running of the CQ WW CW contest in late November (the second-highest cluster of peaks is for the CQ WPX contest, and the third is for the ARRL DX CW contest); also, the burst of activity that coincides with weekends is unmistakable.

For what it's worth, this is the code I used to generate the above plot (I apologise for the awful layout caused by the wrapping of long lines as they are forced into the narrow format used by blogger.com):

#!/usr/bin/Rscript

# generate a plot of the diurnal number of posts by the RBN, stacked by year

MIN_YEAR <- 2009
MAX_YEAR <- 2020

filename <- "/zd1/rbn/rbn-summary-data"  # the local location of the RBN summary data file

# first two lines of the file:
#band          mode          type          year         month           doy         posts         calls       posters       scatter  good_posters   grid_metric
#NA            NA             A          2009            NA            NA       5007040        143724           151          5541           150            22

# rounding function
round_n <- function(x, n) { return ( ( as.integer( (x - 1) / n) +1 ) * n ) }    # function to return next higher integral multiple of n, unless value is already such a multiple

data <- read.table(filename, header=TRUE)

# select diurnal data
diurnal_data <- subset(data, type=='D')

# drop the per-band and per-mode data
diurnal_all_bands_and_modes_data <- subset(diurnal_data, is.na(band) & is.na(mode))

# drop a bunch of columns that we don't want from the summary file
diurnal_all_bands_and_modes_data\$band <- NULL
diurnal_all_bands_and_modes_data\$mode <- NULL
diurnal_all_bands_and_modes_data\$type <- NULL
diurnal_all_bands_and_modes_data\$month <- NULL
diurnal_all_bands_and_modes_data\$calls <- NULL
diurnal_all_bands_and_modes_data\$posters <- NULL
diurnal_all_bands_and_modes_data\$scatter <- NULL
diurnal_all_bands_and_modes_data\$good_posters <- NULL
diurnal_all_bands_and_modes_data\$grid_metric <- NULL

# get ready to start to plot
graphics.off()

png(filename=paste(sep="", "/tmp/rbn-posts-from-summary.png"),  width=800, height=600)

x_lab <- 'DOY'

#            2009   2010                2011      2012     2013    2014      2015               2016      2017             2018     2019   2020
clrs <- c("black", "red", rgb(0.1, 0.1, 0.5), "yellow", "green", "blue", "violet", rgb(0.6, 0.2, 0.2), "white", "cornflowerblue", "gold1", "darkorange")

# create a frame to map between year and days in the year
days_in_year <- data.frame(seq(MIN_YEAR, MAX_YEAR), 365)
names(days_in_year) <- c("year", "days")

days_in_year\$days[days_in_year\$year %% 4 == 0] <- 366

# set boundaries
plot(0, 0, xlim = c(0.5, 366.5), ylim = c(0, round_n(max(diurnal_all_bands_and_modes_data\$posts), 1000000)), xaxt = "n", yaxt = "n", xlab = x_lab, ylab = "", type = 'n', yaxs="i")         # define the plotting region, but don't actually plot anything
rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = 'grey')

# now generate the plot, superimposing each year
for (this_year in seq(MIN_YEAR, MAX_YEAR))
{ this_years_data <- subset(diurnal_all_bands_and_modes_data, year==this_year)
  max_element <- days_in_year\$days[this_year - MIN_YEAR + 1]
 
# set up so that this_years_data\$id_365[365] = this_years_data\$id_366[366] = 366, so either column can be used as a vector of abscissæ,
# depending on whether there are 365 or 366 ordinate values
  this_years_data\$id_366<-seq.int(nrow(this_years_data))
  this_years_data\$id_365<-((this_years_data\$id_366 - 1) * 365.0 / 364.0) + 1

# remove a couple of columns that we no longer need
  this_years_data\$doy <- NULL
  this_years_data\$year <- NULL

# move the two new columns of days to the left of the frame: 365, then 366
  this_years_data <- (this_years_data[ c(ncol(this_years_data), ncol(this_years_data) - 1, 1:(ncol(this_years_data)-2))])


  lines(this_years_data[,(max_element-364)][1:max_element], this_years_data\$posts[1:max_element], type = 'l', col = clrs[this_year - MIN_YEAR + 1], lwd = 1) 
}

title_str <- paste(sep="", 'RBN POSTS PER DAY')
title(title_str)

title(ylab = '# OF POSTS (m)', line = 2.1, cex.lab = 1.0)

x_ticks_at <- c(1, 31, 61, 91, 121, 151, 181, 211, 241, 271, 301, 331, 361)
x_labels_at <- x_ticks_at
x_tick_labels <- x_ticks_at

axis(side = 1, at = x_ticks_at, labels = FALSE )    # ticks on x axis
axis(side = 1, at = x_labels_at, labels = x_tick_labels, tick = FALSE )

y_ticks_at <- seq(0, round_n(max(diurnal_all_bands_and_modes_data\$posts), 1000000), 100000)
y_labels_at <- seq(0, round_n(max(diurnal_all_bands_and_modes_data\$posts), 1000000), 1000000)
y_tick_labels <- seq(0, round_n(max(diurnal_all_bands_and_modes_data\$posts), 1000000) / 1000000, 1)

axis(side = 2, at = y_ticks_at, labels = FALSE )
axis(side = 2, at = y_labels_at, labels = y_tick_labels, tick = FALSE )

minx <- par("usr")[1]
maxx <- par("usr")[2]
miny <- par("usr")[3]
maxy <- par("usr")[4]

xrange <- maxx - minx
yrange <- maxy - miny

xpos <- minx + 0.025 * xrange
ypos <- miny + 0.975 * yrange

par(xpd=T, mar=c(0,0,4,0))

legend(x = xpos, y = ypos, legend = seq(MIN_YEAR, MAX_YEAR),
       lty=c(1, 1), lwd=c(2,2), col = clrs,
       bty = 'n', text.col = 'black')

graphics.off()
 

Of course, many other insights may be gleaned rather rapidly from the summary file.

Reverse NILs in CQ WW: 2020; 2011 to 2020

The basic notion of reverse NILs (rNILs; or, I suppose, RNILs) is described here, along with a description of a simple script for calculating rNILs for CQ WW contests and the result of applying that code to the contests for 2005. See also the comments at the end of that post. I would be delighted if anyone who spots any errors in the script would inform me.

We can also look at the results for periods of ten years at a time; the only difference in the way that these decadal tables are calculated is that the minimum number of rQSOs is raised from 50 to 250 for tables pertaining to all QSOs and from 25 to 125 for those pertaining to intra-W QSOs. (That is, the value of the variable MIN_QSOs is changed from 50 to 250.)

Here are the results for 2020 and for the period from 2011 to 2020. As usual, I refrain (with, it must be admitted, ever-increasing difficulty) from making some pointed comments about these tables. I have also added some additional columns to the W tables; I trust that these are self-explanatory.

2020 SSB:

Callsign	Total rQSOs	Total rNILs
3Z1K	1509	662
DX1CC	558	230
M0MNM	228	227
HC2AO	2830	219
US5AT	258	218
ZF1A	5260	145
YT5A	9571	135
LZ9W	9992	130
XE1CQ	1961	130
EA8RM	6984	122

Callsign	Total rQSOs	Total rNILs	% rNILs
M0MNM	228	227	99.6
M6RVA	51	47	92.2
US5AT	258	218	84.5
F5IND	113	75	66.4
W6BRY	110	71	64.5
KL7EX	61	39	63.9
HB9IQB	99	62	62.6
JF2WXS	92	48	52.2
HB3XUC	241	116	48.1
CS7AMN	133	60	45.1

Callsign	Total rQSOs with Ws	rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws
KV0Q	113	72	640	10
N7DD	65	43	625	8
W6KW	39	38	311	5
KV2K	43	34	1628	27
W3KL	33	33	1066	8
K3EST	260	23	1831	27
NY6DX	31	22	449	14
K5TR	452	16	2120	39
K7ZS	259	12	1065	13
N2IC	90	12	1134	10

Callsign	Total rQSOs with Ws	Total rNILs against Ws	% rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws	% rNILs against non-Ws
W3KL	33	33	100.0	1066	8	0.8
W6KW	39	38	97.4	311	5	1.6
KV2K	43	34	79.1	1628	27	1.7
NY6DX	31	22	71.0	449	14	3.1
N7DD	65	43	66.2	625	8	1.3
KV0Q	113	72	63.7	640	10	1.6
N1PGA	25	7	28.0	1104	6	0.5
N1LN	25	5	20.0	672	1	0.1
N7TU	77	11	14.3	666	12	1.8
N2IC	90	12	13.3	1134	10	0.9

2020 CW:

Callsign	Total rQSOs	Total rNILs
PI4DX	928	813
LY2ZO	2912	807
YU7XX	3303	590
DL9DBZ	405	356
EA6FO	7178	352
OK1TC	684	350
SP5SR	352	350
RU1A	6748	298
S57T	304	295
K3UA	1792	282

Callsign	Total rQSOs	Total rNILs	% rNILs
SP5SR	352	350	99.4
A65BB	133	131	98.5
S57T	304	295	97.0
JM2LEI	184	173	94.0
LZ3BB	308	280	90.9
DL9DBZ	405	356	87.9
PI4DX	928	813	87.6
W1OHM	70	61	87.1
N0IJ	206	167	81.1
JA7AKH	129	101	78.3

Callsign	Total rQSOs with Ws	rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws
N9EZ	113	69	86	48
K3UA	103	34	1689	248
KV2K	48	22	2206	41
NR5M	77	18	1384	11
W5ZN	54	17	1482	19
KC1XX	445	14	7745	115
K1TTT	301	14	5391	99
N7DD	26	14	909	17
NE3F	30	14	1462	26
K0RF	339	13	3501	38

Callsign	Total rQSOs with Ws	Total rNILs against Ws	% rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws	% rNILs against non-Ws
N9EZ	113	69	61.1	86	48	55.8
N7DD	26	14	53.8	909	17	1.9
NE3F	30	14	46.7	1462	26	1.8
KV2K	48	22	45.8	2206	41	1.9
K3UA	103	34	33.0	1689	248	14.7
W5ZN	54	17	31.5	1482	19	1.3
NA3M	26	8	30.8	2141	15	0.7
KB1W	25	7	28.0	1128	21	1.9
NR5M	77	18	23.4	1384	11	0.8
W1WEF	49	11	22.4	2222	24	1.1

As I write this, the results for the 2020 contests are yet to be announced. I shall make a prediction based on the past many years of data that none of the people in the final table of each group whose rNIL rate against Ws exceeds 50% will be disqualified for unsportsmanlike conduct. In fact, I predict that none of them will even be investigated in order to determine the reason for what appears on its face to be an excessive rNIL rate against Ws.

(I do note that rNILs do occur naturally when one of the two operators is a bit sloppy, and a rate that is higher against Ws than against non-Ws could [possibly] be explained as a natural occurrence: for example, the running W operator hears two callers, one W and one DX, and the running station would normally go back to and work the DX station if he can, while the W caller might sloppily believe that he was being worked. What is concerning, though, and should reasonably be a cause for further investigation, is when the rNIL rate against Ws greatly exceeds the rate against non-Ws.)

2011 to 2020 SSB:

Callsign	Total rQSOs	Total rNILs
EA2DMH	4192	1636
CN3A	82248	1573
JR4GPA	3423	1513
OT5A	65066	1400
LZ9W	89914	1385
OK1KZ	1814	1307
A73A	63222	1284
PJ2T	67287	1224
JE5JHZ	1660	1184
OG5B	1173	1171

Callsign	Total rQSOs	Total rNILs	% rNILs
OG5B	1173	1171	99.8
YL2014W	1105	1098	99.4
VU2SWS	261	259	99.2
YY4HAH	493	489	99.2
LY1G	666	659	98.9
CU4AT	274	270	98.5
KP4ROS	664	653	98.3
ZZ5Z	297	292	98.3
PY2ASS	312	296	94.9
YU1IZ	283	266	94.0

Callsign	Total rQSOs with Ws	rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws
KV0Q	733	382	8205	135
N7DD	609	373	9493	184
N9RV	785	138	11925	130
N2IC	730	102	13810	101
K3LR	4471	96	62913	827
W3LPL	2786	84	49945	737
NR5M	642	79	8502	120
K5TR	2355	68	20873	366
W4QNW	148	60	898	23
W0AIH	1468	53	18423	594

Callsign	Total rQSOs with Ws	Total rNILs against Ws	% rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws	% rNILs against non-Ws
N7DD	609	373	61.2	9493	184	1.9
KV0Q	733	382	52.1	8205	135	1.6
W4QNW	148	60	40.5	898	23	2.6
N9RV	785	138	17.6	11925	130	1.1
N2IC	730	102	14.0	13810	101	0.7
AB4B	171	22	12.9	6613	97	1.5
N3AD	177	22	12.4	11580	169	1.5
NR5M	642	79	12.3	8502	120	1.4
WA2HIP	145	17	11.7	935	39	4.2
K3UL	174	18	10.3	8721	155	1.8

CW 2011 to 2020:

Callsign	Total rQSOs	Total rNILs
OK1KZ	2119	1735
LZ9W	103845	1436
JR4GPA	6542	1361
PJ2T	88647	1348
D4C	59818	1256
9A1A	97854	1243
UA1CEC	4539	1172
PI4DX	18696	1100
PJ4A	67987	1096
P33W	79131	1095

Callsign	Total rQSOs	Total rNILs	% rNILs
RA3ATE	908	903	99.4
I1XSG	534	527	98.7
OK1KZ	2119	1735	81.9
WB0CFF	432	313	72.5
UA4CNZ	788	562	71.3
RA9KY	797	461	57.8
AB3UM	276	153	55.4
4S7KM	378	209	55.3
XF1IM	702	385	54.8
JA3AA	338	170	50.3

Callsign	Total rQSOs with Ws	rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws
NR5M	1615	247	22774	211
N7DD	302	178	7957	218
N9RV	637	131	18040	217
K0RF	2227	123	33855	454
KV2K	331	97	18376	286
N0NI	1061	79	28321	229
W3LPL	2970	75	72355	650
N7AT	1630	69	26173	216
NE3F	245	65	17325	297
NR4M	1800	64	49835	522

Callsign	Total rQSOs with Ws	Total rNILs against Ws	% rNILs against Ws	Total rQSOs with non-Ws	rNILs against non-Ws	% rNILs against non-Ws
N7DD	302	178	58.9	7957	218	2.7
W1IE	135	63	46.7	2990	126	4.2
KV2K	331	97	29.3	18376	286	1.6
KV0Q	224	60	26.8	2679	77	2.9
NE3F	245	65	26.5	17325	297	1.7
KZ5D	198	51	25.8	8850	109	1.2
W0ZA	125	28	22.4	4680	33	0.7
N9RV	637	131	20.6	18040	217	1.2
NR5M	1615	247	15.3	22774	211	0.9
NA3M	142	21	14.8	11028	93	0.8

 

Unofficial Station Reports, CQ WW SSB and CW, 2005 to 2020

Using the public logs, it is rather easy to generate unofficial station-by-station reports for the entrants in the CQ WW contests.

The contest committee generates official reports and generally sends these reports individually to each entrant. But these are typically not made public (although there are some exceptions). The unofficial reports, while not necessarily identical to the official ones, may hold some interest.

The unofficial reports may differ from the official ones because the contest committee has access to checklogs, which are not made public. Also, there are various pathological occurrences in logs that require a decision to be made as to how to classify one or more QSOs; the rules by which such decisions are made are not public, so the decisions that I made when constructing the unofficial reports may well be different from those made by the contest committee. Nevertheless, pathological logs (or pathological QSOs within a log) are relatively rare, so these decisions should affect a relatively small percentage of logs and QSOs. (Typical examples [there are many more] of circumstances in which decisions must made be are: by how much may clocks be skewed and a QSO still be considered valid? what to do if the transmitted callsign changes for some number of QSOs in the contest? what do to if more than one entrant claims to have used the same transmitted callsign?)

The complete set of unofficial reports for the CW and SSB versions of the CQ WW contest for the years 2005 to 2020 may be found in appropriately named files in this directory.