  function [data, auxData, metaData, txtData, weights] = mydata_Dules_auriga
metaData.phylum     = 'Chordata'; 
metaData.class      = 'Actinopterygii'; 
metaData.order      = 'Perciformes'; 
metaData.family     = 'Serranidae';
metaData.species    = 'Dules_auriga'; 
metaData.species_en = 'Cochero'; 

metaData.ecoCode.climate = {'MB'};
metaData.ecoCode.ecozone = {'MASW'};
metaData.ecoCode.habitat = {'biMc'};
metaData.ecoCode.embryo  = {'Mp'};
metaData.ecoCode.migrate = {};
metaData.ecoCode.food    = {'biCi','biCvf'};
metaData.ecoCode.gender  = {'Hsf'};
metaData.ecoCode.reprod  = {'O'};

metaData.T_typical  = C2K(13.2); % K, body temp
metaData.data_0     = {'ab'; 'am'; 'Lp'; 'Li'; 'Wwb'; 'Wwp'; 'Wwi'; 'GSI'}; 
metaData.data_1     = {'t-L'; 'L-Ww'}; 
metaData.COMPLETE = 2.5; % using criteria of LikaKear2011

metaData.author   = {'Bas Kooijman'};        
metaData.date_subm = [2025 01 02];                           
metaData.email    = {'bas.kooijman@vu.nl'};                 
metaData.address  = {'VU University, Amsterdam'}; 

metaData.curator     = {'Dina Lika'};
metaData.email_cur   = {'lika@uoc.gr'}; 
metaData.date_acc    = [2025 01 02];

%% set data
% zero-variate data
data.ab = 5; units.ab = 'd';    label.ab = 'age at birth';           bibkey.ab = 'guess';   
  temp.ab = C2K(13.2);  units.temp.ab = 'K'; label.temp.ab = 'temperature';
data.am = 11*365; units.am = 'd';    label.am = 'life span';              bibkey.am = 'RovaCard2017';   
  temp.am = C2K(13.2);  units.temp.am = 'K'; label.temp.am = 'temperature';

data.Lp = 14.1;     units.Lp = 'cm';   label.Lp = 'total length at puberty';bibkey.Lp = 'fishbase';
data.Li = 19.5;     units.Li = 'cm';   label.Li = 'ultimate total length';  bibkey.Li = 'fishbase';

data.Wwb = 5.2e-4;  units.Wwb = 'g';    label.Wwb = 'wet weight at birth';    bibkey.Wwb = 'VillDiez2023';
  comment.Wwb = 'based on egg diameter of 1 mm: pi/6*0.1^3';
data.Wwp = 36;   units.Wwp = 'g';    label.Wwp = 'wet weight at puberty';  bibkey.Wwp = 'fishbase';
  comment.Wwp = 'based on 0.01072*Lp^3.07, see F1';
data.Wwi = 98;   units.Wwi = 'g';    label.Wwi = 'ultimate wet weight';    bibkey.Wwi = 'fishbase';
  comment.Wwi = 'based on 0.01072*Li^3.07, see F1';

data.GSI = 0.03; units.GSI = 'g/g';  label.GSI = 'gonado somatic index';    bibkey.GSI = 'RovaCard2017';
  temp.GSI = C2K(13.2); units.temp.GSI = 'K'; label.temp.GSI = 'temperature';

% uni-variate data
% time-length
data.tL = [ ... % time since birth (yr), total length (cm)
0.788	7.642
1.077	14.764
1.088	8.292
1.088	9.521
1.154	8.842
1.154	10.460
1.165	12.791
1.188	14.538
1.210	11.205
1.232	12.241
1.254	11.756
1.254	10.849
1.254	11.335
1.276	9.393
1.287	11.465
1.287	8.811
1.343	12.662
1.354	9.782
1.365	13.957
1.387	13.439
1.398	11.821
1.421	10.236
1.432	13.504
1.554	11.628
1.565	10.366
1.587	12.599
1.598	12.081
1.632	12.243
1.654	12.179
1.665	11.079
1.676	9.687
1.720	12.535
1.765	14.315
1.776	11.403
1.809	13.377
1.865	8.912
1.876	14.705
1.876	12.698
1.920	16.873
2.020	11.437
2.053	13.832
2.064	16.745
2.064	16.259
2.075	16.162
2.087	18.072
2.087	12.732
2.098	14.382
2.109	13.023
2.120	14.059
2.142	16.486
2.142	14.156
2.153	12.700
2.164	16.616
2.175	12.279
2.186	15.451
2.186	11.114
2.220	15.063
2.231	14.901
2.253	16.325
2.253	16.196
2.264	16.034
2.264	15.257
2.286	17.685
2.286	12.927
2.297	12.280
2.320	17.232
2.320	14.707
2.331	15.549
2.331	13.542
2.364	13.219
2.397	15.226
2.431	15.032
2.531	16.262
2.597	14.256
2.597	15.389
2.597	16.716
2.619	12.509
2.730	14.095
2.730	16.037
2.741	14.451
2.752	16.361
2.752	13.189
2.752	12.963
2.764	14.678
2.764	16.620
2.764	16.717
2.764	17.688
2.775	14.937
2.775	15.196
2.775	15.455
2.775	15.649
2.775	17.008
2.775	17.332
2.863	15.326
2.875	18.044
2.875	14.808
2.919	17.850
2.919	15.779
2.930	16.880
2.941	17.042
2.952	14.970
2.997	12.188
3.008	12.382
3.019	14.485
3.030	14.809
3.041	16.395
3.074	17.042
3.074	13.741
3.108	17.658
3.108	18.531
3.130	18.758
3.163	12.804
3.174	12.383
3.185	17.949
3.208	15.975
3.208	14.648
3.263	16.332
3.285	15.911
3.296	12.869
3.307	17.270
3.319	14.067
3.330	16.526
3.341	16.170
3.352	17.012
3.352	13.711
3.363	17.336
3.363	17.627
3.374	18.112
3.385	14.423
3.396	14.035
3.441	16.559
3.441	16.236
3.441	15.200
3.452	16.754
3.452	15.621
3.452	14.877
3.507	16.948
3.507	16.398
3.518	17.434
3.518	15.913
3.518	15.621
3.541	16.625
3.541	16.819
3.574	17.272
3.574	16.334
3.585	18.178
3.585	17.110
3.607	16.884
3.618	17.467
3.651	16.593
3.663	17.111
3.663	17.758
3.663	18.049
3.674	17.402
3.696	16.011
3.729	17.273
3.740	17.662
3.751	16.885
3.762	16.400
3.762	17.921
3.774	18.924
3.774	16.659
3.785	15.332
3.785	15.753
3.785	16.108
3.796	14.134
3.818	15.461
3.851	16.497
3.862	15.818
3.862	16.692
3.873	16.141
3.896	17.727
3.918	15.721
3.929	18.051
3.929	16.918
3.929	16.595
3.940	17.436
4.007	14.524
4.029	16.822
4.040	16.304
4.051	17.534
4.051	13.554
4.084	17.081
4.107	19.347
4.118	17.599
4.162	18.506
4.173	16.079
4.184	16.338
4.184	16.694
4.195	16.953
4.195	17.956
4.251	15.238
4.251	16.856
4.273	17.277
4.273	18.862
4.306	19.348
4.317	17.212
4.329	18.248
4.362	17.536
4.440	18.993
4.440	19.252
4.451	16.954
4.495	16.922
4.506	17.311
4.539	18.508
4.584	17.732
4.584	16.761
4.595	17.926
4.595	17.085
4.617	17.473
4.628	17.085
4.650	16.114
4.650	18.412
4.673	18.088
4.673	16.794
4.684	17.118
4.684	18.541
4.684	17.862
4.717	16.762
4.739	17.150
4.750	16.406
4.750	17.280
4.772	17.506
4.772	18.057
4.772	18.154
4.784	16.212
4.784	16.762
4.817	16.342
4.839	15.533
4.883	18.316
4.928	17.507
4.950	17.346
5.028	18.641
5.028	16.893
5.028	14.498
5.250	18.124
5.283	17.801
5.350	16.831
5.427	19.550
5.527	18.773
5.527	18.353
5.538	19.324
5.572	17.706
5.583	17.123
5.583	18.256
5.594	18.612
5.605	18.353
5.616	17.997
5.660	18.386
5.671	18.872
5.694	18.160
5.760	17.481
5.771	17.739
5.782	18.354
5.805	16.736
5.805	18.128
5.805	19.487
5.860	18.840
5.871	15.928
5.882	18.258
5.882	18.485
6.016	18.162
6.204	18.228
6.371	18.196
6.526	17.583
6.693	18.619
6.759	17.908
6.859	18.329
6.881	15.805
6.948	18.783
6.948	19.527
7.037	17.036
7.436	18.624
7.592	18.366
7.703	18.755
7.769	17.752
8.024	18.660
8.679	18.373
8.701	17.370
8.812	18.536]; 
data.tL(:,1) = 365 * (0+data.tL(:,1)); % convert yr to d
for i=2:size(data.tL,1); if data.tL(i,1)<=data.tL(i-1,1);data.tL(i,1)=data.tL(i-1,1)+1e-3;end;end
units.tL = {'d', 'cm'};  label.tL = {'time since birth', 'total length'};  
temp.tL = C2K(13.2);  units.temp.tL = 'K'; label.temp.tL = 'temperature';
bibkey.tL = 'RovaCard2017'; 

% length-weight
data.LW = [ ... % total length (cm), weight (g)
7.652	0.605
8.414	0.752
8.488	0.873
8.611	0.945
8.735	1.114
9.055	1.284
9.252	1.308
9.350	1.092
9.646	1.767
9.940	1.287
10.359	1.722
10.409	1.818
10.483	1.938
10.655	1.939
10.876	2.084
11.074	2.350
11.124	3.264
11.369	2.495
11.442	2.158
11.468	2.784
11.517	2.592
11.665	2.857
11.762	2.376
11.788	2.761
11.788	3.074
11.811	2.112
12.009	3.003
12.107	2.305
12.255	2.883
12.329	3.028
12.379	3.269
12.379	3.365
12.477	3.197
12.501	2.980
12.503	4.015
12.552	3.606
12.602	4.208
12.674	3.534
12.748	3.246
12.797	3.415
12.822	3.631
12.847	3.992
12.873	4.642
12.921	3.800
12.922	4.738
12.945	3.680
12.995	3.776
12.995	4.257
13.020	4.474
13.045	4.474
13.069	4.185
13.142	3.849
13.144	4.595
13.192	3.945
13.266	3.970
13.266	4.186
13.291	4.667
13.388	3.705
13.413	4.066
13.439	4.836
13.463	4.427
13.487	4.211
13.490	5.631
13.511	3.610
13.585	3.778
13.586	4.596
13.612	5.198
13.660	4.452
13.686	5.415
13.733	4.188
13.759	5.150
13.760	5.487
13.783	4.814
13.808	5.006
13.856	4.068
13.857	4.333
13.906	4.694
13.931	4.910
13.956	5.344
13.979	4.141
13.980	4.550
13.982	5.656
14.005	4.766
14.005	5.055
14.056	6.282
14.079	5.272
14.128	4.622
14.129	5.488
14.153	5.104
14.177	5.007
14.202	5.152
14.227	5.344
14.251	4.863
14.251	4.960
14.252	5.321
14.252	5.754
14.253	6.451
14.301	5.609
14.302	6.067
14.324	4.671
14.326	5.561
14.376	6.500
14.399	5.273
14.401	6.259
14.424	5.105
14.424	5.586
14.425	5.899
14.426	6.500
14.448	5.153
14.474	6.115
14.498	5.682
14.521	4.720
14.546	4.840
14.549	6.789
14.600	7.800
14.621	5.490
14.622	6.188
14.646	5.948
14.671	5.851
14.673	7.223
14.696	6.309
14.719	5.394
14.744	5.539
14.745	5.876
14.794	5.948
14.795	6.598
14.817	5.274
14.818	5.708
14.844	6.285
14.866	4.962
14.869	7.103
14.893	6.165
14.893	6.381
14.917	6.165
14.941	5.708
14.943	6.863
15.015	5.877
15.041	6.406
15.063	5.107
15.065	6.069
15.065	6.623
15.067	7.417
15.067	7.513
15.089	5.733
15.090	6.334
15.115	6.888
15.138	5.685
15.139	6.406
15.139	6.671
15.165	7.080
15.189	6.864
15.241	8.644
15.260	5.252
15.263	6.840
15.285	5.541
15.287	6.575
15.288	7.177
15.314	8.067
15.335	5.902
15.337	6.936
15.339	8.260
15.385	6.504
15.409	6.167
15.409	6.383
15.410	6.480
15.411	7.538
15.412	7.947
15.435	7.153
15.435	7.250
15.436	7.635
15.458	5.758
15.458	5.758
15.460	7.298
15.483	6.047
15.508	6.167
15.508	6.648
15.557	6.360
15.558	6.745
15.608	7.467
15.608	7.683
15.634	8.141
15.681	7.130
15.682	7.563
15.705	6.264
15.705	6.890
15.707	7.900
15.729	6.168
15.730	6.986
15.731	7.395
15.754	6.505
15.754	6.505
15.782	8.670
15.806	8.478
15.829	7.275
15.830	7.756
15.831	8.719
15.853	6.890
15.854	7.732
15.855	7.853
15.855	8.430
15.878	7.323
15.900	5.711
15.903	7.227
15.903	7.564
15.904	7.997
15.904	8.117
15.931	9.248
15.953	7.660
15.977	7.300
15.978	8.286
16.002	7.637
16.003	8.190
16.027	7.974
16.027	8.286
16.029	9.610
16.051	7.565
16.052	8.022
16.099	6.675
16.102	8.936
16.103	9.562
16.123	6.530
16.126	8.624
16.151	8.696
16.152	9.273
16.174	7.613
16.176	8.961
16.198	7.204
16.200	8.503
16.224	7.854
16.224	8.287
16.248	8.047
16.250	8.841
16.251	9.899
16.270	6.026
16.300	9.394
16.346	7.373
16.347	7.830
16.347	8.119
16.348	8.552
16.396	8.071
16.398	9.034
16.398	9.226
16.421	8.312
16.423	9.852
16.446	8.312
16.446	8.697
16.449	10.285
16.471	8.480
16.471	9.058
16.493	7.350
16.495	8.673
16.518	7.542
16.521	9.251
16.521	9.347
16.522	9.635
16.522	10.261
16.543	7.927
16.547	10.045
16.547	10.333
16.570	9.491
16.618	8.192
16.619	8.722
16.619	8.962
16.641	7.350
16.643	8.433
16.643	8.938
16.644	9.588
16.645	10.045
16.645	10.117
16.646	10.622
16.667	8.216
16.670	10.454
16.694	9.612
16.695	10.334
16.716	7.880
16.716	7.976
16.717	9.059
16.719	10.021
16.742	8.963
16.743	9.372
16.766	8.506
16.789	7.808
16.790	8.289
16.815	8.626
16.815	8.746
16.817	9.877
16.838	7.592
16.840	8.506
16.865	9.228
16.867	10.623
16.890	9.420
16.893	11.056
16.916	10.046
16.917	10.551
16.918	11.490
16.940	9.637
16.941	10.190
16.941	10.287
16.941	10.335
16.962	8.362
16.963	8.795
16.988	8.963
16.989	9.613
16.989	9.878
17.012	8.699
17.013	9.421
17.035	7.303
17.038	9.325
17.038	9.710
17.065	10.937
17.065	11.105
17.086	8.699
17.086	9.036
17.087	9.108
17.087	9.734
17.088	10.167
17.090	11.346
17.113	10.504
17.132	6.654
17.133	7.135
17.135	8.483
17.137	9.710
17.158	7.737
17.162	10.023
17.163	10.937
17.185	9.325
17.186	9.951
17.208	8.242
17.211	10.336
17.233	8.243
17.235	9.446
17.235	9.566
17.236	10.215
17.237	10.721
17.261	10.432
17.264	12.477
17.283	8.772
17.286	10.577
17.308	8.917
17.311	11.202
17.312	12.068
17.333	9.229
17.335	10.938
17.358	9.999
17.359	10.312
17.381	8.532
17.382	9.350
17.383	9.879
17.383	9.903
17.404	7.786
17.408	10.433
17.409	10.649
17.430	8.460
17.432	9.663
17.434	10.890
17.435	11.515
17.455	8.821
17.482	10.168
17.483	11.251
17.484	11.684
17.508	11.203
17.529	8.797
17.532	10.674
17.532	10.986
17.557	10.842
17.558	11.708
17.580	9.880
17.604	9.447
17.608	12.551
17.628	9.110
17.629	10.121
17.631	11.396
17.633	12.671
17.653	9.736
17.676	8.509
17.680	11.131
17.681	12.022
17.704	10.458
17.704	10.698
17.704	10.963
17.705	11.420
17.729	10.771
17.729	11.108
17.730	11.733
17.730	11.733
17.779	11.565
17.800	9.424
17.801	9.881
17.801	10.121
17.805	12.239
17.828	11.252
17.849	9.159
17.851	10.531
17.855	12.961
17.876	10.555
17.877	11.541
17.901	10.964
17.902	11.301
17.902	11.710
17.924	9.785
17.925	10.266
17.948	9.641
17.952	12.095
17.973	9.569
17.975	11.181
17.976	11.734
17.999	10.675
18.021	9.208
18.024	10.892
18.026	12.480
18.045	8.438
18.048	10.507
18.050	11.951
18.052	12.889
18.071	9.112
18.075	11.999
18.098	11.061
18.123	11.301
18.123	11.446
18.125	12.673
18.146	10.171
18.174	12.336
18.196	10.820
18.197	11.542
18.222	11.350
18.246	11.085
18.247	12.192
18.249	13.010
18.271	11.783
18.272	12.192
18.318	10.003
18.319	10.965
18.321	11.783
18.346	12.000
18.347	13.131
18.368	10.556
18.369	11.495
18.393	10.749
18.394	11.639
18.396	12.481
18.419	11.663
18.421	13.348
18.444	12.313
18.467	11.086
18.469	12.073
18.469	12.554
18.492	11.255
18.492	11.351
18.493	11.784
18.514	9.330
18.519	12.698
18.542	11.784
18.566	11.086
18.566	11.592
18.567	12.265
18.591	11.808
18.619	13.661
18.640	11.255
18.644	14.191
18.665	11.640
18.716	13.204
18.718	14.022
18.763	11.352
18.788	11.977
18.814	12.940
18.839	12.699
18.912	12.122
18.913	13.085
18.913	13.277
18.934	10.823
18.961	12.435
19.037	14.024
19.159	13.086
19.329	11.522
19.333	14.073
19.429	12.990
19.430	13.183
19.432	14.939
19.480	13.977
19.529	13.905
19.604	14.483]; 
units.LW = {'cm', 'g'}; label.LW = {'total length', 'weight'};  
bibkey.LW = 'RovaCard2017'; 

%% set weights for all real data
weights = setweights(data, []);
weights.tL = 5 * weights.tL;
weights.ab = weights.ab * 0;
weights.Wwp = weights.Wwp * 0;
weights.Wwi = weights.Wwi * 0;

%% set pseudodata and respective weights
[data, units, label, weights] = addpseudodata(data, units, label, weights);
weights.psd.k_J = 0; weights.psd.k = 0.1;

%% 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 not to differ from females';
D2 = 'Data Wwp and Wwi are ignored due to inconsistency with LW data';
metaData.discussion = struct('D1',D1, 'D2',D2);

%% Facts
F1 = 'length-weight: Ww in g = 0.01072*(TL in cm)^3.07'; 
metaData.bibkey.F1 = 'fishbase'; 
metaData.facts = struct('F1',F1);

%% Links
metaData.links.id_CoL = '38282'; % Cat of Life
metaData.links.id_ITIS = '167975'; % ITIS
metaData.links.id_EoL = '221545'; % Ency of Life
metaData.links.id_Wiki = 'Dules_auriga'; % Wikipedia
metaData.links.id_ADW = 'Dules_auriga'; % ADW
metaData.links.id_Taxo = '173432'; % Taxonomicon
metaData.links.id_WoRMS = '310626'; % WoRMS
metaData.links.id_fishbase = 'Dules-auriga'; % fishbase

%% References
bibkey = 'Wiki'; type = 'Misc'; bib = ...
'howpublished = {\url{http://en.wikipedia.org/wiki/wiki/Dules_auriga}}';  
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 = 'RovaCard2017'; type = 'article'; bib = [ ... 
'doi = {10.1111/jfb.13390}, ' ...
'author = {Rovani, A. T. and Cardoso, L. G.}, ' ... 
'year = {2017}, ' ...
'title = {Life history and initial assessment of fishing impacts on the by-catch species \emph{Dules auriga} ({T}eleostei: {S}erranidae) in southern {B}razil}, ' ...
'journal = {Journal of Fish Biology}, ' ...
'volume = {91(3)}, ' ...
'pages = {896–911}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'VillDiez2023'; type = 'article'; bib = [ ... 
'doi = {10.1643/i2022058}, ' ...
'author = {Luj\''{a}n Villanueva-Gomila and Mar\''{i}a E. Diez and Leonardo A. Venerus and Mar\''{i}a B. Caro-Torti and Cintia D. Medina, and Mart\''{i}n D. Ehrlich}, ' ... 
'year = {2023}, ' ...
'title = {Development and Occurrence of Early Stages of \emph{Dules auriga} in the Northern {P}atagonian {G}ulfs of {A}rgentina}, ' ...
'journal = {Ichthyology & Herpetology}, ' ...
'volume = {111(2)}, ' ...
'pages = {293-301}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'fishbase'; type = 'Misc'; bib = ...
'howpublished = {\url{https://www.fishbase.se/summary/Dules-auriga.html}}';  
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];

