function [data, auxData, metaData, txtData, weights] = mydata_Lateolabrax_japonicus

%% set metaData
metaData.phylum     = 'Chordata'; 
metaData.class      = 'Actinopterygii'; 
metaData.order      = 'Moroniformes'; 
metaData.family     = 'Lateolabracidae';
metaData.species    = 'Lateolabrax_japonicus'; 
metaData.species_en = 'Japanese seabass'; 

metaData.ecoCode.climate = {'MB'};
metaData.ecoCode.ecozone = {'MPW'};
metaData.ecoCode.habitat = {'0jMp', 'jiMd'};
metaData.ecoCode.embryo  = {'Mp'};
metaData.ecoCode.migrate = {};
metaData.ecoCode.food    = {'bjPz', 'jiCi', 'jiCvf'};
metaData.ecoCode.gender  = {'D'};
metaData.ecoCode.reprod  = {'O'};

metaData.T_typical  = C2K(22.4); % K, body temp
metaData.data_0     = {'am'; 'Lp'; 'Li'; 'Wwb'; 'Ri'}; 
metaData.data_1     = {'t-L_f'; 't-Ww_f'; 'L-Ww_f'}; 

metaData.COMPLETE = 2.8; % using criteria of LikaKear2011

metaData.author    = {'Bas Kooijman'};    
metaData.date_subm = [2024 03 10];              
metaData.email     = {'bas.kooijman@vu.nl'};            
metaData.address   = {'VU University Amsterdam'};  

metaData.curator     = {'Dina Lika'};
metaData.email_cur   = {'lika@uoc.gr'}; 
metaData.date_acc    = [2024 03 10];

%% set data
% zero-variate data;

data.ab = 4.5;     units.ab = 'd';    label.ab = 'age at birth';     bibkey.ab = 'fishbase';   
  temp.ab = C2K(22.4);  units.temp.ab = 'K'; label.temp.am = 'temperature'; 
  comment.ab = 'specified temp is 13 C, but this seems at odds with other rates';
data.am = 40*365;     units.am = 'd';    label.am = 'life span';     bibkey.am = 'OrloVedi2018';   
  temp.am = C2K(22.4);  units.temp.am = 'K'; label.temp.am = 'temperature'; 
  comment.am = 'max age found was 38 yr, but this problably underestimates max life span';

data.Lp  = 50;   units.Lp  = 'cm';   label.Lp  = 'total length at puberty for female';  bibkey.Lp  = 'fishbase';
data.Li  = 102;     units.Li  = 'cm';   label.Li  = 'ultimate total length';    bibkey.Li  = 'fishbase';

data.Wwb  = 1.4e-3;   units.Wwb  = 'g';   label.Wwb  = 'wet weight at birth';  bibkey.Wwb  = 'fishbase';
  comment.Wwb = 'based on egg diameter of 1.4 mm: pi/6*0.14^3';

data.Ri = 10e6/365;   units.Ri = '#/d';    label.Ri = 'max reproduction rate';     bibkey.Ri = 'fishbase';   
  temp.Ri = C2K(22.4);  units.temp.Ri = 'K'; label.temp.Ri = 'temperature';
  comment.Ri = 'fecundity 1,726,242-17,822,576';

% uni-variate data

% time-length
data.tL_Sf= [ ... % time since birth (yr), std length (cm) 
0.730	20.129
0.955	18.308
0.957	16.642
1.795	23.601
1.916	35.569
1.965	30.871
2.786	53.891
2.889	41.011
2.968	49.798
2.971	47.828
3.596	46.305
3.605	38.123
3.724	50.849
3.993	49.028
3.998	45.088
4.084	47.511
4.694	59.322
4.792	51.442
4.873	58.410
4.961	59.318
5.004	61.136
5.053	57.347
5.108	47.347
5.361	60.374
5.629	59.461
5.686	48.703
5.767	55.369
5.894	61.579
5.895	60.367
5.987	57.639
6.000	46.729
6.614	54.449
6.652	60.661
6.874	61.112
6.975	50.050
7.006	62.323
7.012	57.020
7.017	52.929
7.634	58.375
7.719	62.314
7.951	53.978
9.019	54.570
11.586	67.871
11.814	63.474
11.816	61.505
11.904	62.261
11.946	64.836
11.984	70.442
12.170	64.076
12.605	73.161
13.066	58.610
13.808	71.934
13.915	56.478
16.873	79.168
17.987	77.639
17.997	69.002
20.925	78.814];
data.tL_Sf(:,1) = data.tL_Sf(:,1) * 365; % convert yr to d
units.tL_Sf = {'d', 'cm'};   label.tL_Sf = {'time since birth', 'std length','female Sendai Bay'};  
temp.tL_Sf = C2K(22.4);  units.temp.tL_Sf = 'K'; label.temp.tL_Sf = 'temperature';
bibkey.tL_Sf = 'JianLave2018';
%
data.tL_Sm= [ ... % time since birth (yr), std length (cm) 
0.643	18.464
0.689	17.100
0.731	19.675
0.773	21.947
0.872	13.309
0.959	14.975
0.998	20.126
1.047	16.186
1.093	14.216
1.129	21.791
1.179	17.548
1.606	33.452
1.698	31.026
1.738	35.268
1.784	33.298
1.830	32.388
1.877	29.812
1.879	28.297
1.917	34.054
2.578	40.712
2.667	40.105
2.699	51.923
2.709	42.377
2.714	38.589
2.761	35.861
2.764	33.285
2.793	46.921
2.795	45.558
2.798	42.679
2.800	41.315
2.801	39.952
2.807	35.103
2.809	32.527
2.810	31.315
2.811	30.709
2.812	30.255
2.813	29.042
2.840	44.800
2.842	43.588
2.853	33.587
2.894	36.314
2.896	34.344
3.187	53.432
3.557	41.003
3.681	49.941
3.691	40.546
3.722	53.122
3.724	51.152
3.726	48.879
3.730	45.243
3.731	43.576
3.732	43.425
3.775	45.394
3.780	41.000
3.812	52.060
3.824	41.454
3.954	44.786
3.999	43.573
4.084	47.966
4.655	54.322
4.967	54.773
5.019	47.348
5.060	50.529
5.637	52.189
5.684	50.370
5.822	46.580
5.863	49.004
5.869	43.701
6.083	52.183
6.615	53.540
6.621	48.843
6.845	47.325
6.928	52.778
7.592	56.406
7.958	47.614
7.989	59.583
8.035	58.219
8.921	61.996
8.967	60.934
9.856	61.984
10.887	56.213
11.956	55.897
12.974	61.490
13.021	59.368
13.071	54.670
16.979	63.712
18.986	60.808
20.903	58.814
20.989	61.086
22.055	63.345
22.988	65.606
23.125	62.271
24.054	68.017];
data.tL_Sm(:,1) = data.tL_Sm(:,1) * 365; % convert yr to d
units.tL_Sm = {'d', 'cm'};   label.tL_Sm = {'time since birth', 'std length','male Sendai Bay'};  
temp.tL_Sm = C2K(22.4);  units.temp.tL_Sm = 'K'; label.temp.tL_Sm = 'temperature';
bibkey.tL_Sm = 'JianLave2018';
%
data.tL_Tf= [ ... % time since birth (yr), std length (cm) 
1.870	36.491
2.135	28.186
2.136	27.280
2.137	25.771
2.271	39.656
2.315	41.467
2.360	34.070
2.537	35.124
2.538	31.804
2.539	39.200
2.716	39.047
2.893	42.063
2.894	43.724
3.027	44.628
3.028	38.741
3.206	45.380
3.250	45.380
3.295	42.511
3.340	38.133
3.562	48.395
3.651	47.790
3.740	47.487
3.829	56.693
3.874	48.089
3.917	55.635
3.918	52.013
3.963	45.220
4.007	48.389
4.051	53.068
4.052	51.709
4.053	50.804
4.096	49.746
4.542	56.080
4.586	50.344
4.675	47.777
4.764	58.342
4.809	46.266
4.853	57.133
4.941	53.207
4.942	52.151
4.943	47.472
5.032	68.301
5.121	45.205
5.165	43.695
5.254	51.543
5.477	47.314
5.521	48.068
5.655	55.614
5.744	54.254
5.922	64.365
6.857	68.278
7.525	56.496
7.614	66.457
7.703	69.776
7.881	54.227
7.970	71.283
8.015	58.603
8.950	59.044
8.951	58.289
13.046	66.539
13.492	65.326
13.848	61.397
14.605	67.878
15.584	68.017
15.985	61.974
16.698	67.852
17.009	64.829];
data.tL_Tf(:,1) = data.tL_Tf(:,1) * 365; % convert yr to d
units.tL_Tf = {'d', 'cm'};   label.tL_Tf = {'time since birth', 'std length','female Tango Bay'};  
temp.tL_Tf = C2K(22.4);  units.temp.tL_Tf = 'K'; label.temp.tL_Tf = 'temperature';
bibkey.tL_Tf = 'JianLave2018';
%
data.tL_Tm= [ ... % time since birth (yr), std length (cm) 
1.557	24.420
1.558	25.778
1.602	23.061
1.603	31.665
1.647	29.551
1.691	24.871
1.692	27.286
1.693	28.494
1.694	30.456
1.826	26.982
1.827	28.190
1.915	34.830
1.959	29.698
2.315	38.448
2.360	33.315
2.494	31.955
2.805	43.272
2.894	39.648
2.939	41.459
3.117	38.287
3.118	31.193
3.206	40.701
3.340	39.643
3.607	44.620
3.963	43.559
4.141	43.708
4.186	46.726
4.187	44.160
4.497	52.005
4.675	47.173
5.610	49.425
5.788	46.857
5.922	48.063
6.188	43.984
6.189	41.569
6.635	48.658
6.679	47.601
7.481	51.213
8.682	55.575
8.683	48.783
9.083	47.721
9.840	58.882
10.820	51.020
12.512	66.244
13.670	49.173
13.981	57.924
14.917	56.856
15.451	55.792];
data.tL_Tm(:,1) = data.tL_Tm(:,1) * 365; % convert yr to d
units.tL_Tm = {'d', 'cm'};   label.tL_Tm = {'time since birth', 'std length','male Tango Bay'};  
temp.tL_Tm = C2K(22.4);  units.temp.tL_Tm = 'K'; label.temp.tL_Tm = 'temperature';
bibkey.tL_Tm = 'JianLave2018';

% time-weight
data.tW_Sf = [ ... % time since birth (yr), wet weight (kg)
1.007	155.214
1.124	68.908
1.783	251.835
1.976	415.190
2.169	674.544
2.671	1154.946
2.751	809.409
2.780	2201.433
2.827	963.071
2.902	1414.331
2.942	1241.562
2.945	713.565
3.137	1145.718
3.210	1836.977
3.717	1530.182
3.986	1962.398
3.992	1012.002
4.258	1933.815
4.717	3047.782
4.953	2606.371
5.037	1627.238
5.068	2712.064
5.070	2472.065
5.071	2376.066
5.142	3364.922
5.151	2001.729
5.269	1704.224
5.378	2933.111
5.538	2117.240
5.769	2491.824
5.888	2021.520
5.931	1512.753
5.963	2443.980
6.076	2981.670
6.081	2290.474
6.116	2828.102
6.160	2127.337
6.769	3961.424
6.898	2022.327
7.004	3664.012
7.011	2550.418
7.013	2252.820
7.047	3020.847
7.048	2790.448
7.121	3577.706
7.249	1811.408
7.284	2368.236
7.750	2426.209
7.865	2656.701
7.901	3088.730
8.255	2426.613
9.150	2158.529
11.618	4752.504
11.782	3389.435
12.052	3792.850
12.095	3101.685
12.163	4522.540
12.175	2746.549
12.562	2977.258
12.628	4647.712
12.629	4532.513
13.146	2776.126
13.989	4514.400
14.272	2786.627
17.093	4968.083
18.033	3701.635
18.101	5247.289
21.007	6190.415];
data.tW_Sf(:,1) = data.tW_Sf(:,1) * 365; % convert yr to d
units.tW_Sf = {'d', 'g'};   label.tW_Sf = {'time since birth', 'wet weight','female Sendai Bay'};  
temp.tW_Sf = C2K(22.4);  units.temp.tW_Sf = 'K'; label.temp.tW_Sf = 'temperature';
bibkey.tW_Sf = 'JianLave2018';
%
data.tW_Sm = [ ... % time since birth (yr), wet weight (kg)
0.697	68.567
0.736	126.197
0.891	145.521
1.086	11.277
1.162	184.139
1.202	88.170
1.780	779.833
1.781	434.235
1.782	386.235
1.821	386.266
1.822	347.866
1.859	511.096
1.861	309.497
1.898	607.127
1.976	539.989
2.015	386.421
2.628	1682.912
2.631	1222.114
2.633	1039.715
2.710	1135.777
2.712	780.578
2.750	972.608
2.751	895.809
2.752	655.810
2.753	444.611
2.786	1337.438
2.791	588.641
2.792	377.443
2.907	694.334
2.944	963.164
2.985	521.597
3.053	2124.851
3.057	1539.254
3.676	1798.950
3.679	1443.751
3.715	1856.581
3.720	896.586
3.721	819.786
3.759	1088.616
3.795	1501.445
3.797	1155.847
3.839	714.280
3.910	1655.137
3.914	1050.340
3.950	1482.369
3.991	1098.402
4.068	1280.863
4.104	1664.892
4.801	2011.050
4.920	1655.945
4.921	1617.545
4.999	1502.408
5.072	2222.466
5.578	1963.671
5.660	1234.137
5.695	1829.365
5.772	2050.226
5.813	1656.659
5.892	1426.323
5.968	1656.784
6.159	2223.336
6.238	1945.000
6.743	2012.603
6.745	1724.605
6.825	1340.669
6.938	1820.760
7.868	2224.703
7.982	2522.395
7.990	1399.201
8.096	2887.286
8.174	2935.348
9.028	2955.231
9.145	2849.725
10.038	2879.240
11.011	2611.218
11.905	2391.133
12.014	3629.620
12.993	2420.803
13.065	3227.261
13.110	2257.697
17.260	3144.217
19.011	2560.018
20.989	2907.201
21.024	3502.429
22.036	3224.838
23.043	3580.844
23.123	3292.907
24.049	4176.848];
data.tW_Sm(:,1) = data.tW_Sm(:,1) * 365; % convert yr to d
units.tW_Sm = {'d', 'g'};   label.tW_Sm = {'time since birth', 'wet weight','male Sendai Bay'};  
temp.tW_Sm = C2K(22.4);  units.temp.tW_Sm = 'K'; label.temp.tW_Sm = 'temperature';
bibkey.tW_Sm = 'JianLave2018';
%
data.tW_Tf = [ ... % time since birth (yr), wet weight (kg)
1.901	630.999
2.092	1034.352
2.136	285.587
2.174	381.617
2.247	1140.076
2.248	1092.076
2.328	679.341
2.407	487.404
2.443	967.432
2.946	1438.235
2.949	948.637
3.104	987.162
3.140	1486.390
3.142	1169.591
3.217	1505.652
3.255	1707.282
3.336	1140.947
3.416	881.811
3.607	1419.564
3.721	1861.255
3.724	1410.057
3.832	2725.344
3.881	1198.983
3.882	949.384
3.915	1947.810
3.919	1352.613
3.956	1592.643
3.997	1199.076
4.031	2072.703
4.071	1784.735
4.383	1688.985
4.502	1429.879
4.577	1765.940
4.767	2418.892
4.850	1718.158
4.852	1372.560
4.882	2754.984
4.892	1142.192
4.893	978.993
4.965	1939.050
5.069	3955.133
5.204	1046.442
5.278	1670.500
5.355	1756.962
5.358	1315.365
5.396	1526.595
5.696	3130.035
5.704	1987.641
6.015	1978.290
6.739	4302.070
7.296	2363.315
7.552	4917.120
7.597	3995.556
7.827	4456.540
7.918	2431.013
7.959	2085.446
9.007	2383.884
9.008	2364.684
12.767	4508.492
13.435	3424.226
13.826	3165.339
14.833	3770.945
15.602	5144.360
16.317	2936.932
16.707	2802.844
17.091	3484.751];
data.tW_Tf(:,1) = data.tW_Tf(:,1) * 365; % convert yr to d
units.tW_Tf = {'d', 'g'};   label.tW_Tf = {'time since birth', 'wet weight','female Tango Bay'};  
temp.tW_Tf = C2K(22.4);  units.temp.tW_Tf = 'K'; label.temp.tW_Tf = 'temperature';
bibkey.tW_Tf = 'JianLave2018';
%
data.tW_Tm = [ ... % time since birth (yr), wet weight (kg)
1.437	179.427
1.474	409.857
1.552	371.520
1.553	285.121
1.554	227.521
1.707	563.643
1.747	333.276
2.213	381.649
2.367	669.772
2.407	545.003
2.446	458.635
2.675	1159.618
2.948	1054.237
3.066	823.931
3.069	382.333
3.106	747.163
3.183	852.825
3.454	949.042
3.530	1246.703
4.075	1151.139
4.114	1170.369
4.386	1285.787
4.500	1621.878
5.281	1267.303
5.435	1392.226
5.866	960.571
6.099	1008.757
6.254	1104.881
6.408	1412.205
6.681	1297.223
7.497	1355.476
8.309	2133.725
8.506	1673.083
8.621	1941.976
9.053	1385.521
9.938	2835.829
10.567	1885.932
12.619	3346.773
13.984	2733.466
14.571	2205.935
14.762	2734.088
15.428	2062.621];
data.tW_Tm(:,1) = data.tW_Tm(:,1) * 365; % convert yr to d
units.tW_Tm = {'d', 'g'};   label.tW_Tm = {'time since birth', 'wet weight','male Tango Bay'};  
temp.tW_Tm = C2K(22.4);  units.temp.tW_Tm = 'K'; label.temp.tW_Tm = 'temperature';
bibkey.tW_Tm = 'JianLave2018';

% length-weight
data.LW_S = [ ... % std legnth (cm), weight (g)
12.894	56.306
14.212	78.829
14.799	90.090
14.945	78.829
15.971	101.351
15.971	22.522
16.703	90.090
16.850	90.090
17.729	112.612
18.608	67.568
19.048	123.874
19.341	90.090
20.073	135.135
21.245	191.441
21.392	123.874
21.538	180.180
23.150	270.270
26.960	326.576
27.692	382.883
28.718	382.883
29.304	450.450
30.037	506.757
30.330	596.847
30.623	427.928
31.062	472.973
31.502	439.189
31.941	574.324
32.234	630.630
32.674	529.279
33.114	686.937
33.553	596.847
33.700	574.324
33.846	484.234
34.286	585.585
34.579	698.198
34.725	585.585
34.725	664.414
35.018	518.018
35.458	731.982
35.751	675.676
35.897	608.108
37.070	754.504
37.656	900.901
37.656	945.946
37.802	867.117
38.095	630.630
38.242	731.982
39.121	833.333
39.267	777.027
39.267	867.117
39.560	945.946
40.000	979.730
40.440	1114.864
41.319	1171.171
41.465	957.207
41.465	889.640
41.758	1036.036
42.198	1373.874
42.198	1069.820
42.344	1216.216
43.077	1351.351
43.077	1295.045
43.370	1103.603
43.516	1024.775
43.956	1261.261
44.103	1204.955
44.689	1407.657
45.128	1148.648
45.275	1250.000
45.568	1554.054
45.861	2297.297
45.861	1238.739
46.154	1677.928
46.447	1362.613
46.593	1013.514
46.593	1317.567
46.886	1689.189
47.912	1711.712
47.912	1542.793
47.912	1283.783
48.352	1846.847
48.645	1486.486
48.645	1452.702
48.938	1925.675
49.670	1497.748
49.963	1644.144
50.110	1835.586
50.696	1970.721
50.989	2027.027
50.989	1554.054
51.429	1632.883
51.868	1858.108
52.015	1700.450
52.161	2376.126
52.601	2105.856
53.333	2297.297
53.480	2409.910
53.480	2015.766
53.919	2105.856
54.212	2207.207
54.212	2060.811
54.359	2454.955
54.359	2522.522
54.652	2162.162
55.092	2545.045
55.238	2274.775
55.385	2342.342
55.531	2173.423
55.824	2635.135
55.971	2792.792
56.703	2398.648
56.703	2533.784
56.996	2308.559
57.436	2308.559
57.875	3085.585
58.168	2871.621
58.315	2939.189
58.315	2635.135
58.608	2353.604
58.755	3029.279
59.048	2680.180
59.341	2759.009
59.780	3018.018
59.780	2815.315
59.927	2511.261
60.220	3952.703
60.220	3614.865
60.220	3479.730
60.659	2939.189
60.806	2533.784
60.952	3344.594
60.952	3220.721
60.952	3164.414
60.952	2759.009
61.245	2950.450
61.392	2837.838
62.125	3310.811
62.125	2747.748
62.271	3074.324
62.271	3006.757
63.004	3209.459
63.150	3637.387
63.297	3108.108
63.443	3378.378
63.883	3006.757
64.469	3761.261
65.055	3547.297
67.399	3603.603
67.546	4234.234
68.278	4763.513
69.304	3659.910
71.062	4538.288
71.795	4493.243
72.821	4639.639
73.846	4527.027
78.095	5247.748
78.535	6204.955
79.414	4943.694];
units.LW_S = {'cm', 'g'};   label.LW_S = {'std length', 'wet weight', 'Sendai Bay'};  
bibkey.LW_S = 'JianLave2018';
%
data.LW_T = [ ... % std legnth (cm), weight (g)
21.752	135.135
23.212	225.225
23.212	168.919
23.212	168.919
24.234	191.441
24.234	270.270
25.109	236.487
25.693	281.531
25.839	225.225
26.277	225.225
26.569	292.792
26.715	270.270
27.007	337.838
27.591	360.360
28.175	292.792
28.321	371.622
28.759	259.009
29.343	326.576
29.343	416.667
30.803	371.622
31.387	416.667
31.679	472.973
31.971	292.792
33.139	529.279
33.723	495.495
34.891	574.324
35.328	698.198
35.474	709.460
36.496	653.153
37.080	675.676
37.226	653.153
37.664	822.072
37.810	934.684
38.686	619.369
38.832	799.549
38.832	653.153
38.978	686.937
39.124	1047.297
39.416	934.684
40.584	799.549
40.730	1103.603
41.022	822.072
41.168	1002.252
41.168	889.640
41.606	1013.514
41.606	822.072
41.752	1407.657
42.044	1126.126
42.628	1126.126
42.628	968.468
43.212	1204.955
43.212	979.730
43.650	934.684
43.942	1250.000
44.234	1148.648
44.526	1002.252
44.964	968.468
45.401	1114.864
45.401	1486.486
45.985	1126.126
46.131	1441.441
46.131	1542.793
46.715	1362.613
46.861	1137.387
46.861	1283.783
47.299	1463.964
47.445	979.730
47.737	1385.135
47.883	1711.712
48.467	1283.783
48.905	1644.144
49.051	1430.180
49.197	1677.928
49.781	1216.216
50.365	1722.973
50.657	1790.540
51.241	1655.405
51.387	1351.351
51.971	1689.189
52.263	1554.054
52.409	1948.198
52.847	2049.549
53.139	1948.198
53.723	2015.766
54.161	1835.586
54.599	2072.072
54.745	2184.685
54.891	1993.243
55.620	1779.279
55.766	2027.027
56.204	2308.559
56.350	1891.892
56.934	2635.135
57.080	2702.702
57.518	2736.486
57.664	2691.441
57.956	2432.432
58.102	2342.342
58.686	2409.910
58.832	2826.576
61.168	3108.108
62.044	2905.405
64.526	3130.630
64.672	3412.162
65.547	3277.027
65.693	3378.378
65.839	4493.243
66.569	3952.703
67.445	2792.792
68.029	5078.829
68.029	4245.495
68.321	3806.306
68.613	3918.919
70.073	4921.171
71.387	4459.459];
units.LW_T = {'cm', 'g'};   label.LW_T = {'std length', 'wet weight', 'Tango Bay'};  
bibkey.LW_T = 'JianLave2018';
  
%% set weights for all real data
weights = setweights(data, []);
weights.Li = 3 * weights.Li;
weights.ab = 3 * weights.ab;

%% set pseudodata and respective weights
[data, units, label, weights] = addpseudodata(data, units, label, weights);

%% pack auxData and txtData for output
auxData.temp = temp;
txtData.units = units;
txtData.label = label;
txtData.bibkey = bibkey;
txtData.comment = comment;

%% Group plots
set1 = {'tL_Sf','tL_Sm'}; subtitle1 = {'Data for females, males from Sendai Bay'};
set2 = {'tL_Tf','tL_Tm'}; subtitle2 = {'Data for females, males from Tango Bay'};
set3 = {'tW_Sf','tW_Sm'}; subtitle3 = {'Data for females, males from Sendai Bay'};
set4 = {'tW_Tf','tW_Tm'}; subtitle4 = {'Data for females, males from Tango Bay'};
set5 = {'LW_S','LW_T'}; subtitle5 = {'Data from Sendai, Tango Bay'};
metaData.grp.sets = {set1,set2,set3,set4,set5};
metaData.grp.subtitle = {subtitle1,subtitle2,subtitle3,subtitle4,subtitle5};

%% Discussion points
D1 = 'males are assumed to differ from females by {p_Am} only';
metaData.discussion = struct('D1',D1);

%% Links
metaData.links.id_CoL = '3SHXM'; % Cat of Life
metaData.links.id_ITIS = '167951'; % ITIS
metaData.links.id_EoL = '46581403'; % Ency of Life
metaData.links.id_Wiki = 'Lateolabrax_japonicus'; % Wikipedia
metaData.links.id_ADW = 'Lateolabrax_japonicus'; % ADW
metaData.links.id_Taxo = '178238'; % Taxonomicon
metaData.links.id_WoRMS = '281275'; % WoRMS
metaData.links.id_fishbase = 'Lateolabrax-japonicus'; % fishbase

%% References
bibkey = 'Wiki'; type = 'Misc'; bib =  ...
'howpublished = {\url{http://en.wikipedia.org/wiki/Lateolabrax_japonicus}}';
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'Kooy2010'; type = 'Book'; bib = [ ...  % used in setting of chemical parameters and pseudodata
'author = {Kooijman, S.A.L.M.}, ' ...
'year = {2010}, ' ...
'title  = {Dynamic Energy Budget theory for metabolic organisation}, ' ...
'publisher = {Cambridge Univ. Press, Cambridge}, ' ...
'pages = {Table 4.2 (page 150), 8.1 (page 300)}, ' ...
'howpublished = {\url{../../../bib/Kooy2010.html}}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'HataSeki1962'; type = 'Article'; bib = [ ... 
'author = {Masayoshi Hatanaka and Kiyonari Sekino}, ' ... 
'year = {1962}, ' ...
'title = {Ecological studies of the {J}apanese sea-bass \emhp{Lateolabrax japonicus} {I}: {Feeding habit}, ' ...
'journal = {Bull. Jap. Soc. Sci. Fisheries}, ' ...
'volume = {28}, ' ...
'pages = {851-856}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'JianLave2018'; type = 'Article'; bib = [ ... 
'doi = {10.1007/s12562-018-1271-9}, ' ...
'author = {Jiang, Wei and Lavergne, Edouard and Kurita, Yutaka and Todate, Kana and Kasai, Akihide and Fuji, Taiki and Yamashita, Yoh}, ' ... 
'year = {2018}, ' ...
'title = {Age determination and growth pattern of temperate seabass \emph{Lateolabrax japonicus} in {T}ango {B}ay and {S}endai {B}ay, {J}apan}, ' ...
'journal = {Fisheries Science}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'fishbase'; type = 'Misc'; bib =  ...
'howpublished = {\url{http://www.fishbase.org/summary/Lateolabrax-japonicus.html}}';
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];

