  function [data, auxData, metaData, txtData, weights] = mydata_Hemiculter_leucisculus
%% set metadata
metaData.phylum     = 'Chordata'; 
metaData.class      = 'Actinopterygii'; 
metaData.order      = 'Cypriniformes'; 
metaData.family     = 'Cyprinidae';
metaData.species    = 'Hemiculter_leucisculus'; 
metaData.species_en = 'Sharpbelly'; 

metaData.ecoCode.climate = {'Am', 'Cwa'};
metaData.ecoCode.ecozone = {'TPi'};
metaData.ecoCode.habitat = {'0iFr'};
metaData.ecoCode.embryo  = {'Fg'};
metaData.ecoCode.migrate = {};
metaData.ecoCode.food    = {'biCi'};
metaData.ecoCode.gender  = {'D'};
metaData.ecoCode.reprod  = {'O'};

metaData.T_typical  = C2K(20); % 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 = [2024 05 06];                           
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 05 06];

%% set data
% zero-variate data
data.ab = 11;        units.ab = 'd';  label.ab = 'age at birth';           bibkey.ab = 'JohaNeg2003'; 
  temp.ab = C2K(20); units.temp.ab = 'K'; label.temp.ab = 'temperature';
data.am = 10*365;   units.am = 'd';  label.am = 'life span';              bibkey.am = 'fishbase';   
  temp.am = C2K(20); units.temp.am = 'K'; label.temp.am = 'temperature';

data.Lp = 5.6;    units.Lp = 'cm'; label.Lp = 'total length at puberty for females'; bibkey.Lp = 'WangJako2016'; 
data.Li = 29;    units.Li = 'cm'; label.Li = 'ultimate total length';  bibkey.Li = 'fishbase';

data.Wwb = 1.8e-4;   units.Wwb = 'g';  label.Wwb = 'wet weight at birth';    bibkey.Wwb = 'WangJako2016';
  comment.Wwb = 'Based on egg diameter of 0.7 mm: pi/6*0.07^3';
data.Wwp = 1.3;   units.Wwp = 'g';  label.Wwp = 'wet weight at puberty for females';    bibkey.Wwp = {'fishbase','WangJako2016'};
  comment.Wwp = 'based on 0.00676*Lp^3.07, see F1';
data.Wwi = 209;   units.Wwi = 'g';  label.Wwi = 'ultimate wet weight';    bibkey.Wwi = 'fishbase';
  comment.Wwi = 'based on 0.00676*Li^3.07, see F1';

data.GSI = 0.2 ; units.GSI = 'g/g';  label.GSI = 'gonado somatic index for female'; bibkey.GSI = 'WangJako2016'; 
  temp.GSI = C2K(20); units.temp.GSI = 'K'; label.temp.GSI = 'temperature';

% univariate data
% time-length
data.tL_f = [ ... % time (yr), std length (cm)
0.179	5.976
0.247	5.221
0.250	7.544
0.386	6.267
0.388	8.242
0.390	10.100
0.418	8.532
0.814	10.103
0.851	8.100
0.871	8.652
0.966	5.575
1.016	6.156
1.028	8.566
1.029	9.291
1.029	9.524
1.047	7.259
1.048	8.682
1.067	7.985
1.077	8.334
1.078	9.002
1.081	11.382
1.083	4.618
1.097	8.653
1.129	10.366
1.135	6.999
1.149	11.121
1.174	6.680
1.223	6.767
1.250	13.271
1.281	14.519
1.327	12.023
1.346	11.326
1.366	11.675
1.377	12.226
1.378	12.836
1.383	8.655
1.388	13.388
1.397	12.488
1.405	11.414
1.406	11.791
1.411	7.756
1.412	8.481
1.429	14.694
1.801	13.275
1.812	14.407
1.819	11.446
1.829	12.055
1.830	12.926
1.831	13.826
1.840	12.462
1.856	9.181
1.879	12.636
1.890	13.711
1.934	8.921
1.958	12.347
1.964	9.037
1.968	12.550
1.975	10.605
1.984	9.966
1.985	10.227
1.986	10.982
1.987	12.231
2.006	11.970
2.016	11.418
2.022	8.021
2.024	9.995
2.033	9.154
2.034	10.402
2.045	11.331
2.047	13.189
2.056	12.347
2.066	12.696
2.077	13.654
2.083	10.228
2.096	12.899
2.103	10.722
2.105	11.883
2.113	10.490
2.115	12.115
2.121	9.619
2.122	9.764
2.123	11.041
2.125	12.638
2.138	15.193
2.140	8.167
2.144	11.448
2.145	13.277
2.146	13.451
2.147	13.887
2.152	9.967
2.164	12.377
2.165	12.871
2.186	14.061
2.226	14.787
2.234	12.987
2.243	12.087
2.253	12.262
2.264	13.800
2.274	13.481
2.330	10.927
2.343	13.482
2.361	12.321
2.363	13.772
2.370	11.856
2.391	13.105
2.399	10.985
2.400	11.595
2.401	12.698
2.402	12.960
2.403	13.279
2.404	14.034
2.409	11.334
2.420	12.553
2.422	14.150
2.423	14.585
2.428	10.521
2.430	12.031
2.431	13.627
2.778	15.604
2.814	12.963
2.817	15.285
2.824	12.905
2.826	14.269
2.826	14.559
2.826	14.821
2.827	15.866
2.835	14.124
2.836	14.966
2.837	15.518
2.844	13.456
2.973	13.689
2.981	12.238
3.003	14.474
3.031	12.732
3.033	14.822
3.042	13.893
3.044	15.984
3.051	13.109
3.053	15.084
3.058	10.787
3.059	12.122
3.061	13.342
3.079	11.919
3.081	13.632
3.099	12.819
3.102	15.548
3.121	14.039
3.131	14.678
3.139	13.226
3.141	14.997
3.162	15.984
3.169	13.604
3.170	14.504
3.171	14.765
3.179	13.517
3.180	14.330
3.181	15.404
3.209	13.952
3.251	16.159
3.396	14.215
3.406	13.896
3.406	14.041
3.406	14.593
3.425	13.402
3.456	15.232
3.802	16.192
3.802	16.454
3.811	15.496
3.832	16.599
3.832	16.860
3.843	17.557
3.870	15.990
3.978	15.613
4.068	16.281
4.086	14.772
4.086	15.004
4.086	15.265
4.095	13.988
4.095	14.452
4.105	14.249
4.108	17.559
4.116	15.759
4.134	13.988
4.135	15.033
4.146	16.224
4.165	15.440
4.184	14.598
4.187	17.356
4.194	15.208
5.072	17.305
5.104	19.192
5.141	16.725
5.368	18.091
5.989	18.531
6.186	18.881
6.224	17.865
7.043	19.787
8.038	20.636];
data.tL_f(:,1) = 365 * (0.3 + data.tL_f(:,1)); % convert 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', 'std length','females'};  
temp.tL_f = C2K(20);  units.temp.tL_f = 'K'; label.temp.tL_f = 'temperature';
bibkey.tL_f = 'WangHuan2013'; 
%
data.tL_m = [ ... % time (yr), total length (cm)
0.109	4.465
0.220	6.760
0.257	5.221
0.359	8.009
0.367	6.819
0.367	7.022
0.387	6.558
0.407	7.400
0.455	6.152
0.760	5.660
0.782	7.722
0.790	6.503
0.803	9.812
0.957	6.533
0.987	6.301
0.988	7.898
0.996	5.778
1.005	5.314
1.045	5.895
1.057	7.608
1.066	6.534
1.076	6.737
1.084	5.489
1.107	7.986
1.116	7.463
1.136	7.899
1.156	8.334
1.196	8.538
1.203	6.390
1.205	7.754
1.206	9.322
1.207	9.931
1.214	7.203
1.217	10.193
1.226	9.670
1.232	5.606
1.252	6.100
1.265	8.887
1.275	9.322
1.305	9.932
1.316	10.252
1.316	10.832
1.336	11.123
1.344	9.555
1.383	8.859
1.414	9.991
1.414	10.194
1.425	10.891
1.434	10.572
1.443	9.701
1.452	9.149
1.806	8.542
1.808	10.691
1.817	10.023
1.818	10.575
1.827	9.559
1.838	11.010
1.868	11.243
1.868	11.504
1.876	10.023
1.895	9.443
1.896	10.401
1.906	9.762
1.926	10.837
1.927	11.156
1.937	11.650
2.016	11.534
2.044	10.779
2.124	11.622
2.488	11.305
3.020	12.151
3.532	12.823
4.055	13.436
4.449	13.961
4.990	14.372
5.522	14.753
5.995	14.931
6.477	15.167
7.029	15.461
8.033	15.729];
data.tL_m(:,1) = 365 * (0.3 + data.tL_m(:,1)); % convert 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', 'std length','males'};  
temp.tL_m = C2K(20);  units.temp.tL_m = 'K'; label.temp.tL_m = 'temperature';
bibkey.tL_m = 'WangHuan2013'; 

% length-weight
data.LW_f = [ ... % total length (cm), weight (g)
4.302	1.820
4.462	1.236
4.783	2.412
4.912	0.950
4.944	1.829
5.040	2.416
5.136	1.832
5.169	0.954
5.361	1.835
5.490	1.837
5.554	2.131
5.586	4.180
5.811	2.134
5.843	1.842
5.971	2.137
6.067	2.431
6.196	3.896
6.292	2.434
6.453	2.729
6.517	3.608
6.742	5.660
6.774	3.904
6.870	6.540
6.998	4.200
7.095	5.080
7.159	6.837
7.352	6.254
7.352	3.913
7.416	6.548
7.544	5.086
7.608	5.380
7.640	8.307
7.737	6.845
7.833	5.676
8.058	8.606
8.122	5.973
8.218	6.560
8.315	10.366
8.475	7.149
8.475	5.978
8.539	12.711
8.539	9.784
8.764	11.836
8.796	8.324
8.828	5.983
8.892	13.887
8.925	8.912
9.149	11.256
9.213	7.745
9.310	13.307
9.310	15.649
9.374	11.552
9.406	9.504
9.502	18.286
9.599	13.604
9.631	9.215
9.695	10.679
9.759	11.851
9.791	20.046
9.888	16.828
9.952	14.780
10.112	10.978
10.144	14.490
10.241	21.809
10.241	17.419
10.273	20.053
10.305	12.737
10.369	9.518
10.401	17.128
10.498	16.252
10.594	21.814
10.626	19.766
10.626	24.449
10.658	14.791
10.819	18.890
10.883	14.209
10.915	21.233
10.947	26.795
10.947	15.380
11.011	17.430
11.043	25.333
11.108	31.773
11.108	22.407
11.300	25.337
11.364	21.240
11.364	13.923
11.396	18.314
11.429	19.777
11.461	16.851
11.493	27.681
11.557	29.145
11.621	21.536
11.653	23.878
11.750	20.368
11.782	30.612
11.782	19.490
11.878	34.126
11.910	25.931
11.974	23.005
12.006	20.957
12.039	24.762
12.071	32.958
12.103	31.495
12.135	28.861
12.263	23.302
12.327	36.474
12.327	25.937
12.360	34.425
12.424	21.841
12.488	31.793
12.520	27.403
12.520	29.745
12.552	37.648
12.552	42.916
12.745	38.528
12.777	32.968
12.809	30.920
12.841	35.018
12.841	25.944
12.905	44.970
12.905	27.994
12.905	25.360
12.937	40.287
13.034	37.362
13.066	30.338
13.098	40.875
13.130	34.437
13.162	44.974
13.162	37.071
13.258	46.438
13.258	31.219
13.355	40.001
13.387	42.928
13.387	31.221
13.451	35.027
13.515	42.930
13.547	33.272
13.644	46.444
13.676	50.249
13.676	43.225
13.676	37.079
13.676	39.713
13.740	30.348
13.772	34.446
13.836	44.105
13.868	50.838
13.900	40.887
13.997	45.864
13.997	36.791
14.093	33.573
14.125	38.841
14.157	35.622
14.189	48.793
14.222	54.940
14.222	43.526
14.222	43.233
14.318	61.088
14.350	52.015
14.350	42.064
14.414	55.528
14.414	40.602
14.414	37.089
14.478	51.432
14.510	53.774
14.510	44.993
14.575	48.214
14.671	51.727
14.671	45.581
14.703	56.703
14.703	42.947
14.864	65.486
14.864	48.803
14.896	45.292
14.992	60.805
14.992	52.610
15.056	58.757
15.056	53.196
15.152	64.320
15.152	62.271
15.152	56.125
15.152	44.417
15.185	67.540
15.249	51.736
15.249	46.175
15.313	58.761
15.441	60.226
15.441	57.300
15.441	54.958
15.506	65.496
15.506	63.447
15.506	53.203
15.730	60.816
15.762	65.792
15.891	63.160
15.891	64.916
15.987	69.600
16.116	71.651
16.116	61.407
16.533	82.194
16.629	86.585
16.629	76.927
16.661	94.488
16.790	79.563
16.822	80.149
17.303	89.815
17.368	75.181
17.432	88.060
17.528	93.623
17.881	94.213
18.170	90.998
18.748	93.348
19.133	113.549];
units.LW_f = {'cm', 'g'}; label.LW_f = {'std length','weight','females'};  
bibkey.LW_f = 'WangHuan2013'; 
%
data.LW_m = [ ... % total length (cm), weight (g)
4.616	1.159
4.638	1.159
4.837	0.942
4.859	1.232
4.992	1.232
5.124	0.942
5.124	1.304
5.279	2.391
5.345	1.594
5.367	1.957
5.434	2.246
5.500	1.667
5.544	1.884
5.655	2.246
5.655	2.536
5.787	1.739
5.810	3.043
5.875	1.957
5.942	2.536
6.163	2.391
6.185	2.826
6.296	3.406
6.406	3.333
6.472	2.464
6.494	2.899
6.561	3.043
6.605	3.623
6.716	3.841
6.737	3.043
6.870	3.333
6.958	2.174
6.959	4.203
6.981	3.913
7.026	5.072
7.157	3.043
7.157	3.696
7.246	4.203
7.291	5.145
7.313	4.855
7.356	3.913
7.401	5.217
7.467	4.420
7.511	4.058
7.600	5.072
7.733	6.159
7.755	5.362
7.776	4.710
7.865	5.652
7.932	6.304
7.954	5.942
8.000	8.406
8.086	5.725
8.088	7.826
8.109	6.812
8.196	5.290
8.199	8.623
8.219	6.304
8.220	7.681
8.351	5.652
8.397	7.754
8.418	6.957
8.420	8.478
8.462	6.377
8.487	10.362
8.552	7.681
8.575	9.058
8.595	7.101
8.616	5.652
8.641	9.420
8.662	8.188
8.752	10.000
8.838	6.812
8.860	6.522
8.861	8.043
8.861	8.623
8.906	9.275
8.906	9.783
8.907	10.725
8.926	7.101
8.927	7.319
8.930	12.536
8.949	7.536
9.061	9.855
9.082	8.841
9.126	8.261
9.128	10.217
9.128	11.159
9.130	13.261
9.173	12.029
9.193	9.275
9.260	10.797
9.263	14.928
9.304	10.507
9.325	8.406
9.326	9.783
9.347	8.768
9.371	11.594
9.414	9.420
9.437	11.014
9.460	12.319
9.461	12.754
9.569	9.565
9.569	10.217
9.592	11.232
9.637	12.101
9.637	12.826
9.638	13.696
9.657	9.275
9.661	14.348
9.702	10.362
9.748	13.261
9.769	12.174
9.791	11.377
9.860	15.072
9.901	10.870
9.902	12.754
9.903	13.696
9.968	12.464
9.969	14.130
9.990	11.812
10.099	10.000
10.122	11.014
10.126	16.594
10.169	15.362
10.189	12.391
10.190	13.188
10.210	10.290
10.211	11.667
10.213	14.493
10.256	13.551
10.257	14.710
10.258	16.087
10.281	17.826
10.321	11.667
10.366	12.319
10.366	12.826
10.369	16.304
10.434	14.783
10.455	14.058
10.478	15.362
10.479	16.159
10.569	18.406
10.590	17.029
10.610	14.420
10.611	15.580
10.612	17.464
10.631	13.333
10.678	16.957
10.745	17.971
10.747	21.014
10.765	15.290
10.786	13.986
10.788	16.014
10.790	18.913
10.811	17.391
10.837	22.029
10.878	18.478
10.879	19.348
10.897	14.348
10.920	15.000
10.940	13.116
10.941	13.768
10.965	16.884
10.966	17.681
10.966	18.043
10.991	22.464
11.013	21.159
11.033	19.348
11.051	13.188
11.100	20.362
11.143	18.623
11.163	15.000
11.206	14.420
11.208	16.812
11.209	18.116
11.236	24.348
11.252	16.087
11.254	19.710
11.275	17.319
11.321	20.870
11.385	17.754
11.408	18.623
11.429	16.667
11.499	21.449
11.519	19.855
11.525	27.246
11.540	18.188
11.542	20.652
11.673	18.768
11.696	19.493
11.697	21.667
11.721	23.696
11.744	24.638
11.763	20.145
11.783	17.609
11.787	22.826
11.871	17.754
11.873	20.435
11.942	24.348
11.987	25.580
12.120	25.435
12.273	23.261
12.407	25.435];
units.LW_m = {'cm', 'g'}; label.LW_m = {'std length','weight','males'};  
bibkey.LW_m = 'WangHuan2013'; 

%% set weights for all real data
weights = setweights(data, []);
weights.tL_f = weights.tL_f * 3;
weights.tL_m = weights.tL_m * 3;
weights.Wwi = weights.Wwi * 0;
weights.Wwp = weights.Wwp * 0;
weights.ab = weights.ab * 0;

%% 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_f','tL_m'}; subtitle1 = {'Data for females, males'};
set2 = {'LW_f','LW_m'}; subtitle2 = {'Data for females, males'};
metaData.grp.sets = {set1,set2};
metaData.grp.subtitle = {subtitle1,subtitle2};

%% Discussion points
D1 = 'Males are assumed to differ from females by {p_Am} only';
D2 = 'Wwi and Wwp are ignored due to inconsistency';
metaData.discussion = struct('D1',D1, 'D2',D2);

%% Facts
F1 = 'TL-weight: weight (in g) = 0.00676*(TL in cm)^3.07';
metaData.bibkey.F1 = 'fishbase'; 
metaData.facts = struct('F1',F1);

%% Links
metaData.links.id_CoL = '6LPMT'; % Cat of Life
metaData.links.id_ITIS = '689210'; % ITIS
metaData.links.id_EoL = ''; % Ency of Life
metaData.links.id_Wiki = 'Hemiculter_leucisculus'; % Wikipedia
metaData.links.id_ADW = 'Hemiculter_leucisculus'; % ADW
metaData.links.id_Taxo = '43704'; % Taxonomicon
metaData.links.id_WoRMS = '281021'; % WoRMS
metaData.links.id_fishbase = 'Hemiculter-leucisculus'; % fishbase

%% References
bibkey = 'Wiki'; type = 'Misc'; bib = ...
'howpublished = {\url{https://en.wikipedia.org/wiki/Hemiculter_leucisculus}}';  
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 = 'WangHuan2013'; type = 'article'; bib = [ ... 
'doi = {10.1111/jai.12287}, ' ...
'author = {T. Wang and D. Huang and Y. Zhao and H.Wang and S.Hu and J. Shen}, ' ...
'year = {2013}, ' ...
'title = {Age, growth and mortality of invasive sharpbelly, \emph{Hemiculter leucisculus} ({B}asilewski, 1855) in {E}rhai {L}ake, {C}hina}, ' ... 
'journal = {J. Appl. Ichthyol.}, ' ...
'volume = {29}, ' ...
'pages = {1279–1285}, ' ...
'howpublished = {url{https://aquadocs.org/handle/1834/33381}}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'WangJako20162013'; type = 'article'; bib = [ ... 
'doi = {10.1111/jai.13001}, ' ...
'author = {T. Wang and I. Jakovli and D. Huang and J.-G. Wang and J.-Z. Shen}, ' ...
'year = {2016}, ' ...
'title = {Reproductive strategy of the invasive sharpbelly, \emph{Hemiculter leucisculus} ({B}asilewsky 1855), in {E}rhai {L}ake, {C}hina}, ' ... 
'journal = {J. Appl. Ichthyol.}, ' ...
'volume = {32}, ' ...
'pages = {324–331}, ' ...
'howpublished = {url{https://aquadocs.org/handle/1834/33381}}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'fishbase'; type = 'Misc'; bib = ...
'howpublished = {\url{https://www.fishbase.se/summary/Hemiculter_leucisculus}}';  
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];

