function [data, auxData, metaData, txtData, weights] = mydata_Netuma_bilineata
%% set metaData

metaData.phylum     = 'Chordata'; 
metaData.class      = 'Actinopterygii'; 
metaData.order      = 'Siluriformes'; 
metaData.family     = 'Ariidae';
metaData.species    = 'Netuma_bilineata'; 
metaData.species_en = 'Bronze catfish'; 

metaData.ecoCode.climate = {'MA'};
metaData.ecoCode.ecozone = {'MIN','MPSW'};
metaData.ecoCode.habitat = {'0iMcd'};
metaData.ecoCode.embryo  = {'Mbm'};
metaData.ecoCode.migrate = {'Mo'};
metaData.ecoCode.food    = {'biO'};
metaData.ecoCode.gender  = {'D'};
metaData.ecoCode.reprod  = {'O'};

metaData.T_typical  = C2K(28.3); % K, body temp
metaData.data_0     = {'am'; 'Lp'; 'Li'; 'Wwb'; 'Wwp'; 'Wwi'}; 
metaData.data_1     = {'t-L'; 'Ww-N'}; 

metaData.COMPLETE = 2.5; % using criteria of LikaKear2011

metaData.author   = {'Bas Kooijman'};    
metaData.date_subm = [2023 11 04];              
metaData.email    = {'bas.kooijman@vu.nl'};            
metaData.address  = {'VU University Amsterdam'};   

metaData.curator     = {'Dina Lika'};
metaData.email_cur   = {'lika@uoc.gr'}; 
metaData.date_acc    = [2023 11 04];

%% set data
% zero-variate data;

data.am = 22*365;     units.am = 'd';    label.am = 'life span';     bibkey.am = 'AlHuAlBa2021';
  temp.am = C2K(28.3);  units.temp.am = 'K'; label.temp.am = 'temperature'; 

data.Lp  = 36;  units.Lp  = 'cm';	label.Lp  = 'total length at puberty';  bibkey.Lp  = 'fishbase';
data.Li  = 90;   units.Li  = 'cm';	label.Li  = 'ultimate total length';    bibkey.Li  = 'fishbase'; 

data.Wwb = 0.449; units.Wwb = 'g';	label.Wwb = 'wet weight at birth';      bibkey.Wwb = 'AlHuAlBa2021'; 
  comment.Wwb = 'based on egg diameter of 9.5 mm: pi/6*0.95^3';
data.Wwp  = 470; units.Wwp  = 'g';	label.Wwp  = 'wet weight at puberty';    bibkey.Wwp  = 'AlHuAlBa2021'; 
  comment.Wwp = 'based on 0.00794*Lp^3.07, see F1';
data.Wwi  = 7.9e3; units.Wwi  = 'g';	label.Wwi  = 'ultimate wet weight';    bibkey.Wwi  = 'fishbase'; 
  comment.Wwi = 'based on 0.00794*Li^3.07, see F1';
 
% uni-variate data
% time-length
data.tL_f = [ ... % time since birth (yr), total length (cm)
1.322	43.039
1.372	41.267
1.671	45.162
1.746	43.922
1.970	43.920
2.219	54.193
2.219	44.803
2.369	42.144
2.594	47.103
2.668	50.114
2.693	43.027
2.718	45.330
2.993	50.466
3.042	43.910
3.142	41.252
3.167	47.984
3.167	44.795
3.217	42.668
3.367	55.777
3.392	54.714
3.441	52.056
3.491	48.158
3.516	59.851
3.516	50.107
3.541	55.599
3.641	50.106
3.666	61.975
3.865	60.556
3.865	56.304
3.865	52.230
3.915	59.316
3.965	65.693
3.965	65.516
3.990	51.697
4.040	63.035
4.040	56.126
4.090	54.531
4.140	50.987
4.165	59.845
4.214	58.427
4.239	55.415
4.289	65.336
4.289	57.009
4.314	56.123
4.364	60.198
4.364	55.060
4.414	62.677
4.464	66.752
4.489	64.094
4.539	66.397
4.589	71.180
4.613	65.510
4.663	63.207
4.688	52.931
4.788	62.851
4.813	68.875
4.813	58.953
4.863	60.370
4.913	63.559
4.938	59.838
4.963	68.342
4.963	56.295
4.988	65.330
4.988	62.141
5.012	66.924
5.012	65.152
5.012	62.849
5.012	58.952
5.037	58.066
5.037	57.180
5.162	60.190
5.187	66.214
5.212	53.989
5.287	61.252
5.312	63.201
5.337	52.925
5.362	65.858
5.436	61.251
5.436	63.909
5.461	68.515
5.486	62.136
5.486	63.377
5.511	58.593
5.810	63.905
5.810	66.031
5.810	70.106
5.960	70.813
5.985	62.309
6.010	61.246
6.010	62.840
6.060	69.041
6.060	72.407
6.085	62.840
6.085	67.092
6.135	72.938
6.160	64.965
6.234	66.913
6.234	58.941
6.234	58.764
6.259	67.445
6.284	70.102
6.309	63.724
6.384	74.176
6.409	66.735
6.459	72.935
6.484	70.277
6.559	72.048
6.584	68.505
6.733	68.858
6.833	79.132
6.833	67.617
6.858	66.022
6.908	66.907
6.933	67.616
6.933	69.033
6.983	70.627
7.007	74.525
7.007	72.399
7.007	63.895
7.132	67.437
7.132	64.248
7.157	71.866
7.157	59.996
7.257	71.865
7.307	61.943
7.332	69.030
7.382	71.864
7.506	75.937
7.531	72.217
7.581	70.267
7.606	76.822
7.631	71.684
7.731	73.455
7.756	69.203
7.756	68.140
7.855	57.332
7.855	76.820
7.880	70.265
7.905	72.391
7.955	66.544
8.005	67.783
8.005	76.996
8.030	69.201
8.030	75.933
8.080	70.972
8.279	65.301
8.304	74.159
8.354	70.615
8.479	71.677
8.603	76.282
8.603	74.688
8.603	62.995
8.828	81.241
8.828	71.851
8.828	74.331
8.878	63.524
8.978	79.113
9.002	75.038
9.027	70.432
9.077	68.837
9.102	75.392
9.127	74.329
9.177	78.934
9.252	74.859
9.352	70.960
9.551	79.285
9.676	69.895
9.776	74.323
9.825	70.602
9.825	78.220
9.850	72.019
10.025	73.080
10.274	61.917
10.299	65.283
10.349	73.609
10.524	71.659
10.574	75.910
10.599	82.288
10.798	72.896
10.823	79.097
10.823	74.668
10.998	78.033
11.097	75.374
11.372	74.309
11.521	70.587
11.696	76.078
11.820	69.876
11.845	80.328
11.845	82.986
11.895	71.115
11.995	70.228
12.020	75.897
12.020	72.177
12.045	80.504
12.070	78.023
12.145	73.416
12.494	71.996
12.618	65.971
12.618	78.195
13.815	76.945
15.237	68.605
15.262	74.806
15.486	79.764
15.835	71.257
16.484	74.441
18.279	79.917
19.501	83.095
20.349	76.887];
data.tL_f(:,1) = 365 * (0.8 + data.tL_f(:,1)); % conver yr to d
for i=2:size(data.tL_f,1);if data.tL_f(i,1)<=data.tL_f(i-1,1);data.tL_f(i,1)=data.tL_f(i-1,1)+1e-3;end;end
units.tL_f   = {'d', 'cm'};  label.tL_f = {'time since birth', 'total length','females'};  
temp.tL_f    = C2K(28.3);  units.temp.tL_f = 'K'; label.temp.tL_f = 'temperature';
bibkey.tL_f = 'AlHuAlBa2021';
%
data.tL_m = [ ... % time since birth (yr), total length (cm)
0.010	10.453
0.025	9.212
0.100	7.972
0.150	13.818
0.175	10.451
0.175	7.262
0.200	9.742
0.274	12.399
0.274	13.639
0.299	19.308
0.324	20.017
0.349	15.942
0.349	12.044
0.399	17.182
0.399	11.689
0.424	16.473
0.424	12.043
0.449	13.992
0.449	15.055
0.474	10.980
0.499	10.980
0.549	19.129
0.549	20.723
0.549	14.700
0.623	18.420
0.623	23.912
0.623	16.117
0.623	13.282
0.623	10.270
0.648	26.923
0.648	12.219
0.673	21.785
0.673	22.848
0.673	24.974
0.673	17.711
0.673	12.927
0.723	13.812
0.723	11.864
0.823	17.178
0.823	15.229
0.848	16.469
0.898	21.429
0.898	19.303
0.898	15.583
0.923	18.240
0.973	18.417
0.998	20.365
1.072	24.971
1.072	21.782
1.072	19.479
1.072	17.530
1.097	26.565
1.097	17.884
1.122	27.451
1.147	21.427
1.172	23.730
1.172	22.844
1.172	19.301
1.197	28.690
1.222	24.969
1.222	21.426
1.322	13.276
1.322	24.969
1.421	23.728
1.446	27.802
1.446	28.865
1.446	31.523
1.471	19.298
1.496	27.093
1.496	38.432
1.546	14.337
1.546	15.400
1.596	18.765
1.596	34.356
1.621	20.182
1.621	29.218
1.621	35.596
1.646	17.879
1.646	23.194
1.646	25.852
1.671	20.891
1.671	23.903
1.671	24.966
1.721	31.343
2.020	32.758
2.020	27.797
2.020	28.683
2.020	28.860
2.095	27.265
2.120	30.985
2.120	33.288
2.170	20.178
2.195	35.768
2.219	37.362
2.219	34.528
2.244	30.275
2.294	24.960
2.344	28.857
2.369	34.526
2.394	41.258
2.394	31.337
2.444	28.856
2.444	27.439
2.469	25.136
2.469	21.592
2.494	37.360
2.494	33.108
2.494	26.198
2.544	40.017
2.544	38.954
2.569	38.245
2.569	31.690
2.569	30.095
2.618	32.930
2.618	32.221
2.643	25.311
2.643	18.047
2.668	28.677
2.668	27.968
2.668	20.350
2.668	21.591
2.718	34.346
2.718	30.448
2.943	27.789
2.943	36.647
2.968	29.029
2.968	31.863
2.993	26.903
3.042	33.457
3.042	38.241
3.092	36.114
3.092	37.709
3.117	30.799
3.117	38.949
3.142	41.252
3.192	31.153
3.192	36.468
3.242	31.861
3.292	34.518
3.317	30.620
3.367	35.758
3.416	29.911
3.416	29.025
3.466	32.036
3.566	44.968
3.641	35.578
3.666	32.034
3.691	23.707
3.741	40.715
3.766	36.286
3.865	41.068
3.865	40.005
3.965	35.575
4.015	46.205
4.015	40.004
4.015	35.929
4.065	38.586
4.140	40.711
4.140	41.952
4.239	36.813
4.314	35.041
4.314	37.698
4.339	40.001
4.464	39.114
4.464	43.012
4.489	41.594
4.713	38.403
4.763	40.529
4.763	39.289
4.813	41.768
4.988	40.172
5.012	27.948
5.037	41.766
5.162	49.738
5.162	40.879
5.237	36.095
5.262	42.119
5.337	39.106
5.387	40.700
5.387	42.118
5.411	41.763
5.436	39.283
5.636	46.545
5.636	27.942
5.661	41.407
5.711	41.938
5.736	43.000
5.761	37.685
5.810	44.240
5.960	44.061
5.985	48.845
5.985	39.101
5.985	36.797
6.035	41.935
6.110	44.237
6.160	43.528
6.284	42.464
6.284	39.984
6.384	50.790
6.434	49.018
6.434	41.931
7.007	29.170
7.007	36.788
7.057	47.418
7.057	50.961
7.182	47.948
7.307	52.376
7.357	28.990
7.357	39.443
7.406	40.505
7.406	44.049
7.506	45.465
7.781	42.982
7.830	42.096
7.830	44.045
7.930	40.501
7.980	42.095
7.980	29.516
8.005	44.929
8.005	44.398
8.030	48.649
8.030	39.614
8.030	35.362
8.105	45.283
8.155	44.219
8.180	51.128
8.254	43.332
8.279	41.206
8.454	45.811
8.454	53.252
8.479	47.760
8.529	44.570
8.778	46.340
8.878	45.276
8.928	40.315
8.978	47.932
9.002	45.452
9.077	38.010
9.102	45.451
9.177	48.994
9.202	39.781
9.352	45.094
9.352	46.689
9.401	41.373
9.476	48.282
9.875	45.798
9.875	40.483
10.000	47.037
10.050	50.757
10.125	44.024
10.125	45.442
10.150	47.745
10.150	52.705
10.823	46.144
10.848	43.664
11.047	44.725
11.172	51.633
11.197	45.255
11.197	50.216
11.222	45.964
11.247	42.597
11.347	45.608
11.347	48.974
11.721	46.314
11.746	39.935
11.771	48.262
11.820	41.883
11.920	46.135
11.945	56.764
11.995	41.173
12.020	53.043
12.070	37.098
12.195	55.345
12.219	46.309
12.244	42.057
12.369	45.245
12.444	44.535
12.444	46.130
12.693	45.951
12.743	45.064
12.993	44.708
12.993	46.834
13.167	44.883
13.192	45.946
13.317	42.225
13.416	47.361
13.491	48.069
13.915	41.333
13.940	46.117
14.090	52.316
14.090	44.167
14.140	46.292
14.140	43.812
14.314	52.314
14.339	46.822
14.988	46.285
15.162	45.220
15.262	46.459
15.436	46.458
15.786	45.746
16.683	53.002
16.733	45.383
17.007	52.113
17.132	46.088
17.382	46.441
17.481	49.983];
data.tL_m(:,1) = 365 * (0.8 + data.tL_m(:,1)); % conver yr to d
for i=2:size(data.tL_m,1);if data.tL_m(i,1)<=data.tL_m(i-1,1);data.tL_m(i,1)=data.tL_m(i-1,1)+1e-3;end;end
units.tL_m   = {'d', 'cm'};  label.tL_m = {'time since birth', 'total length','males'};  
temp.tL_m    = C2K(28.3);  units.temp.tL_m = 'K'; label.temp.tL_m = 'temperature';
bibkey.tL_m = 'AlHuAlBa2021';

% weight-fecundity
data.WN = [ ... % weight (g), fecundity (#)
474.636	28.209
474.963	47.015
505.449	52.239
535.826	51.194
626.757	36.567
663.231	36.567
742.440	47.015
784.884	40.746
845.765	45.970
845.928	55.373
875.852	28.209
876.287	53.284
888.300	44.925
894.706	63.731
912.253	24.030
925.228	71.045
967.400	49.104
985.764	56.418
985.873	62.687
991.734	50.149
1016.286	63.731
1028.807	84.627
1052.870	70.000
1076.841	50.149
1095.876	96.119
1137.595	48.060
1144.109	73.134
1156.195	68.955
1161.965	51.194
1216.822	59.552
1351.050	87.761
1387.797	103.433
1405.544	75.224
1497.455	117.015
1503.661	124.328
1551.948	104.478
1569.895	87.761
1636.855	92.985
1648.542	65.821
1758.254	82.537
1836.919	61.642
1837.245	80.448
1915.728	49.104
1935.798	154.627
1979.023	193.284
2051.009	137.910
2119.022	203.731
2184.966	150.448
2428.581	176.567
2435.113	202.687
2452.407	148.358
2452.534	155.672
2477.086	169.254
2500.803	134.776
2543.501	143.134
2593.730	235.075
2604.128	133.731
2670.889	127.463
2818.001	197.463
2895.577	113.881
2896.321	156.716
2939.364	184.925
3006.342	191.194
3049.930	250.746
3170.675	202.687
3213.247	203.731
3218.201	138.955
3287.556	282.090
3290.695	112.836
3315.338	131.642
3322.179	175.522
3358.890	189.104
3382.806	166.119
3432.781	243.433
3503.770	130.597
3517.452	218.358
3529.066	187.015
3595.264	148.358
3602.831	234.030
3608.257	196.418
3654.947	84.627
3710.802	150.448
3772.463	200.597
3777.852	160.896
3840.494	267.463
3862.124	112.836
3863.249	177.612
3917.216	134.776
4033.916	203.731
4113.705	247.612
4252.071	164.030
4436.837	301.940
4737.522	113.881
4792.960	155.672
5565.577	189.104];
units.WN   = {'g', '#'};  label.WN = {'weight', 'fecundity'};  
temp.WN    = C2K(28.3);  units.temp.WN = 'K'; label.temp.WN = 'temperature';
bibkey.WN = 'AlHuAlBa2021';
    
%% set weights for all real data
weights = setweights(data, []);
weights.tL_f = 3 * weights.tL_f;
weights.tL_m = 3 * weights.tL_m;

%% set pseudodata and respective weights
[data, units, label, weights] = addpseudodata(data, units, label, weights);
weights.psd.k_J = 0; weights.psd.k = 1;

%% 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_f','tL_m'}; subtitle1 = {'Data for females, males'};
metaData.grp.sets = {set1};
metaData.grp.subtitle = {subtitle1};

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

%% Facts
F1 = 'length-weight: W in g = 0.00794*(TL in cm)^3.07';
metaData.bibkey.F1 = 'fishbase';
metaData.facts = struct('F1',F1); 

%% Links
metaData.links.id_CoL = '6RYKB'; % Cat of Life
metaData.links.id_ITIS = ''; % ITIS
metaData.links.id_EoL = '46582087'; % Ency of Life
metaData.links.id_Wiki = 'Netuma_bilineata'; % Wikipedia
metaData.links.id_ADW = 'Netuma_bilineata'; % ADW
metaData.links.id_Taxo = '3417896'; % Taxonomicon
metaData.links.id_WoRMS = '281743'; % WoRMS
metaData.links.id_fishbase = 'Netuma-bilineata'; % fishbase

%% References
bibkey = 'Wiki'; type = 'Misc'; bib = ...
'howpublished = {\url{https://en.wikipedia.org/wiki/Netuma_bilineata}}';
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 = 'AlHuAlBa2021'; type = 'Article'; bib = [ ... 
'doi = {10.1016/j.rsma.2021.101885}, ' ...
'author = {M. Al-Husaini and A. Al-Baz and T. Dashti and S. Rajab;H. Husain},'...
'title = {Age, growth, and reproductive parameters of four species of sea catfish ({S}iluriformes: {A}riidae) from {K}uwaiti waters },'...
'journal = {Regional Studies in Marine Science}, '...
'volume = {46}, '...
'year = {2021}, '...
'pages = {101885}'];
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