Column names

[warmonke]

Hi,
Thanks for uploading this extremely interesting dataset. I have a quick question regarding column headings, particularly concerning the second date column - is this the date the sighting was reported? Alternatively if you could provide a list of all the column headings to prevent and confusion/misinterpretation that would be greatly appreciated.

Thanks again.

[planetsig]

Thank you for your interest in this data set. Have to look at it closer. Should be self explanatory. Probably one date is the date recorded the other is the actual incident date. Which data set are you interested in UFO data or missing persons? The validity of this data is way out of date. The NUFORC data is really bad and the whole thing was an exercise dealing with public data which was fascinating how bad it really can be. There are several large error margins. Including the geo-location is just a gross estimate. In testing some of the geolocation codes were way off when validated with google maps and other sources. So careful on making conclusions with this data. One has to use this data as a guide to drill down into specific cases. Where you will really see the error margins in real life. So depending on the individual record correlation the error margins vary not only on the geocodes and on the dates as well. 59/10000 is a very small percentage of hits. The missing persons database is a joke (see article). Did a heat map on just the missing persons. Shocking. There is a real problem with the whole missing person system. Be careful pointing out issues with these public systems. It's like a double headed snake. It's swampy. Stay positive. If you want to collaborate on a new project of this kind let me know. Attached is the original now black balled article. Do not print, publish or distribute without my permission. You have been warned.

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On Sat, Nov 28, 2020 at 6:56 AM warmonke ***@***.***> wrote: Hi, Thanks for uploading this extremely interesting dataset. I have a quick question regarding column headings, particularly concerning the second date column - is this the date the sighting was reported? Alternatively if you could provide a list of all the column headings to prevent and confusion/misinterpretation that would be greatly appreciated. Thanks again. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#2>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AB4UC5ZRFOXLAJHKTCB344TSSDXP3ANCNFSM4UFY5MEA> .

[warmonke]

Hi Sigmond, Thanks for your reply. I was interested in using the dataset for a data visualisation project at university but the deadline has since passed. However if i choose to use the dataset in future I'll bear in mind your comments. Thanks for taking the time to reply and sorry for wasting your time. Regards, Dan

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On Sun, 6 Dec 2020, 16:45 sigmond axel, ***@***.***> wrote: Thank you for your interest in this data set. Have to look at it closer. Should be self explanatory. Probably one date is the date recorded the other is the actual incident date. Which data set are you interested in UFO data or missing persons? The validity of this data is way out of date. The NUFORC data is really bad and the whole thing was an exercise dealing with public data which was fascinating how bad it really can be. There are several large error margins. Including the geo-location is just a gross estimate. In testing some of the geolocation codes were way off when validated with google maps and other sources. So careful on making conclusions with this data. One has to use this data as a guide to drill down into specific cases. Where you will really see the error margins in real life. So depending on the individual record correlation the error margins vary not only on the geocodes and on the dates as well. 59/10000 is a very small percentage of hits. The missing persons database is a joke (see article). Did a heat map on just the missing persons. Shocking. There is a real problem with the whole missing person system. Be careful pointing out issues with these public systems. It's like a double headed snake. It's swampy. Stay positive. If you want to collaborate on a new project of this kind let me know. Attached is the original now black balled article. Do not print, publish or distribute without my permission. You have been warned. On Sat, Nov 28, 2020 at 6:56 AM warmonke ***@***.***> wrote: > Hi, > Thanks for uploading this extremely interesting dataset. I have a quick > question regarding column headings, particularly concerning the second date > column - is this the date the sighting was reported? Alternatively if you > could provide a list of all the column headings to prevent and > confusion/misinterpretation that would be greatly appreciated. > > Thanks again. > > — > You are receiving this because you are subscribed to this thread. > Reply to this email directly, view it on GitHub > <#2>, or unsubscribe > < https://github.com/notifications/unsubscribe-auth/AB4UC5ZRFOXLAJHKTCB344TSSDXP3ANCNFSM4UFY5MEA > > . > — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#2 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AR5FMS7X3IOYHXW7PCDHUX3STOYJLANCNFSM4UFY5MEA> .

[eddy-v]

Finding interesting (not boring) test data for teaching/practicing sql/sqlite3+python can be challenging, This is a great source and thank you for offering it.

[planetsig]

Eddy, Thanks for the ping, That is what it is for. These are some of the things I have learned in using wild data. Siggy's General theorems of/on Wild Data. 1. This is real human data and there are complex feelings and emotions with/in every bit. 2. There is at least 20% deceit either propagated, learned, or direct in all wild data. 3. Error margins are not what they seem - There is gold in things that do not fit. Look closer recoorelate. 4. Always drill down questions through the data model; the answer is there somewhere. 5. Its Ok to have emotions, or anger. With that learn something and make a difference. My favorite one liner on the NUFOC data set was "mommy there here." Best of Holidays and the Greatest New Year to you and yours, Sig

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On Fri, Dec 17, 2021 at 6:04 PM eddy vasile ***@***.***> wrote: Thank you for offering an excellent and very interesting (not boring) data source for teaching sqlite3/python. — Reply to this email directly, view it on GitHub <#2 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AB4UC5Y54SMJTMCF5T43T43URPFY3ANCNFSM4UFY5MEA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you commented.Message ID: ***@***.***>

[eddy-v]

Took a quick look. 88,875 records. 10,450 in California (12%). As expected :-) Thanks again

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On Dec 17, 2021, at 4:33 PM, sigmond axel ***@***.***> wrote:  Eddy, Thanks for the ping, That is what it is for. These are some of the things I have learned in using wild data. Siggy's General theorems of/on Wild Data. 1. This is real human data and there are complex feelings and emotions with/in every bit. 2. There is at least 20% deceit either propagated, learned, or direct in all wild data. 3. Error margins are not what they seem - There is gold in things that do not fit. Look closer recoorelate. 4. Always drill down questions through the data model; the answer is there somewhere. 5. Its Ok to have emotions, or anger. With that learn something and make a difference. My favorite one liner on the NUFOC data set was "mommy there here." Best of Holidays and the Greatest New Year to you and yours, Sig On Fri, Dec 17, 2021 at 6:04 PM eddy vasile ***@***.***> wrote: > Thank you for offering an excellent and very interesting (not boring) data > source for teaching sqlite3/python. > > — > Reply to this email directly, view it on GitHub > <#2 (comment)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AB4UC5Y54SMJTMCF5T43T43URPFY3ANCNFSM4UFY5MEA> > . > Triage notifications on the go with GitHub Mobile for iOS > <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> > or Android > <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. > > You are receiving this because you commented.Message ID: > ***@***.***> > — Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android. You are receiving this because you commented.

[planetsig]

The best way I can describe it all to keep it positive is that the wild data is like Modern Art. At first it seems illogical yet after a while it makes sense. Best of holidays to you and yours. On Fri, Dec 17, 2021 at 7:46 PM eddy vasile ***@***.***> wrote:

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Took a quick look. 88,875 records. 10,450 in California (12%). As expected :-) Thanks again > On Dec 17, 2021, at 4:33 PM, sigmond axel ***@***.***> wrote: > >  > Eddy, Thanks for the ping, That is what it is for. > > These are some of the things I have learned in using wild data. > > Siggy's General theorems of/on Wild Data. > > 1. This is real human data and there are complex feelings and emotions > with/in every bit. > 2. There is at least 20% deceit either propagated, learned, or direct in > all wild data. > 3. Error margins are not what they seem - There is gold in things that do > not fit. Look closer recoorelate. > 4. Always drill down questions through the data model; the answer is there > somewhere. > 5. Its Ok to have emotions, or anger. With that learn something and make a > difference. > > My favorite one liner on the NUFOC data set was "mommy there here." > > Best of Holidays and the Greatest New Year to you and yours, Sig > > > On Fri, Dec 17, 2021 at 6:04 PM eddy vasile ***@***.***> > wrote: > > > Thank you for offering an excellent and very interesting (not boring) data > > source for teaching sqlite3/python. > > > > — > > Reply to this email directly, view it on GitHub > > < #2 (comment)>, > > or unsubscribe > > < https://github.com/notifications/unsubscribe-auth/AB4UC5Y54SMJTMCF5T43T43URPFY3ANCNFSM4UFY5MEA > > > . > > Triage notifications on the go with GitHub Mobile for iOS > > < https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 > > > or Android > > < https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub >. > > > > You are receiving this because you commented.Message ID: > > ***@***.***> > > > — > Reply to this email directly, view it on GitHub, or unsubscribe. > Triage notifications on the go with GitHub Mobile for iOS or Android. > You are receiving this because you commented. — Reply to this email directly, view it on GitHub <#2 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AB4UC53QDMU5WQOTVHBOJA3URPRXVANCNFSM4UFY5MEA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you commented.Message ID: ***@***.***>