Just for fun. What are your two way closest samples?

[East Anglian]

Yorkshire was found to have the highest percentage of Anglo-Saxon ancestry (41.17 per cent), while the East Midlands has the most Scandinavian ancestry (10.37 per cent)

Based on my results, I shall limit myself to the Yorkshire and East Midlands part of Danelaw.

I'd be cautious on that survey. Correct me if I am wrong but it was commissioned by Ancestry.com for publicity, based on results from their commercial present day population, during a period when Ancestry.com didn't seem to understand the differences between Britain and England judging on the labels they were applying those days. Then sensationalised in the Daily Mail tabloid. Ancestry have made a lot of improvements since then.  No AncientDNA was used. But I do love the suggestion that East Yorkshire appears very Danish, and East Anglia more Dutch. The Dutch link continued through the Medieval and into the Post Medieval as the Strangers.

To support your place name comment, a placename map

And a surname and placename map

That little cluster in Eastern most Norfolk, is the old isle of Flegg.  I've written extensively about that here:

https://genarchivist.com/showthread.php?tid=584

Otherwise, I see the old problems with these two populations, in that Eastern England sits in the North Sea (as do my results on a PCA) between 'British' and Continental / Scandinavian / Germanic, or even French. The tests are confused by our North Continental European immigration of the Early Medieval. Tests always give me big lumps of Scandinavian or Continental, that are not real in recent ancestry:

   

[JMcB]

Using Stefano’s sims:


0.014 JMcB = 74% ENG_Northamptonshire(n=1) + 26% BEL_Flemish-Brabant(n=2)
0.015 JMcB = 51% NED_Zeeland(n=3) + 49% IRE_Kildare(n=1)
0.015 JMcB = 70% ENG_Bedfordshire(n=3) + 30% IRE_Kildare(n=1)
0.015 JMcB = 55% ENG_Tyne_and_Wear(n=5) + 45% NED_Zeeland(n=3)
0.015 JMcB = 68% ENG_Northamptonshire(n=1) + 32% BEL_Hainaut(n=1)
0.015 JMcB = 73% ENG_Tyne_and_Wear(n=5) + 27% BEL_Hainaut(n=1)
0.015 JMcB = 79% ENG_Tyne_and_Wear(n=5) + 21% BEL_Flemish-Brabant(n=2)
0.015 JMcB = 59% IRE_Kildare(n=1) + 41% ENG_Middlesex(n=1)
0.015 JMcB = 50% ENG_Northamptonshire(n=1) + 50% NED_Zeeland(n=3)
0.015 JMcB = 63% WAL_Denbighshire(n=2) + 37% BEL_Hainaut(n=1

[okarinaofsteiner]

Ancient (from the default selections):

Code:

0.031 okarinaofsteiner_scaled = 89% CHN_Yellow_River_LN + 11% CHN_Qihe_11500BP
0.035 okarinaofsteiner_scaled = 97% CHN_Yellow_River_LN + 3% IND_Great_Andamanese_100BP
0.035 okarinaofsteiner_scaled = 98% CHN_Yellow_River_LN + 2% RUS_Yakutia_Ymyiakhtakh_LN
0.035 okarinaofsteiner_scaled = 97% CHN_Yellow_River_LN + 3% RUS_Baikal_N
0.035 okarinaofsteiner_scaled = 98% CHN_Yellow_River_LN + 2% RUS_Krasnoyarsk_BA
0.035 okarinaofsteiner_scaled = 97% CHN_Yellow_River_LN + 3% RUS_Shamanka_N
0.035 okarinaofsteiner_scaled = 99% CHN_Yellow_River_LN + 1% CHN_Tarim_EMBA1
0.035 okarinaofsteiner_scaled = 99% CHN_Yellow_River_LN + 1% BRA_Sumidouro_10100BP
0.035 okarinaofsteiner_scaled = 99% CHN_Yellow_River_LN + 1% RUS_AfontovaGora3

Modern (from here):

Code:

0.024 okarinaofsteiner_scaled = 64% __Han_Hubei + 36% _CHN_UYR_LN
0.025 okarinaofsteiner_scaled = 63% __Han_Guizhou + 37% _CHN_UYR_LN
0.026 okarinaofsteiner_scaled = 71% __Han_Zhejiang + 29% _CHN_UYR_LN
0.026 okarinaofsteiner_scaled = 56% __Han_Fujian + 44% _CHN_UYR_LN
0.026 okarinaofsteiner_scaled = 99% __Han_Shandong + 1% Papuan

all the other modern 2-way models that are closer than the closest ancient 2-way model

Show Content

Spoiler

Code:

0.027 okarinaofsteiner_scaled = 80% __Han_Shandong + 20% __Han_Guizhou
0.027 okarinaofsteiner_scaled = 98% __Han_Jiangsu + 2% Papuan
0.027 okarinaofsteiner_scaled = 84% __Han_Shandong + 16% __Han_Chongqing
0.027 okarinaofsteiner_scaled = 96% __Han_Shandong + 4% _Mlabri
0.027 okarinaofsteiner_scaled = 90% __Han_Zhejiang + 10% _Amur_River_EN
0.027 okarinaofsteiner_scaled = 79% __Han_Jiangsu + 21% _CHN_UYR_LN
0.027 okarinaofsteiner_scaled = 91% __Han_Shandong + 9% __Han_Guangxi_all
0.027 okarinaofsteiner_scaled = 94% __Han_Shandong + 6% _Dai
0.027 okarinaofsteiner_scaled = 81% __Han_Shandong + 19% __Han_Hubei
0.027 okarinaofsteiner_scaled = 59% __Han_Chongqing + 41% _CHN_UYR_LN
0.027 okarinaofsteiner_scaled = 95% __Han_Shandong + 5% _Wa
0.027 okarinaofsteiner_scaled = 77% __Han_Shandong + 23% __Han_Zhejiang
0.027 okarinaofsteiner_scaled = 83% __Han_Shandong + 17% __Han_Sichuan
0.027 okarinaofsteiner_scaled = 92% __Han_Shandong + 8% __Han_Guangdong
0.027 okarinaofsteiner_scaled = 97% __Han_Shandong + 3% _Igorot
0.027 okarinaofsteiner_scaled = 89% __Han_Shandong + 11% __Han_Fujian
0.028 okarinaofsteiner_scaled = 74% __Han_Shandong + 26% __Han_Jiangsu
0.028 okarinaofsteiner_scaled = 89% __Han_Zhejiang + 11% _Nanai
0.028 okarinaofsteiner_scaled = 99% __Han_Shandong + 1% IDN_Leang_Panninge_7100BP
0.028 okarinaofsteiner_scaled = 65% __Han_Shanxi + 35% __Han_Guizhou
0.028 okarinaofsteiner_scaled = 66% __Han_Sichuan + 34% _CHN_UYR_LN
0.028 okarinaofsteiner_scaled = 93% __Han_Jiangsu + 7% _Amur_River_EN
0.028 okarinaofsteiner_scaled = 54% __Han_Shanxi + 46% __Han_Zhejiang
0.028 okarinaofsteiner_scaled = 63% __Han_Shanxi + 37% __Han_Hubei
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% _Amur_River_EN
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% ^_Mixtec
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% __Han_NChina_HGDP
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% __Han_Shanxi
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% __Han_Henan
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% _CHN_UYR_LN
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% _CHN_YR_LN
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% _Nanai
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% _Nivkh
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% Simulated_AASI_3
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% LAO_Hoabinhian_La368
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% Jomon_avg
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% ^Yamnaya_RUS_Samara
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% ^IRN_Ganj_Dareh_N
0.028 okarinaofsteiner_scaled = 100% __Han_Shandong + 0% ^_Yoruba
0.028 okarinaofsteiner_scaled = 70% __Han_Shanxi + 30% __Han_Chongqing
0.028 okarinaofsteiner_scaled = 54% _CHN_UYR_LN + 46% __Han_Guangdong
0.028 okarinaofsteiner_scaled = 56% __Han_Jiangsu + 44% __Han_Shanxi
0.028 okarinaofsteiner_scaled = 93% __Han_Jiangsu + 7% _Nanai
0.029 okarinaofsteiner_scaled = 57% __Han_Jiangsu + 43% __Han_NChina_HGDP
0.029 okarinaofsteiner_scaled = 54% __Han_NChina_HGDP + 46% __Han_Zhejiang
0.029 okarinaofsteiner_scaled = 97% __Han_Jiangsu + 3% IDN_Leang_Panninge_7100BP
0.029 okarinaofsteiner_scaled = 56% _CHN_UYR_LN + 44% __Han_Guangxi_all
0.029 okarinaofsteiner_scaled = 93% __Han_Jiangsu + 7% _Nivkh
0.029 okarinaofsteiner_scaled = 64% __Han_Shanxi + 36% __Han_Sichuan
0.029 okarinaofsteiner_scaled = 67% __Han_Henan + 33% __Han_Guizhou
0.029 okarinaofsteiner_scaled = 73% __Han_Shanxi + 27% __Han_Fujian
0.029 okarinaofsteiner_scaled = 64% __Han_NChina_HGDP + 36% __Han_Hubei
0.029 okarinaofsteiner_scaled = 82% __Han_Shanxi + 18% __Han_Guangxi_all
0.029 okarinaofsteiner_scaled = 89% __Han_Zhejiang + 11% _Nivkh
0.029 okarinaofsteiner_scaled = 67% __Han_NChina_HGDP + 33% __Han_Guizhou
0.029 okarinaofsteiner_scaled = 82% __Han_Shanxi + 18% __Han_Guangdong
0.029 okarinaofsteiner_scaled = 62% _CHN_YR_LN + 38% __Han_Guizhou
0.030 okarinaofsteiner_scaled = 57% __Han_Henan + 43% __Han_Zhejiang
0.030 okarinaofsteiner_scaled = 66% __Han_Henan + 34% __Han_Hubei
0.030 okarinaofsteiner_scaled = 59% __Han_Jiangsu + 41% __Han_Henan
0.030 okarinaofsteiner_scaled = 98% __Han_Jiangsu + 2% ^_Mixtec
0.030 okarinaofsteiner_scaled = 74% __Han_Henan + 26% __Han_Chongqing
0.030 okarinaofsteiner_scaled = 73% __Han_NChina_HGDP + 27% __Han_Chongqing
0.030 okarinaofsteiner_scaled = 89% __Han_Shanxi + 11% _Dai
0.030 okarinaofsteiner_scaled = 93% __Han_Shanxi + 7% _Igorot
0.030 okarinaofsteiner_scaled = 68% __Han_Henan + 32% __Han_Sichuan
0.030 okarinaofsteiner_scaled = 68% __Han_Jiangsu + 32% _CHN_YR_LN
0.030 okarinaofsteiner_scaled = 75% __Han_NChina_HGDP + 25% __Han_Fujian
0.030 okarinaofsteiner_scaled = 85% __Han_Henan + 15% __Han_Guangxi_all
0.030 okarinaofsteiner_scaled = 66% __Han_NChina_HGDP + 34% __Han_Sichuan

[ESPLover]

Modern Average:

Code:

0.015 esp_scaled = 54% Brahmin_Gujarat_Bardai + 46% Uttar_Pradesh_Scheduled_Castes
0.016 esp_scaled = 64% Brahmin_Gujarat_Bardai + 36% Chamar_Uttar_Pradesh
0.016 esp_scaled = 68% Brahmin_Gujarat_Bardai + 32% Hakkipikki
0.016 esp_scaled = 70% Brahmin_Gujarat_Bardai + 30% Mala
0.016 esp_scaled = 68% Brahmin_Gujarat_Bardai + 32% North_Kannadi
0.017 esp_scaled = 59% Brahmin_Gujarat_Bardai + 41% Punjabi_Christian_India
0.017 esp_scaled = 56% Chamar_Uttar_Pradesh_o + 44% Brahmin_Gujarat_Bardai
0.017 esp_scaled = 59% Brahmin_Gujarat_Bardai + 41% Konkani_Christian_B
0.017 esp_scaled = 51% Kamboj_o + 49% Brahmin_Gujarat_Bardai
0.017 esp_scaled = 54% Brahmin_Gujarat_Bardai + 46% Koli_Gujarat
0.017 esp_scaled = 52% Dharkar + 48% Brahmin_Gujarat_Bardai
0.018 esp_scaled = 63% Pillai_Tamil + 37% Jat_Haryana
0.018 esp_scaled = 58% Piramalai_Kallar + 42% Jat_Haryana
0.018 esp_scaled = 60% Brahmin_Gujarat_Bardai + 40% Dusadh
0.018 esp_scaled = 67% Brahmin_Gujarat_Bardai + 33% Pallan
0.018 esp_scaled = 66% Brahmin_Gujarat_Bardai + 34% Sakilli
0.018 esp_scaled = 54% Brahmin_Gujarat_Bardai + 46% Pillai_Tamil
0.019 esp_scaled = 61% Brahmin_Rajasthan + 39% Chamar_Uttar_Pradesh
0.019 esp_scaled = 55% Kamboj_o + 45% Brahmin_Rajasthan
0.019 esp_scaled = 61% Brahmin_Gujarat_Bardai + 39% Maratha
0.019 esp_scaled = 58% Brahmin_Gujarat_Bardai + 42% Yadav_Telugu
0.019 esp_scaled = 66% Pillai_Tamil + 34% Ror
0.019 esp_scaled = 66% Brahmin_Gujarat_Bardai + 34% Relli
0.019 esp_scaled = 69% Brahmin_Gujarat_Bardai + 31% Madiga
0.019 esp_scaled = 63% Pillai_Tamil + 37% Jat_Uttar_Pradesh
0.019 esp_scaled = 51% Brahmin_Rajasthan + 49% Uttar_Pradesh_Scheduled_Castes
0.019 esp_scaled = 75% Brahmin_Gujarat_Bardai + 25% Irula
0.019 esp_scaled = 59% Brahmin_Gujarat_Bardai + 41% Piramalai_Kallar
0.019 esp_scaled = 60% Chamar_Uttar_Pradesh_o + 40% Brahmin_Rajasthan
0.019 esp_scaled = 58% Kanjar + 42% Brahmin_Gujarat_Bardai

Modern + Ancient Average:

Code:

0.015 esp_scaled = 54% Brahmin_Gujarat_Bardai + 46% Uttar_Pradesh_Scheduled_Castes
0.016 esp_scaled = 64% Brahmin_Gujarat_Bardai + 36% Chamar_Uttar_Pradesh
0.016 esp_scaled = 68% Brahmin_Gujarat_Bardai + 32% Hakkipikki
0.016 esp_scaled = 70% Brahmin_Gujarat_Bardai + 30% Mala
0.016 esp_scaled = 68% Brahmin_Gujarat_Bardai + 32% North_Kannadi
0.017 esp_scaled = 59% Brahmin_Gujarat_Bardai + 41% Punjabi_Christian_India
0.017 esp_scaled = 56% Chamar_Uttar_Pradesh_o + 44% Brahmin_Gujarat_Bardai
0.017 esp_scaled = 59% Brahmin_Gujarat_Bardai + 41% Konkani_Christian_B
0.017 esp_scaled = 51% Kamboj_o + 49% Brahmin_Gujarat_Bardai
0.017 esp_scaled = 54% Brahmin_Gujarat_Bardai + 46% Koli_Gujarat
0.017 esp_scaled = 52% Dharkar + 48% Brahmin_Gujarat_Bardai
0.018 esp_scaled = 63% Pillai_Tamil + 37% Jat_Haryana
0.018 esp_scaled = 58% Piramalai_Kallar + 42% Jat_Haryana
0.018 esp_scaled = 60% Brahmin_Gujarat_Bardai + 40% Dusadh
0.018 esp_scaled = 82% Pillai_Tamil + 18% Kazakhstan_EBA_Yamnaya.SG
0.018 esp_scaled = 82% Pillai_Tamil + 18% Mongolia_Chalcolithic_Afanasievo_1
0.018 esp_scaled = 67% Brahmin_Gujarat_Bardai + 33% Pallan
0.018 esp_scaled = 66% Brahmin_Gujarat_Bardai + 34% Sakilli
0.018 esp_scaled = 83% Pillai_Tamil + 17% Russia_Afanasievo
0.018 esp_scaled = 54% Brahmin_Gujarat_Bardai + 46% Pillai_Tamil
0.019 esp_scaled = 61% Brahmin_Rajasthan + 39% Chamar_Uttar_Pradesh
0.019 esp_scaled = 55% Kamboj_o + 45% Brahmin_Rajasthan
0.019 esp_scaled = 61% Brahmin_Gujarat_Bardai + 39% Maratha
0.019 esp_scaled = 58% Brahmin_Gujarat_Bardai + 42% Yadav_Telugu
0.019 esp_scaled = 83% Pillai_Tamil + 17% China_Xinjiang_G218_BA_Afanasievo_oWestEurasia
0.019 esp_scaled = 82% Pillai_Tamil + 18% Russia_Caucasus_EBA_Yamnaya
0.019 esp_scaled = 82% Pillai_Tamil + 18% Russia_Steppe_Catacomb
0.019 esp_scaled = 66% Pillai_Tamil + 34% Ror
0.019 esp_scaled = 66% Brahmin_Gujarat_Bardai + 34% Relli
0.019 esp_scaled = 83% Pillai_Tamil + 17% Russia_Samara_EBA_Yamnaya

[Dewsloth]

Moderns:

Code:

0.014 Dewsloth_scaled = 54% Druze + 46% BelgianB
0.014 Dewsloth_scaled = 53% Druze + 47% French_Paris
0.014 Dewsloth_scaled = 52% Druze + 48% Swiss_German
0.014 Dewsloth_scaled = 58% Druze + 42% French_Brittany
0.014 Dewsloth_scaled = 54% Druze + 46% BelgianC
0.014 Dewsloth_scaled = 53% Druze + 47% French_Nord
0.014 Dewsloth_scaled = 55% Druze + 45% BelgianA
0.014 Dewsloth_scaled = 60% French_Provence + 40% Kurdish_Jew
0.015 Dewsloth_scaled = 55% French_Auvergne + 45% Kurdish_Jew
0.015 Dewsloth_scaled = 51% Lebanese_Druze + 49% Swiss_German
0.015 Dewsloth_scaled = 59% Italian_Aosta_Valley + 41% Kurdish_Jew
0.015 Dewsloth_scaled = 59% Druze + 41% English_Cornwall
0.015 Dewsloth_scaled = 52% Druze + 48% French_Alsace
0.015 Dewsloth_scaled = 59% Druze + 41% English
0.015 Dewsloth_scaled = 65% Cypriot + 35% Scottish
0.015 Dewsloth_scaled = 53% Lebanese_Druze + 47% BelgianB
0.015 Dewsloth_scaled = 54% Druze + 46% French_Seine-Maritime
0.015 Dewsloth_scaled = 59% Druze + 41% Welsh
0.015 Dewsloth_scaled = 53% Alawite + 47% BelgianB
0.015 Dewsloth_scaled = 54% French_Occitanie + 46% Kurdish_Jew
0.015 Dewsloth_scaled = 64% Cypriot + 36% Dutch
0.015 Dewsloth_scaled = 55% Druze + 45% French_Pas-de-Calais
0.015 Dewsloth_scaled = 53% Lebanese_Druze + 47% French_Nord
0.015 Dewsloth_scaled = 65% Cypriot + 35% Orcadian
0.016 Dewsloth_scaled = 67% Cypriot + 33% Norwegian

Mom's is pretty good - she has one Armenian great grandparent and seven that are Lebanese Christian.  The slight shift in totality resembles Lebanese Druze (see also above)

Code:

0.015 DewslothMom_scaled = 73% Palestinian_Beit_Sahour + 27% Greek_Trabzon
0.015 DewslothMom_scaled = 75% Druze + 25% Palestinian_Beit_Sahour
0.016 DewslothMom_scaled = 68% Palestinian_Beit_Sahour + 32% Armenian_Gesaria
0.016 DewslothMom_scaled = 89% Lebanese_Christian + 11% Abkhasian_Gudauta
0.016 DewslothMom_scaled = 89% Lebanese_Christian + 11% Georgian_Ratcha
0.016 DewslothMom_scaled = 85% Lebanese_Christian + 15% Georgian_Samtckhe
0.016 DewslothMom_scaled = 74% Lebanese_Christian + 26% Armenian_Aintab
0.016 DewslothMom_scaled = 88% Lebanese_Christian + 12% Georgian_Ajar
0.016 DewslothMom_scaled = 81% Lebanese_Christian + 19% Georgian_Meskheti
0.016 DewslothMom_scaled = 87% Lebanese_Christian + 13% Ahiska
0.016 DewslothMom_scaled = 89% Lebanese_Christian + 11% Georgian_West
0.016 DewslothMom_scaled = 66% Palestinian_Beit_Sahour + 34% Greek_Cappadocia
0.016 DewslothMom_scaled = 73% Druze + 27% Lebanese_Christian
0.016 DewslothMom_scaled = 78% Lebanese_Christian + 22% Armenian_Gesaria
0.016 DewslothMom_scaled = 74% Palestinian_Beit_Sahour + 26% Turkish_Trabzon
0.016 DewslothMom_scaled = 86% Lebanese_Christian + 14% Georgian_Laz
0.016 DewslothMom_scaled = 90% Lebanese_Christian + 10% Georgian_Megr
0.016 DewslothMom_scaled = 84% Lebanese_Christian + 16% Armenian_Hemsheni

Dad's is pretty confused - we have no Roma  (he's West German + Colonial British + small amount of Rhine Ashkenazi= my BelgianB/FrenchNord above)
0.032 DewslothDad_scaled = 65% French_Seine-Maritime + 35% Roma_Madrid
0.032 DewslothDad_scaled = 81% Swiss_German + 19% Parsi_India
0.032 DewslothDad_scaled = 67% Swiss_German + 33% Roma_Madrid
0.032 DewslothDad_scaled = 79% French_Occitanie + 21% Parsi_India
0.033 DewslothDad_scaled = 81% Swiss_German + 19% Parsi_Pakistan
0.033 DewslothDad_scaled = 79% French_Occitanie + 21% Parsi_Pakistan
0.033 DewslothDad_scaled = 64% BelgianB + 36% Roma_Madrid

[Capitalis]

Top 10 ancient individuals using G25 scaled
0.012 Capitalis = 70% England_EarlyMedieval_Saxon:I20670 + 30% Czech_IA_Hallstatt:I14980
0.013 Capitalis = 74% Netherlands_Groningen_Saxon_Medieval:GRO012.A + 26% Czech_IA_LaTene:I16269
0.013 Capitalis = 71% England_EarlyMedieval_Saxon:I20670 + 29% Romania_BA_Arman:I10558
0.013 Capitalis = 57% England_EarlyMedieval_Saxon:I20670 + 43% Czech_LBA_Knoviz:I13799
0.013 Capitalis = 74% England_Saxon.SG:HAD002.SG + 26% Hungary_IA_LaTene:I25512
0.014 Capitalis = 70% England_EarlyMedieval_Saxon:I20670 + 30% Serbia_Mokrin_EBA_Maros:I23207
0.014 Capitalis = 58% England_EarlyMedieval_Saxon:I20670 + 42% Czech_IA_LaTene:I15047
0.014 Capitalis = 73% England_EarlyMedieval_Saxon:I20670 + 27% Hungary_MBA_Vatya.SG:RISE480_noUDG.SG
0.014 Capitalis = 78% England_EarlyMedieval_Saxon:I20670 + 22% Bulgaria_EBA:I2180
0.014 Capitalis = 77% England_EarlyMedieval_Saxon:I20670 + 23% Romania_BA_Arman:I10481

Ignoring the problem of mixing time periods, I conducted a sanity check on the I20670-based models, plotting the Gretzinger et al. England samples alongside the later England Iron Age and England Viking samples as a backdrop. This is a modern Europe PCA but the moderns are hidden.



The model isn't producing a two sample output that passes through me, and in fact misses quite badly. I used the default settings.

Apologies for spoiling the fun. :-)

[Nqp15hhu]

Modern:

1 0.014 61% Irish:Irish52 + 39% English:England15
2 0.016 57% Irish:518 + 43% English_Cornwall:HG00249
3 0.016 57% Irish:Irish53 + 43% Irish:512
4 0.017 55% Scottish:Scottish12 + 45% English:England15
5 0.018 61% Scottish:Scottish27 + 39% Irish:516
6 0.018 55% Irish:Irish43 + 45% Irish:520
7 0.018 58% English_Cornwall:HG00243 + 42% Irish:Irish54
8 0.018 51% Irish:Irish19 + 49% English_Cornwall:HG00249
9 0.019 50% English_Cornwall:HG00249 + 50% Irish:Irish41
10 0.019 76% Welsh:WAL004 + 24% Irish:515
11 0.019 55% English:English6 + 45% Irish:513
12 0.019 60% Irish:Irish54 + 40% Irish:517
13 0.019 58% English:England15 + 42% Irish:520
14 0.019 60% Irish:Irish39 + 40% Irish:Irish30
15 0.019 56% Irish:509 + 44% Welsh:WalesCHF12
16 0.019 53% Irish:532 + 47% Irish:529
17 0.019 59% Scottish:Scottish13 + 41% Irish:Irish18
18 0.019 55% Irish:516 + 45% Irish:Irish18
19 0.020 61% Irish:Irish50 + 39% Irish:514
20 0.020 56% Irish:Irish57 + 44% Irish:514

[FitzDarcy]

0.034 Kevin= 65% DEU_Karsdorf_LN + 35% England_N (Neolithic England and Corded Ware Karsdorf Saxony Germany)
0.036 Kevin _Scaled = 53% Yamnaya_RUS_Samara + 47% England_N
0.037 Kevin= 85% POL_Unetice_EBA + 15% England_N
0.039 Kevin_scaled = 88% POL_Unetice_EBA + 12% TUR_Pinarbasi_HG
0.041 Kevin__scaled = 90% POL_Unetice_EBA + 10% DEU_LBK_KD
0.041 Kevin_scaled = 90% POL_Unetice_EBA + 10% TUR_Barcin_N
0.044 Kevin_scaled = 72% Corded_Ware_Baltic + 28% England_N
0.045 Kevin_scaled = 95% POL_Unetice_EBA + 5% Levant_Natufian
0.045 Kevin_scaled = 78% Corded_Ware_Baltic + 22% DEU_LBK_KD


Modern:
392,941 models (0.74 s):
0.026 Kevscaled = 82% Norwegian + 18% Spanish_Soria
0.027 Kevscaled = 82% Norwegian + 18% Italian_Aosta_Valley
0.027 Kevscaled = 98% Dutch + 2% Greenlander_East
0.027 Kevscaled = 98% Dutch + 2% Cree
0.027 Kevscaled = 99% Dutch + 1% Nahua
0.027 Kevscaled = 99% Dutch + 1% Eskimo_Naukan
0.027 Kevscaled = 99% Dutch + 1% Pima
0.027 Kevscaled = 99% Dutch + 1% Chipewyan
0.027 Kevscaled = 99% Dutch + 1% Amerindian_North

[mjaguk]

I have G25 coordinates from DNAGenics (LivingDNA raw data) and from Davidski (23andMe raw data). They vary quite a lot, as you can see here.

Ancient Scaled Averages - using my G25 from DNAGenics:

0.015 mjag_LDNA_scaled = 77% Russia_Viking.SG + 23% Russia_IA_Ingria.SG
0.015 mjag_LDNA_scaled = 80% Russia_Viking.SG + 20% Lithuania_Marvele_Roman.SG
0.015 mjag_LDNA_scaled = 56% Slovakia_Poprad.SG + 44% Russia_IA_Ingria.SG
0.016 mjag_LDNA_scaled = 77% Russia_Viking.SG + 23% Lithuania_BA
0.016 mjag_LDNA_scaled = 68% Sweden_Viking.SG + 32% Estonia_Medieval.SG
0.016 mjag_LDNA_scaled = 58% Hungary_Langobard.SG + 42% Russia_IA_Ingria.SG
0.016 mjag_LDNA_scaled = 60% Estonia_Medieval.SG + 40% England_LBA_highEEF
0.016 mjag_LDNA_scaled = 57% Russia_Viking.SG + 43% Poland_Viking.SG
0.016 mjag_LDNA_scaled = 71% Russia_Viking.SG + 29% Kazakhstan_GoldenHordeEuro.SG
0.016 mjag_LDNA_scaled = 77% Sweden_Viking.SG + 23% Serbia_Viminacium_Roman.SG
0.016 mjag_LDNA_scaled = 63% Denmark_Zealand_Saxon_Medieval + 37% Russia_IA_Ingria.SG
0.016 mjag_LDNA_scaled = 52% Germany_EarlyMedieval.SG + 48% Estonia_Medieval.SG
0.016 mjag_LDNA_scaled = 75% Sweden_Viking.SG + 25% Russia_IA_Ingria.SG
0.016 mjag_LDNA_scaled = 83% Russia_Viking.SG + 17% Latvia_BA
0.016 mjag_LDNA_scaled = 51% Sweden_Viking.SG + 49% Russia_Viking_o.SG
0.016 mjag_LDNA_scaled = 84% Russia_Viking.SG + 16% Estonia_BA.SG
0.017 mjag_LDNA_scaled = 82% Russia_Viking.SG + 18% Lithuania_Bailuliai_BarrowCulture.SG
0.017 mjag_LDNA_scaled = 61% Denmark_Viking.SG + 39% Russia_IA_Ingria.SG
0.017 mjag_LDNA_scaled = 55% Denmark_Zealand_Saxon_Medieval + 45% Estonia_Medieval.SG
0.017 mjag_LDNA_scaled = 64% Germany_EarlyMedieval.SG + 36% Serbia_Viminacium_Roman.SG
0.017 mjag_LDNA_scaled = 81% Russia_Viking.SG + 19% Serbia_Viminacium_Roman.SG
0.017 mjag_LDNA_scaled = 56% Estonia_Medieval.SG + 44% England_Viking_o.SG
0.017 mjag_LDNA_scaled = 60% Germany_Schleswig_Saxon_LMedieval + 40% Serbia_Viminacium_Roman.SG
0.017 mjag_LDNA_scaled = 72% Russia_Viking.SG + 28% Estonia_Medieval.SG
0.017 mjag_LDNA_scaled = 75% Russia_Viking_o.SG + 25% England_LIA_o
0.017 mjag_LDNA_scaled = 66% Denmark_Zealand_Saxon_Medieval + 34% Serbia_Viminacium_Roman.SG
0.017 mjag_LDNA_scaled = 66% Denmark_Zealand_Saxon_Medieval + 34% Lithuania_Marvele_Roman.SG
0.017 mjag_LDNA_scaled = 68% Russia_Viking_o.SG + 32% Hungary_Langobard.SG
0.017 mjag_LDNA_scaled = 71% Denmark_Zealand_Saxon_Medieval + 29% Estonia_BA.SG
0.017 mjag_LDNA_scaled = 57% Italy_North_EarlyMedieval_Langobards_1 + 43% Serbia_Viminacium_Roman.SG



Ancient Scaled Averages - using my Davidski G25:

0.015 mjag_scaled = 56% England_IA_EarlyMedieval + 44% Serbia_Viminacium_Roman.SG
0.016 mjag_scaled = 57% Germany_Schleswig_Saxon_LMedieval + 43% Serbia_Viminacium_Roman.SG
0.016 mjag_scaled = 53% Scotland_Viking.SG + 47% Serbia_Viminacium_Roman.SG
0.016 mjag_scaled = 57% Faroes_EarlyModern.SG + 43% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 61% Denmark_Viking.SG + 39% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 60% Germany_EarlyMedieval.SG + 40% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 59% England_EarlyMedieval_Saxon + 41% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 61% Denmark_Viking.SG + 39% Lithuania_Marvele_Roman.SG
0.017 mjag_scaled = 63% England_IA_EarlyMedieval + 37% Estonia_BA.SG
0.017 mjag_scaled = 73% Sweden_Viking.SG + 27% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 75% Russia_Viking.SG + 25% Lithuania_Marvele_Roman.SG
0.017 mjag_scaled = 54% Italy_North_EarlyMedieval_Langobards_1 + 46% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 57% England_IA_EarlyMedieval + 43% Lithuania_Marvele_Roman.SG
0.017 mjag_scaled = 58% Germany_Schleswig_Saxon_LMedieval + 42% Lithuania_Marvele_Roman.SG
0.017 mjag_scaled = 63% Denmark_Zealand_Saxon_Medieval + 37% Lithuania_Marvele_Roman.SG
0.017 mjag_scaled = 58% Netherlands_Groningen_Saxon_Medieval + 42% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 52% Scotland_Viking_o.SG + 48% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 58% England_Saxon.SG + 42% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 59% England_Saxon + 41% Serbia_Viminacium_Roman.SG
0.017 mjag_scaled = 73% Sweden_Viking.SG + 27% Lithuania_Marvele_Roman.SG
0.018 mjag_scaled = 54% England_MIA_LIA + 46% Serbia_Viminacium_Roman.SG
0.018 mjag_scaled = 64% Germany_Schleswig_Saxon_LMedieval + 36% Estonia_BA.SG
0.018 mjag_scaled = 57% England_EarlyMedieval + 43% Serbia_Viminacium_Roman.SG
0.018 mjag_scaled = 57% Germany_Anderten_Saxon_Medieval + 43% Lithuania_Marvele_Roman.SG
0.018 mjag_scaled = 67% Denmark_Viking.SG + 33% Estonia_BA.SG
0.018 mjag_scaled = 59% Iceland_Viking.SG + 41% Serbia_Viminacium_Roman.SG
0.018 mjag_scaled = 56% England_IA_Roman.SG + 44% Serbia_Viminacium_Roman.SG
0.018 mjag_scaled = 61% Germany_EarlyMedieval.SG + 39% Czech_EBA_Unetice_o
0.018 mjag_scaled = 59% Greenland_EarlyNorse.SG + 41% Serbia_Viminacium_Roman.SG
0.018 mjag_scaled = 62% England_IA_EarlyMedieval + 38% Latvia_BA

[mjaguk]

Modern Scaled Averages - using my Davidski G25

0.016 mjag_scaled = 51% Latvian + 49% Dutch
0.016 mjag_scaled = 53% Lithuanian_SZ + 47% Orcadian
0.016 mjag_scaled = 55% Lithuanian_RA + 45% Orcadian
0.017 mjag_scaled = 54% Lithuanian_SZ + 46% Scottish
0.017 mjag_scaled = 52% Latvian + 48% Orcadian
0.017 mjag_scaled = 53% Latvian + 47% Scottish
0.017 mjag_scaled = 53% Lithuanian_PZ + 47% Dutch
0.017 mjag_scaled = 54% Latvian + 46% English
0.017 mjag_scaled = 60% Lithuanian_VA + 40% Orcadian
0.017 mjag_scaled = 56% Lithuanian_VA + 44% Norwegian
0.017 mjag_scaled = 52% Lithuanian_SZ + 48% Dutch
0.017 mjag_scaled = 62% Lithuanian_VA + 38% Irish
0.017 mjag_scaled = 55% Latvian + 45% English_Cornwall
0.017 mjag_scaled = 51% Lithuanian_RA + 49% Norwegian
0.017 mjag_scaled = 61% Lithuanian_VA + 39% Scottish
0.017 mjag_scaled = 56% Lithuanian_RA + 44% Scottish
0.017 mjag_scaled = 55% Lithuanian_SZ + 45% English
0.017 mjag_scaled = 57% Lithuanian_RA + 43% Irish
0.017 mjag_scaled = 52% Danish + 48% Lithuanian_SZ
0.017 mjag_scaled = 53% Lithuanian_VZ + 47% Norwegian
0.017 mjag_scaled = 51% Norwegian + 49% Lithuanian_SZ
0.017 mjag_scaled = 57% Lithuanian_VA + 43% Icelandic
0.017 mjag_scaled = 53% Danish + 47% Latvian
0.017 mjag_scaled = 58% Lithuanian_PA + 42% Norwegian
0.017 mjag_scaled = 55% Lithuanian_SZ + 45% Irish
0.017 mjag_scaled = 52% Lithuanian_VZ + 48% Danish
0.017 mjag_scaled = 56% Lithuanian_VZ + 44% Dutch
0.017 mjag_scaled = 59% Lithuanian_PA + 41% Icelandic
0.017 mjag_scaled = 55% Lithuanian_VA + 45% Danish
0.017 mjag_scaled = 50% Danish + 50% Lithuanian_RA



Modern Scaled Averages - using my DNAGenics G25

0.015 mjag_LDNA_scaled = 53% German_Hamburg + 47% Estonian
0.015 mjag_LDNA_scaled = 58% Estonian + 42% Dutch
0.015 mjag_LDNA_scaled = 59% Swedish + 41% Lithuanian_VA
0.015 mjag_LDNA_scaled = 63% Estonian + 37% French_Brittany
0.015 mjag_LDNA_scaled = 57% Swedish + 43% Russian_Pskov
0.015 mjag_LDNA_scaled = 56% Swedish + 44% Lithuanian_PA
0.015 mjag_LDNA_scaled = 52% Lithuanian_VA + 48% Norwegian
0.015 mjag_LDNA_scaled = 54% Russian_Pskov + 46% Norwegian
0.015 mjag_LDNA_scaled = 57% Estonian + 43% German
0.015 mjag_LDNA_scaled = 63% Swedish + 37% Lithuanian_RA
0.016 mjag_LDNA_scaled = 65% Estonian + 35% BelgianB
0.016 mjag_LDNA_scaled = 54% Russian_Pskov + 46% Danish
0.016 mjag_LDNA_scaled = 61% Estonian + 39% Welsh
0.016 mjag_LDNA_scaled = 67% Estonian + 33% BelgianC
0.016 mjag_LDNA_scaled = 68% Estonian + 32% French_Paris
0.016 mjag_LDNA_scaled = 62% Swedish + 38% Lithuanian_VZ
0.016 mjag_LDNA_scaled = 60% Estonian + 40% English
0.016 mjag_LDNA_scaled = 65% Estonian + 35% French_Pas-de-Calais
0.016 mjag_LDNA_scaled = 63% Estonian + 37% BelgianA
0.016 mjag_LDNA_scaled = 53% Norwegian + 47% Lithuanian_RA
0.016 mjag_LDNA_scaled = 53% Estonian + 47% Danish
0.016 mjag_LDNA_scaled = 59% Estonian + 41% Scottish
0.016 mjag_LDNA_scaled = 55% Lithuanian_PA + 45% Norwegian
0.016 mjag_LDNA_scaled = 66% Estonian + 34% French_Nord
0.016 mjag_LDNA_scaled = 59% Estonian + 41% Orcadian
0.016 mjag_LDNA_scaled = 67% Estonian + 33% Swiss_German
0.016 mjag_LDNA_scaled = 62% Estonian + 38% English_Cornwall
0.016 mjag_LDNA_scaled = 66% Swedish + 34% Lithuanian_SZ
0.016 mjag_LDNA_scaled = 52% Lithuanian_VA + 48% Danish
0.016 mjag_LDNA_scaled = 56% Swedish + 44% Belarusian

[Albruic]

I get pretty much the same thing on modern averages or ancient averages. The original one doesn't give me a good fit. 

0.012 Albruic = 92% Spanish_Mallorca + 8% Brazil_PrePottery_2200BP
0.012 Albruic = 92% Spanish_Mallorca + 8% Uruguay_Rocha_PreColonial.SG
0.012 Albruic = 92% Spanish_Mallorca + 8% PuertoRico_CanasColloresMonserrate_Ceramic
0.012 Albruic = 92% Spanish_Mallorca + 8% Cuba_GuayaboBlanco_Archaic
0.012 Albruic = 92% Spanish_Mallorca + 8% Bolivia_MonolitoDescabezado_Tiwanaku
0.012 Albruic = 92% Spanish_Mallorca + 8% USA_NM_Chaco
0.012 Albruic = 91% Spanish_Mallorca + 9% Bolivian_Pando
0.012 Albruic = 92% Spanish_Mallorca + 8% Chile_PuntaSantaAna_7300BP.SG
0.012 Albruic = 92% Spanish_Mallorca + 8% Cuba_Manuelito_Archaic
0.013 Albruic = 92% Spanish_Mallorca + 8% PuertoRico_LosIndios_Ceramic
0.013 Albruic = 92% Spanish_Mallorca + 8% PuertoRico_SantaElena_Ceramic
0.013 Albruic = 92% Spanish_Mallorca + 8% Belize_3600BP
0.013 Albruic = 92% Spanish_Mallorca + 8% PuertoRico_PuntaCandelero_Ceramic
0.013 Albruic = 92% Spanish_Mallorca + 8% Peru_LIP_Ychsma
0.013 Albruic = 92% Spanish_Mallorca + 8% Peru_Antaura_Tiwanaku


Ancient: 
0.019 Albruic = 92% Croatia_EIA + 8% Brazil_PrePottery_2200BP
0.019 Albruic = 91% Croatia_EIA + 9% PuertoRico_PasodelIndio_Ceramic_o
0.020 Albruic = 92% Croatia_EIA + 8% Uruguay_Rocha_PreColonial.SG
0.020 Albruic = 92% Croatia_EIA + 8% PuertoRico_LosIndios_Ceramic
0.020 Albruic = 92% Italy_LA_o1CentralEuropean.SG + 8% Brazil_PrePottery_2500BP
0.020 Albruic = 92% Croatia_EIA + 8% Bolivia_Pumapunku_Tiwanaku
0.020 Albruic = 92% Italy_LA_o1CentralEuropean.SG + 8% Uruguay_Rocha_PreColonial.SG
0.020 Albruic = 92% Croatia_EIA + 8% Brazil_Sumidouro_10400BP.SG
0.020 Albruic = 92% Croatia_EIA + 8% Bolivia_Putuni_Tiwanaku
0.020 Albruic = 92% Italy_LA_o1CentralEuropean.SG + 8% Brazil_PrePottery_2200BP
0.020 Albruic = 93% Croatia_EIA + 7% Chile_LIP_Pukara_600BP
0.020 Albruic = 92% Croatia_EIA + 8% Bolivia_MonolitoDescabezado_Tiwanaku
0.020 Albruic = 92% Croatia_EIA + 8% Brazil_Laranjal_6800BP
0.020 Albruic = 92% Croatia_EIA + 8% PuertoRico_CanasColloresMonserrate_Ceramic
0.020 Albruic = 92% Croatia_EIA + 8% Brazil_Sumidouro_10200BP.SG


Same goes with Mariopoulos collection. If I remove all Iberian samples this is what it looks like: 
0.018 Albruic = 92% French_Provence + 8% Bolivian_Pando
0.019 Albruic = 92% French_Provence + 8% Bolivian_Cochabamba
0.019 Albruic = 92% French_Provence + 8% Cachi
0.019 Albruic = 93% French_Provence + 7% Piapoco
0.019 Albruic = 92% French_Provence + 8% Quechua
0.019 Albruic = 93% French_Provence + 7% Surui
0.019 Albruic = 92% French_Provence + 8% Bolivian_LaPaz
0.019 Albruic = 93% French_Provence + 7% Wichi
0.019 Albruic = 92% French_Provence + 8% Colla
0.019 Albruic = 93% French_Provence + 7% Yukpa
0.019 Albruic = 93% French_Provence + 7% Aymara
etc etc

No Provence gives me this: 
0.022 Albruic = 91% Italian_Trentino_Alto_Adige + 9% Bolivian_Cochabamba
0.022 Albruic = 91% Italian_Trentino_Alto_Adige + 9% Bolivian_Pando
0.022 Albruic = 91% Italian_Trentino_Alto_Adige + 9% Quechua
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Piapoco
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Cachi
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Colla
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Zapotec
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Mixtec
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Huichol
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Wichi
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Nahua
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Mayan
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Pima
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Bolivian_LaPaz
0.022 Albruic = 92% Italian_Trentino_Alto_Adige + 8% Yukpa
Guess I need a 3-way tool for the perfect fit lol

[Kelmendasi]

Including Albanian average:
0.019 = 83% Greek_Macedonia + 17% Sardinian
0.019 = 70% Albanian + 30% Italian_Bergamo
0.019 = 86% Albanian + 14% Spanish_Asturias
0.019 = 72% Albanian + 28% Italian_Liguria
0.019 = 88% Albanian + 12% Basque_Soule
0.020 = 88% Albanian + 12% Basque_French
0.020 = 71% Albanian + 29% Italian_Lombardy
0.020 = 85% Greek_Central_Macedonia + 15% Sardinian
0.020 = 90% Albanian + 10% Sardinian
0.020 = 81% Albanian + 19% Spanish_Menorca

Excluding Albanian average:
0.019 = 83% Greek_Macedonia + 17% Sardinian
0.020 = 85% Greek_Central_Macedonia + 15% Sardinian
0.020 = 55% Greek_Arcadia + 45% Italian_Bergamo
0.020 = 60% Greek_Macedonia + 40% Italian_Bergamo
0.020 = 62% Greek_Central_Macedonia + 38% Italian_Bergamo
0.021 = 60% Greek_Arcadia + 40% Italian_Trentino_Alto_Adige
0.021 = 56% Greek_Messenia + 44% Italian_Bergamo
0.021 = 80% Greek_Arcadia + 20% Basque_Soule
0.021 = 81% Greek_Messenia + 19% Basque_Soule
0.021 = 63% Greek_Macedonia + 37% French_Corsica

Moriopoulos collection:
0.018 = 84% Greek_Central_Macedonia + 16% Sardinian_Benetutti
0.018 = 86% Greek_Central_Macedonia + 14% Sardinian
0.018 = 85% Albanian + 15% Sardinian_North_o
0.018 = 69% Albanian + 31% Italian_Lombardy_Bergamo_(Lombard)
0.018 = 81% Greek_Central_Macedonia + 19% Sardinian_North_o
0.018 = 88% Albanian + 12% Sardinian_Benetutti
0.019 = 86% Greek_Central_Macedonia + 14% Sardinian_North
0.019 = 77% Albanian + 23% Italian_(Corsican_Profile)
0.019 = 86% Albanian + 14% Spanish_Asturias_(Asturian)
0.019 = 84% Greek_Central_Macedonia + 16% Sardinian_Sulcis-Iglesiente

Moriopoulos colleciton excluding Albanian:
0.018 = 84% Greek_Central_Macedonia + 16% Sardinian_Benetutti
0.018 = 86% Greek_Central_Macedonia + 14% Sardinian
0.018 = 81% Greek_Central_Macedonia + 19% Sardinian_North_o
0.019 = 86% Greek_Central_Macedonia + 14% Sardinian_North
0.019 = 84% Greek_Central_Macedonia + 16% Sardinian_Sulcis-Iglesiente
0.019 = 71% Moldovan_Stefan_Voda + 29% Sardinian_Sulcis-Iglesiente
0.019 = 73% Moldovan_Stefan_Voda + 27% Sardinian_Benetutti
0.019 = 71% Bulgarian_South_Central + 29% Sardinian_Sulcis-Iglesiente
0.019 = 59% Greek_Central_Macedonia + 41% Italian_Lombardy_Brescia_(Lombard)
0.019 = 81% Vlach_Aromanian + 19% Sardinian_Benetutti

[TheHolyGoose]

Modern:
0.011 TheHolyGoose_AncestryDNA_scaled = 78% Danish:GSM1985817 + 22% Spanish_Terres_de_l'Ebre:RAG111
0.011 TheHolyGoose_AncestryDNA_scaled = 63% Scottish:Scottish22 + 37% Dutch:GSM1633980
0.011 TheHolyGoose_AncestryDNA_scaled = 65% English_Cornwall:HG00252 + 35% Dutch:GSM567341
0.011 TheHolyGoose_AncestryDNA_scaled = 61% English_Cornwall:HG00252 + 39% Welsh:WalesDR56
0.011 TheHolyGoose_AncestryDNA_scaled = 71% English_Cornwall:HG00252 + 29% German_Erlangen:GSM1658603
0.011 TheHolyGoose_AncestryDNA_scaled = 80% English_Cornwall:HG00252 + 20% German:German69
0.011 TheHolyGoose_AncestryDNA_scaled = 79% English_Cornwall:HG00252 + 21% Czech:Czech3
0.012 TheHolyGoose_AncestryDNA_scaled = 56% Scottish:Scottish22 + 44% BelgianA:5434289041_R03C01
0.012 TheHolyGoose_AncestryDNA_scaled = 71% English_Cornwall:HG00252 + 29% French_Brittany:Rennes_B_29
0.012 TheHolyGoose_AncestryDNA_scaled = 77% Danish:GSM1985817 + 23% Basque_Biscay:4455
0.012 TheHolyGoose_AncestryDNA_scaled = 67% English_Cornwall:HG00252 + 33% English:HG01790
0.012 TheHolyGoose_AncestryDNA_scaled = 78% English_Cornwall:HG00252 + 22% German_East:German_East6

Ancient:
0.009 TheHolyGoose_AncestryDNA_scaled = 65% Netherlands_Groningen_Saxon_Medieval:GRO014.A_noUDG + 35% France_SouthEast_IA2:I19918
0.011 TheHolyGoose_AncestryDNA_scaled = 72% Netherlands_Groningen_Saxon_Medieval:GRO014.A_noUDG + 28% Spain_EBA_Mallorca:I4329
0.012 TheHolyGoose_AncestryDNA_scaled = 73% England_MIA:I13717 + 27% Sweden_Viking.SG:VK108_noUDG.SG
0.012 TheHolyGoose_AncestryDNA_scaled = 52% Denmark_Viking.SG:VK329_noUDG.SG + 48% England_EarlyMedieval_Saxon:I20671
0.012 TheHolyGoose_AncestryDNA_scaled = 63% England_MIA:I13717 + 37% Sweden_Viking.SG:VK429_noUDG.SG
0.012 TheHolyGoose_AncestryDNA_scaled = 53% England_MIA:I13717 + 47% England_MIA:I12771
0.012 TheHolyGoose_AncestryDNA_scaled = 70% Netherlands_Groningen_Saxon_Medieval:GRO014.A_noUDG + 30% Italy_TarquiniaMonterozzi_IA.SG:R10361.SG
0.012 TheHolyGoose_AncestryDNA_scaled = 86% Netherlands_Groningen_Saxon_Medieval:GRO014.A_noUDG + 14% Italy_Sardinia_C:FIL004
0.012 TheHolyGoose_AncestryDNA_scaled = 71% England_MIA:I13717 + 29% Denmark_Zealand_Saxon_Medieval:KPN012.A_noUDG
0.012 TheHolyGoose_AncestryDNA_scaled = 74% England_MIA:I13717 + 26% Denmark_Zealand_Saxon_Medieval:KPN002.A_noUDG
0.012 TheHolyGoose_AncestryDNA_scaled = 59% Estonia_EarlyViking.SG:VK550_noUDG.SG + 41% Denmark_Viking.SG:VK286_noUDG.SG
0.012 TheHolyGoose_AncestryDNA_scaled = 55% England_MIA:I12771 + 45% England_MIA_LIA:I14353

[Webb]

Just reviewing the comments up to this point it seems,

Anglesqueville, Stefano, and possibly JMcB and Rodoon, seem to get good results.

My technique, which may only apply to New World and other mixed pop members, is to look at the modern average and individual 2 ways for combos that include known ancestry, or population substitutes for known ancesetry.

While I do have some German, a little bit of Irish, and a minuscule amount of Dutch Danish, Norwegian, Swedish, Finn, French (no known Icelandic), the bulk of it is English, Scottish/Ulster Scot, and Welsh

As far as population substitutes, in my case
Danes are probably most similar to Yorkshire/East Midlands English,
Dutch are probably more similar to the Rest of Eastern and south eastern England e.g. East Anglia and other predominately Saxon areas of England
Scotland is going to be more similar to northern England e.g. Durham, Northumbria, and Cumbria.

The Irish cover such a large area on PCAs that it's hard to nail them down...maybe generic pre Saxon Britons & Irish.  I'm gonna skip them and the Icelandics in the possible interpretations below.

In my case of these Modern scaled averages might be interpreting my mostly English ancestry as:
0.01498 Mitchell_scaled = 66% Scotland + 34% Denmark (possibly 66% Scottish/Northern English, 34% Yorkshire/East Midlands)
0.01518 Mitchell_scaled = 61% Scotland + 39% Holland (61% Scottish/North English, 39% East Anglia, southeastern England)
0.0157 Mitchell_scaled = 60% Holland + 40% Denmark (60% East Anglia and SE England, 40% Yorkshire & East Midlands)
0.01604 Mitchell_scaled = 59% Denmark + 41% England (59% Yorkshire & East Midlands, 41% rest of Britain and Ireland)

On the individual moderns,
0.01100 51% English:England7 + 49% Danish:436.  Possibly 51% Scottish/Northern England, 49% Yorkshire and East Midlands
0.01270 61% Scottish:Scottish24 + 39% Dutch:Netherlands56. Possibly 61% Scottish and Northern English, 39% East Anglia and SE England

Not an exact science, as my English ancestry wasn't limited to North Sea facing England, but if you know you don't have ancestry from some of these modern 2 way countries, then it's just a way to try make more sense of the results.

How does one get coordinates to use these tools?  Thanks.

[Mitchell-Atkins]

In the above map the area marked as the DANELAW stretches too far to the north.

County Durham, the Newcastle area and Northumberland, with south-east Scotland up to Edinburgh, remained in the hands of the Northumbrian Angles.

Just reviewing the comments up to this point it seems,

Anglesqueville, Stefano, and possibly JMcB and Rodoon, seem to get good results.

My technique, which may only apply to New World and other mixed pop members, is to look at the modern average and individual 2 ways for combos that include known ancestry, or population substitutes for known ancesetry.

While I do have some German, a little bit of Irish, and a minuscule amount of Dutch Danish, Norwegian, Swedish, Finn, French (no known Icelandic), the bulk of it is English, Scottish/Ulster Scot, and Welsh

As far as population substitutes, in my case
Danes are probably most similar to Yorkshire/East Midlands English,
Dutch are probably more similar to the Rest of Eastern and south eastern England e.g. East Anglia and other predominately Saxon areas of England
Scotland is going to be more similar to northern England e.g. Durham, Northumbria, and Cumbria.

The Irish cover such a large area on PCAs that it's hard to nail them down...maybe generic pre Saxon Britons & Irish.  I'm gonna skip them and the Icelandics in the possible interpretations below.

In my case of these Modern scaled averages might be interpreting my mostly English ancestry as:
0.01498 Mitchell_scaled = 66% Scotland + 34% Denmark (possibly 66% Scottish/Northern English, 34% Yorkshire/East Midlands)
0.01518 Mitchell_scaled = 61% Scotland + 39% Holland (61% Scottish/North English, 39% East Anglia, southeastern England)
0.0157 Mitchell_scaled = 60% Holland + 40% Denmark (60% East Anglia and SE England, 40% Yorkshire & East Midlands)
0.01604 Mitchell_scaled = 59% Denmark + 41% England (59% Yorkshire & East Midlands, 41% rest of Britain and Ireland)

On the individual moderns,
0.01100 51% English:England7 + 49% Danish:436.  Possibly 51% Scottish/Northern England, 49% Yorkshire and East Midlands
0.01270 61% Scottish:Scottish24 + 39% Dutch:Netherlands56. Possibly 61% Scottish and Northern English, 39% East Anglia and SE England

Not an exact science, as my English ancestry wasn't limited to North Sea facing England, but if you know you don't have ancestry from some of these modern 2 way countries, then it's just a way to try make more sense of the results.

How does one get coordinates to use these tools?  Thanks.

I think your answer is the links in post 1?