Addendum: Band-by-Band Comparison of DX QSO Rates for CQ WW, 2005 to 2017

This post shows band-by-band plots of inter-zone QSOs made during CQ WW between 2005 and 2017.

I thought that it might be somewhat interesting to include a plot that shows the same data, integrating over all bands:

Revised Cleaned and Augmented Logs for 2017 CQ WW CW and SSB Contests

Revisions to the cleaned and augmented versions of the logs for the CQ WW CW and SSB contests are now available for the period 2005 to 2017.

Links to the cleaned and augmented logs may be followed here.

The cleaned logs are the result of processing the QSO: lines from the entrants' submitted Cabrillo files to ensure that all fields contain valid values and all the data match the format required in the rules. Any line containing illegal data in a field (for example, a zone number greater than 40, or a date/time stamp that is outside the contest period) has simply been removed. Also, only the QSO: lines are retained, so that each line in the file can be processed easily. All zones are now rendered with two digits, so as to further simplify processing by other parties.

The augmented logs contain the same information as the cleaned logs, with the addition of some useful information on each line. The information added to each line comprises:

1. The sequence of four characters that are the same for each entry in a particular log:
•  a. letter "A" or "U" indicating "assisted" or "unassisted"
•  b. letter "Q", "L", "H" or "U", indicating respectively QRP, low power, high power or unknown power level
•  c. letter "S", "M", "C" or "U", indicating respectively a single-operator, multi-operator, checklog or unknown operator category [ the contest organisers have stated that checklogs are not made public, but in fact at least some of them from the early years have been, hence the need for the "C" category ]
•  d. character "1", "2", "+" or "U", indicating respectively that the number of transmitters is one, two, unlimited or unknown
2. A four-digit number representing the time if the contact in minutes measured from the start of the contest. (I realise that this can be calculated from the other information on the line, but it saves subsequent processors of the file considerable time to have the number readily available in the file without having to calculate it each time.)
3. Band
4. A set of eleven flags, each -- apart from column k -- encoded as T/F: 
   • a. QSO is confirmed by a log from the second party 
   • b. QSO is a reverse bust (i.e., the second party appears to have bust the call of the first party) 
   • c. QSO is an ordinary bust (i.e., the first party appears to have bust the call of the second party) 
   • d. the call of the second party is unique 
   • e. QSO appears to be a NIL 
   • f. QSO is with a station that did not send in a log, but who did make 20 or more QSOs in the contest 
   • g. QSO appears to be a country mult 
   • h. QSO appears to be a zone mult 
   • i. QSO is a zone bust (i.e., the received zone appears to be a bust)
   • j. QSO is a reverse zone bust (i.e. the second party appears to have bust the zone of the first party)
   • k. This entry has three possible values rather than just T/F:
     • T: QSO appears to be made during a run by the first party
     • F: QSO appears not to be made during a run by the first party
     • U: the run status is unknown because insufficient frequency information is available in the first party's log 
5. If the QSO is a reverse bust, the call logged by the second party; otherwise, the placeholder "-"
6. If the QSO is an ordinary bust, the correct call that should have been logged by the first party; otherwise, the placeholder "-"
7. If the QSO is a reverse zone bust, the zone logged by the second party; otherwise, the placeholder "-"
8.  If the QSO is an ordinary zone bust, the correct zone that should have been logged by the first party; otherwise, the placeholder "-"

Notes:

• The encoding of some of the flags requires subjective decisions to be made as to whether the flag should be true or false; consequently, and because CQ has yet to understand the importance of making their scoring code public, the value of a flag for a specific QSO line in some circumstances might not match the value that CQ would assign. (Also, CQ has more data available in the form of check logs, which are generally not made public.)
• I made no attempt to deduce the run status of a QSO in the second party's log (if such exists), regardless of the status in the first party's log. This allows one cleanly to perform correct statistical analyses anent the number of QSOs made by running stations merely by excluding QSOs marked with a U in column k.
• No attempt is made to detect the case in which both participants of a QSO bust the other station's call. This is a problematic situation because of the relatively high probability of a false positive unless both stations log the frequency as opposed to the band. (Also, on bands on which split-frequency QSOs are common, the absence of both transmit and receive frequency is a problem.) Because of the likelihood of false positives, it seems better, given the presumed rarity of double-bust QSOs, that no attempt be made to mark them.
• The entries for the zones in the case of zone or reverse zone busts are normalised to two-digit values.

Band-by-Band Comparison of DX QSO Rates for CQ WW, 2005 to 2017

The availability of public logs for CQ WW CW and SSB contests spanning thirteen years (cq-ww-2005--2017-augmented.xz; see here for details of the augmented format) allows us to look at the variation in the rate at which DX QSOs are made throughout each contest over a relatively long period. (I here define DX QSOs as those between different zones; other definitions are obviously possible.)

This exercise is essentially identical to the one performed last year for the twelve years of data then available.

In the plots, the number of QSOs is accumulated every ten minutes, so there are six points per hour.

I present the results without comment.

In regard to the 10m results, I should point out, as I did last year, that the CW contest occurs roughly a month after the SSB one, and is therefore considerably closer to the winter solstice, with the concomitant decrease in the number of hours of common daylight between Europe and North America (particularly western NA). I have long wondered what would happen if CQ WW (and CQ WPX and ARRL DX) would switch CW and SSB weekends every year.

Call Busts and Reverse Busts: CQ WW 2017

This is the thirteenth in a series of posts on busts and reverse busts in the CQ WW contests. These posts are based on the various public CQ WW logs (cq-ww-2005--2017-augmented.xz; see here for details of the augmented format).

Prior posts in the series on busts and reverse busts in CQ WW:

• 2005
• 2006 
• 2007
• 2008 
• 2009 
• 2010
• 2011 
• 2012
• 2013 
• 2014
• 2015 
• 2016

In this post, we include only verified QSOs; that is, QSOs for which both parties submitted a log. This is a change from earlier years' posts.

First, the tables for the 2017 SSB data:

2017 SSB -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	CN3A	10,192	183	1.8
2	LZ9W	8,143	162	2.0
3	PJ2T	7,168	158	2.2
4	HI3K	4,927	153	3.1
5	EI9E	5,055	152	3.0
6	A44A	6,508	135	2.1
7	CU4DX	7,020	133	1.9
8	V26B	6,595	132	2.0
9	OT5A	6,158	131	2.1
10	HG7T	6,488	128	2.0

2017 SSB -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	CN2R	8,757	247	2.8
2	SJ0X	5,256	233	4.4
3	IK2YCW	5,201	213	4.1
4	DF0HQ	7,444	172	2.3
5	HC0E	3,981	168	4.2
6	TM3R	3,819	168	4.4
7	6Y1LZ	5,229	153	2.9
8	VE3JM	4,991	140	2.8
9	EC2DX	7,228	137	1.9
10	W3LPL	5,693	131	2.3

2017 SSB -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	YB0ZZD	134	12.7
2	XR3L	111	12.6
3	EA1HTF	438	12.6
4	PY2TED	128	12.5
5	4Z5FW	120	12.5
6	AJ2E	100	12.0
7	UA6BFE	200	12.0
8	K2JMY	303	11.9
9	YO8RKP	144	11.1
10	PD5ISW	136	11.0

2017 SSB -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	VR20C	112	69.6
2	VR20FUN	134	25.4
3	TC10K	108	19.4
4	KM4EOG	201	16.9
5	XR3L	111	16.2
6	BG2VIA	224	16.1
7	IW0HLZ	212	14.2
8	BI4IV	405	13.6
9	BG8SXC	130	10.8
10	LU9DDJ	701	10.4

In tables of reverse busts, one sometimes finds what seems like an unreasonable number of reverse busts (as, in this table, for VR20C). This is generally caused by a discrepancy between the call actually sent by the listed station and the one recorded as being sent in at least some QSOs in the log, although it can, of course, be due to an unusual callsign structure (as appears to be the cause in the instant case) or poor audio quality.

Now the tables for the 2017 CW data:

2017 CW -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	PS0F	3,259	155	4.8
2	ZW8T	2,635	153	5.8
3	6Y0W	8,074	142	1.8
4	EW6W	7,641	141	1.8
5	F6ENO	2,838	134	4.7
6	EI3KE	1,881	130	6.9
7	ZW5B	2,411	119	4.9
8	TO2SP	6,095	112	1.8
9	CN2R	8,885	112	1.3
10	PJ2T	9,571	112	1.2

2017 CW -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	IB9T	7488	315	4.2
2	9571	8986	304	3.4
3	DF0HQ	8475	303	3.6
4	ED8X	8780	295	3.4
5	TI7W	7639	232	3.0
6	EF8R	9574	225	2.4
7	V47T	9234	222	2.4
8	P40L	9103	212	2.3
9	JS3CTQ	2050	206	10.0
10	SN8B	5334	186	3.5

2017 CW -- Highest Percentage of Busts (≥100 QSOs)
Position	Call	QSOs	% Busts
1	W2UDT	216	27.3
2	BD3MV	130	23.8
3	DJ5UZ	209	23.0
4	AE6JV	100	22.0
5	CT1ZQ	108	19.4
6	IQ5PO	130	18.5
7	W2OF	126	18.3
8	WA2JQK	266	18.0
9	PY1CC	134	17.2
10	PY4RL	101	16.8

2017 CW -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position	Call	QSOs	% Reverse Busts
1	CR5SSB	288	34.0
2	EA7/OG55W	226	21.2
3	OE3BKC	259	17.0
4	R4WBD	135	16.3
5	R2AHH	249	14.1
6	JL3ZHU	122	13.9
7	LU5DDX	141	12.8
8	K7HI	181	12.7
9	PY2LPM	206	12.1
10	IS0URA	134	11.9

Now we look at the tables that integrate ten years' data.

For SSB:

2008 to 2017 SSB -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	OT5A	69,479	1,381	2.0
2	LZ9W	81,222	1,350	1.7
3	PJ2T	68,720	1,293	1.9
4	A73A	59,609	1,214	2.0
5	CN3A	78,609	1,206	1.5
6	HG1S	46,753	979	2.1
7	JA7YRR	29,688	826	2.8
8	PT2CM	26,606	766	2.9
9	LY7A	50,053	755	1.5
10	RT6A	41,974	750	1.8

2008 to 2017 SSB -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	DF0HQ	78,630	2,244	2.9
2	JA3YBK	40,551	1,228	3.0
3	K3LR	68,781	1,077	1.6
4	WE3C	39,647	1,073	2.7
5	CN2R	60,225	1,032	1.7
6	HG1S	46,753	939	2.0
7	S52ZW	34,604	895	2.6
8	GM2T	37,394	861	2.3
9	CN3A	78,609	813	1.0
10	W3LPL	52,637	790	1.5

2008 to 2017 SSB -- Highest Percentage of Busts (≥500 QSOs)
Position	Call	QSOs	% Busts
1	PV8ADI	858	12.6
2	K2JMY	2,062	12.2
3	EA1HTF	854	10.9
4	EA7JQT	572	10.9
5	PU1MMZ	544	9.5
6	PU2TRX	868	9.3
7	UR5ZDZ	502	8.7
8	K8TS	732	8.7
9	YB9KA	557	8.6
10	DS5FNE	1,396	7.6

2008 to 2017 SSB -- Highest Percentage of Reverse Busts (≥500 QSOs)
Position	Call	QSOs	% Reverse Busts
1	CW90A	1,370	30.9
2	BA8AG	752	16.4
3	BW2/KU1CW	940	12.3
4	ZP6DYA	1,226	11.3
5	V84SCQ	806	10.5
6	LU9DDJ	701	10.4
7	BI8FZA	654	10.4
8	BV55D	919	10.2
9	JG3SVP	1,270	10.1
10	PP5BS	895	9.5

 And for CW:

2008 to 2017 CW -- Most Busts
Position	Call	QSOs	Busts	% Busts
1	PJ2T	86,693	1,232	1.4
2	PV8ADI	8,127	1,177	14.5
3	LZ9W	91,852	1,098	1.2
4	PI4CC	49,201	965	2.0
5	HG1S	39,874	924	2.3
6	D4C	64,215	837	1.3
7	PJ4A	68,099	810	1.2
8	NR4M	52,432	742	1.4
9	9A1A	87,797	742	0.8
10	RW0A	44,026	723	1.6

2008 to 2017 CW -- Most Reverse Busts
Position	Call	QSOs	Reverse Busts	% Reverse Busts
1	DF0HQ	84,331	3,055	3.6
2	JS3CTQ	22,349	2,905	13.0
3	ES9C	49,467	1,512	3.1
4	W2FU	55,507	1,415	2.5
5	DR1A	54,949	1,396	2.5
6	K3LR	71,723	1,395	1.9
7	IR4X	43,912	1,325	3.0
8	W0AIH	31,204	1,246	4.0
9	NR4M	52,432	1,129	2.2
10	V26K	50,123	1,122	2.2

2008 to 2017 CW -- Highest Percentage of Busts (≥500 QSOs)
Position	Call	QSOs	% Busts
1	BD3MV	954	19.8
2	AD7XG	911	18.7
3	W2UDT	989	17.5
4	DJ5UZ	508	16.9
5	YO7LYM	1,461	16.7
6	JA3AHY	554	16.6
7	WP3Y	570	16.3
8	AE3D	1016	15.8
9	YU1NIM	619	15.8
10	N8WS	524	14.9

2008 to 2017 CW -- Highest Percentage of Reverse Busts (≥500 QSOs)
Position	Call	QSOs	% Reverse Busts
1	HA8FW	900	100.0
2	G3RWF	943	99.9
3	RZ3VO	1,792	49.9
4	YT65A	1,149	37.2
5	OG55W	2,569	32.2
6	5K0A	1,853	32.1
7	DP65HSC	516	16.9
8	5J1E	1,523	15.6
9	SB0A	1,102	15.5
10	YP0HQ	1,787	14.0

Statistics from 2017 CQ WW SSB and CQ WW CW logs

A huge number of analyses can be performed with the various public CQ WW logs (cq-ww-2005--2017-augmented.xz; see here for details of the augmented format) for the period from 2005 to 2017.

There follow a few analyses that have interested me. There is plenty of scope to use the files for further analyses.

 

Number of Logs

The raw number of submitted logs for SSB has been relatively flat for several years; the logs submitted for CW continues to show a fairly steady annual increase:

One not infrequently reads statements to the effect that the popularity of contests such as CQ WW has been increasing for the past several years. Certainly it is true that, for CW, the number of logs is still increasing, but the above plot shows that the same cannot be truthfully said for SSB, for which the number of logs has shown no systematic variation for the last lustrum or so.

 

Popularity

By definition, popularity requires some measure of people (or, in our case, the simple proxy of callsigns) -- there is no reason to believe, a priori, that the number of received logs as shown above is related in any particular way to the popularity of a contest.

So we look at the number of calls in the logs as a function of time, rather than positing any kind of well-defined positively correlated relationship between log submission and popularity (actually, the posts I have seen don't even bother to posit such a relationship: they are silent on the matter, thereby simply seeming to presume that the reader will assume one). 

However, the situation isn't as simple as it might be, because of the presence of busted calls in logs. If a call appears in the logs just once (or some small number of times), it is more likely to be a bust rather an actual participant. Where to set a cut-off a priori in order to discriminate between busts and actual calls is unclear; but we can plot the results of choosing several such values. 

First, for SSB:

Regardless of how many logs a call has to appear in before we regard it as a legitimate callsign, the popularity of CQ WW SSB in the past couple of years has fallen to a level rarely (if ever) seen in the public logs.

Conditions were quite bad for the 2016 running of the contest; nevertheless, while poor conditions might reasonably be expected to have a major bearing on metrics related to the number of QSOs, it is not obvious, a priori, that it should have a substantial effect on the raw popularity as estimated by this metric. It seems that there were simply fewer stations QRV for the contest in 2016 than in earlier years. This seems to be borne out by the numbers for 2017, when conditions were considerably improved over 2016, and yet the number of distinct callsigns seems to be more or less the same as in the prior year. It is certainly difficult to argue, on the basis of the above plot, that this contest is now more popular than it was at a similar point in the last solar cycle -- indeed, it appears, on its face, that the opposite is true.

[I note that a reasonable argument can be made that the number of uniques will be more or less proportional to the number of QSOs made (I have not tested that hypothesis; I leave it as an exercise for the interested reader to determine whether it is true), but there is no obvious reason why the same would be true for, for example, callsigns that appear in, say, ten or more logs.]


Moving to CW:

we see a similar story to SSB, except that any decrease in participation since the same point in the last cycle appears to be very small: participation in the CW event in the current inter-cycle doldrums seems to be more or less the same as at the corresponding point in the last cycle.

 

Geographical Participation

How has the geographical distribution of entries changed over time?

Again, looking at SSB first:

Zone 28 seems to be continuing to show a slow but sustained increase in the number of logs submitted. Nevertheless, compared to the behemoths like zones 14 and 15, the number of logs from zones such as 11 or 28 is miniscule. This can be seen more clearly if we plot the percentage of logs received from each zone as a function of time:

 On CW, most zones evidence a long-term increase:

But the relative increase seems to be spread more or less evenly across all zones, with the percentages of logs from each zone barely changing over the years 2005 to 2017:

 

Activity

Total activity in a contest depends both on the number of people who participate and on how many QSOs each of those people makes. We can use the public logs to count the total number of distinct QSOs in the logs (that is, each QSO is counted only once, even if both participants have submitted a log).

For SSB:

The total number of distinct QSOs in the current inter-cycle doldrums is essentially the same as at the same point in the last solar cycle.

And for CW:

On this mode there appears to be an underlying upward trend (on which the effect of the solar cycle is superimposed), although this suggestion must be regarded as merely tentative, as it is essentially based on the minor uptick in 2017. Still, it is noteworthy that, despite the claims I see that CW is an obsolete technology in serious decline, the actual evidence, at least from this, the largest contest of the year, is quite the opposite. (This is a good reminder that when someone makes a claim whose truth is not self-evident, one should examine the underlying data for oneself. I have found that all too often it transpires that no defensible evidence has been put forward for the conclusion being drawn.)

 

Running and Calling

On SSB, the ongoing gradual shift towards stations strongly favouring either running or calling, rather than splitting their effort between the two types of operation, continued in 2017:

I have not investigated the cause of the continued decrease in the percentage of stations strongly favouring running, although the public logs could readily be used to distinguish possibilities that spring to mind, such as more SO2R operation, more multi-operator stations, and/or a reluctance of stations to forego the perceived advantages of spots from cluster networks.

On CW, 2017 saw no real change in the percentage of stations that strongly favoured running; there was a very slight decrease in the percentage that strongly favoured calling (I leave the determination of statistical [in]significance of the 2017 numbers to the interested reader). The split between callers and runners continues to be much less bimodal on CW than on SSB, although there is no room for doubt that the long-term trend on both modes is towards what used to be "search and pounce" but is now "point and click":

FT8 and the Reverse Beacon Network (RBN)

The rise of FT8 over the past year or so leads one to wonder how FT8 activity compares to other modes. I thought that the RBN would be an ideal vehicle to make an objective comparison between , say,m FT8 and CW. After all, although the RBN documentation is rather vague on the subject of FT8, the web site does contain clear instructions as to how to upload FT8 posts to the RBN. The web-based spot filter also allows one to select FT8 posts:

Using the historical, archived RBN data, It's easy to create a plot showing the percentage of spots in particular modes as a function of time, from the beginning of the RBN's existence in 2009 to the present day (late 2018):

One immediately sees that there is a major problem with this plot: although FT8 is present, it appears for only a short span in the first half of 2018.

This prompts us to look at the actual historical data, to see when FT8 activity has been recorded.

The first FT8 post was on the 19th of February, 2018:

[HN:2018] rbncat 2018 | grep ",FT8," | head
W3OA,K,NA,7074,40m,K0ERE,K,NA,CQ,8,2018-02-19 23:42:16,6,FT8,20180219,1519083736
W3OA,K,NA,7074,40m,K8SIA,K,NA,CQ,13,2018-02-19 23:42:16,6,FT8,20180219,1519083736
W3OA,K,NA,7074,40m,WS9V,K,NA,CQ,13,2018-02-19 23:42:16,6,FT8,20180219,1519083736
W3OA,K,NA,7074,40m,K5MAF,K,NA,CQ,17,2018-02-19 23:42:31,6,FT8,20180219,1519083751
W3OA,K,NA,7074,40m,KI1P,K,NA,CQ,6,2018-02-19 23:42:31,6,FT8,20180219,1519083751
W3OA,K,NA,7074,40m,N1KDO,K,NA,CQ,22,2018-02-19 23:42:31,6,FT8,20180219,1519083751
W3OA,K,NA,7074,40m,WX2U,K,NA,CQ,7,2018-02-19 23:42:31,6,FT8,20180219,1519083751
W3OA,K,NA,7074,40m,YV2GAW,YV,SA,CQ,8,2018-02-19 23:42:31,6,FT8,20180219,1519083751
W3OA,K,NA,7074,40m,N4XPZ,K,NA,CQ,2,2018-02-19 23:43:01,6,FT8,20180219,1519083781
W3OA,K,NA,7074,40m,VA2FW,VE,NA,CQ,3,2018-02-19 23:43:06,6,FT8,20180219,1519083786
[HN:2018]

This aligns with the start of the yellow blip in the plot above, and presumably coincides with the date when the RBN first permitted FT8 posts.

However, the most recent FT8 post (I am writing this on the 12th of November, 2018) was on the 12th of June, 2018:

[HN:2018] rbncat 2018 | grep ",FT8," | tail
KM3T-2,K,NA,7074,40m,AC9HP,K,NA,CQ,-20,2018-05-29 11:47:01,6,FT8,20180529,1527594421
W3OA,K,NA,7074,40m,KK9G,K,NA,CQ,-12,2018-06-13 11:02:02,6,FT8,20180613,1528887722
WZ7I,K,NA,10136,30m,EW8W,EU,EU,CQ,-21,2018-06-13 11:02:03,6,FT8,20180613,1528887723
WZ7I,K,NA,10136,30m,VA3HP,VE,NA,CQ,-16,2018-06-13 11:02:03,6,FT8,20180613,1528887723
WZ7I,K,NA,7074,40m,KK9G,K,NA,CQ,0,2018-06-13 11:02:03,6,FT8,20180613,1528887723
WZ7I,K,NA,14074,20m,GW0DSJ,GW,EU,CQ,-4,2018-06-13 11:02:23,6,FT8,20180613,1528887743
WZ7I,K,NA,14074,20m,PD3WDK,PA,EU,CQ,-18,2018-06-13 11:02:23,6,FT8,20180613,1528887743
WZ7I,K,NA,14074,20m,SP7FFY,SP,EU,CQ,-16,2018-06-13 11:02:23,6,FT8,20180613,1528887743
WZ7I,K,NA,14074,20m,UA6FZ,UA,EU,CQ,-12,2018-06-13 11:02:23,6,FT8,20180613,1528887743
WZ7I,K,NA,18100,17m,GI3SG,GI,EU,CQ,-11,2018-06-13 11:02:23,6,FT8,20180613,1528887743
[HN:2018]

(And we see that prior to the 13th of June, the next most recent post was on the 29th of May.)

It seems that, after a relatively brief period during which FT8 posts were allowed, the controllers of the RBN silently decided to ban them -- even though one is still permitted to select such (now non-existent) posts on the web interface . Consequently, at least as of the current date, the RBN appears to be essentially useless as a resource for determining any metric related to current or historical FT8 activity.

Summary File for RBN data, 2009 to 2017

The complete set of RBN data for 2009 to the end of 2017, after uncompression, is some 60GB 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 the basic script for each individual year from 2009 to 2017 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 more 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	11

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 11 distinct G(15, 100) grid cells.

The summary file allows rather rapid analysis of the RBN. For example, this plot in this post was originally generated rather tediously from the entire 50GB dataset. Using the summary file (but now extended to include 2017), the same plot --

 -- can be generated in about five seconds. From this plot, for example, we can immediately see that the largest number of daily posts occurred during the 2017 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.

The summary file can be used to generate a simple plot of the growth of the RBN since its inception just as quickly:

Y82V audio

The operators at the WRTC station Y82V have kindly made their audio available. (The file contains the audio from both rigs, one in each stereo channel.) The size of the file is roughly 108MB.

World Radio Team Championship (WRTC) logs

The logs for the 63 participant stations in WRTC 2018 are available:

1. In Cabrillo format;
2. In ordinary ASCII text format.