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

%% set metaData
metaData.phylum     = 'Arthropoda'; 
metaData.class      = 'Malacostraca'; 
metaData.order      = 'Decapoda'; 
metaData.family     = 'Portunidae';
metaData.species    = 'Charybdis_bimaculata'; 
metaData.species_en = 'Two-spot swimming crab'; 

metaData.ecoCode.climate = {'MB'};
metaData.ecoCode.ecozone = {'MPSE', 'MIW'};
metaData.ecoCode.habitat = {'0bMb', 'bjMp', 'jiMb'};
metaData.ecoCode.embryo  = {'Mbf'};
metaData.ecoCode.migrate = {};
metaData.ecoCode.food    = {'bjP', 'biD', 'jiCi', 'jiS'};
metaData.ecoCode.gender  = {'D'};
metaData.ecoCode.reprod  = {'O'};

metaData.T_typical  = C2K(21); % K, body temp
metaData.data_0     = {'am','Lb','Lp','Wwb'}; 
metaData.data_1     = {'t-L','L-Ww','L-N'}; 

metaData.COMPLETE = 2.5; % using criteria of LikaKear2011

metaData.author   = {'Bas Kooijman'};    
metaData.date_subm = [2016 12 28];              
metaData.email    = {'bas.kooijman@vu.nl'};            
metaData.address  = {'VU University Amsterdam'};   

metaData.curator     = {'Starrlight Augustine'};
metaData.email_cur   = {'starrlight.augustine@akvaplan.niva.no'}; 
metaData.date_acc    = [2017 01 02]; 

%% set data
% zero-variate data

data.am = 1.2*365;    units.am = 'd';    label.am = 'life span';                bibkey.am = 'DoiYoko2008';   
  temp.am = C2K(21);  units.temp.am = 'K'; label.temp.am = 'temperature'; 
  comment.am = 'between 11 and 19 months';

data.Lb  = 0.031;   units.Lb  = 'cm';  label.Lb  = 'diameter pigmented eyed egg';   bibkey.Lb  = 'DoiYoko2008';
  comment.Lb = 'diameter of non-pigmented eyed eggs were 0.028 cm diameter';
data.Lp  = 1.183;  units.Lp  = 'cm';  label.Lp  = 'carapace length at puberty for females'; bibkey.Lp  = 'DoiYoko2008';
data.Lpm = 1.25;   units.Lpm = 'cm';  label.Lpm = 'carapace length at puberty for males'; bibkey.Lpm  = 'DoiYoko2008';

data.Wwb = 1.56e-5; units.Wwb = 'g';   label.Wwb = 'wet weight at birth';     bibkey.Wwb = 'DoiYoko2008';
  comment.Wwb = 'based on spherical shape of eggs with diameter Lb and 1 g/cm^3';

% uni-variate data
% t-L data
data.tL_f = [ ... % time since 1 Oct 2003 (d), carapace length (cm)
25.710	1.035
57.263	1.106
95.048	1.334
111.798	1.261
141.403	1.338
174.125	1.440
205.678	1.598
238.789	1.725
265.277	1.680
299.168	1.698
328.773	1.674
355.261	1.773];  
data.tL_f(:,1) = data.tL_f(:,1) + 3.5*30.5; % convert time to time since birth
units.tL_f   = {'d', 'cm'};  label.tL_f = {'time since birth', 'carapace length', 'female'};  
temp.tL_f    = C2K(21);  units.temp.tL_f = 'K'; label.temp.tL_f = 'temperature';
bibkey.tL_f = {'DoiYoko2008','seatemp'};
comment.tL_f = 'Data for females; temperature varies between 14 and 28 C';
%
% t-L data
data.tL_m = [ ... % time since 1 Oct 2003 (d), carapace length (cm)
25.710	1.063
57.263	1.204
111.019	1.348
141.793	1.471
174.125	1.556
204.899	1.707
238.789	1.756
264.888	1.799
299.168	1.929
328.383	1.950
354.872	1.919];  
data.tL_m(:,1) = data.tL_m(:,1) + 3.5*30.5; % convert time to time since birth
units.tL_m   = {'d', 'cm'};  label.tL_m = {'time since birth', 'carapace length', 'male'};  
temp.tL_m    = C2K(21);  units.temp.tL_m = 'K'; label.temp.tL_m = 'temperature';
bibkey.tL_m = 'DoiYoko2008';
comment.tL_m = 'Data for males; temperature varies between 14 and 28 C';

% L-W data
data.LW_f = [ ... % carapace length (cm), wet weight (g)
0.541	0.146
0.588	0.167
0.612	0.167
0.630	0.167
0.644	0.209
0.677	0.147
0.686	0.230
0.700	0.127
0.703	0.334
0.724	0.272
0.727	0.210
0.739	0.168
0.742	0.334
0.762	0.335
0.762	0.231
0.771	0.106
0.783	0.293
0.795	0.231
0.806	0.314
0.809	0.128
0.815	0.377
0.818	0.356
0.830	0.398
0.848	0.439
0.853	0.377
0.859	0.398
0.862	0.543
0.883	0.377
0.889	0.481
0.901	0.502
0.904	0.336
0.912	0.232
0.921	0.481
0.921	0.585
0.933	0.336
0.936	0.585
0.945	0.606
0.945	0.502
0.959	0.357
0.971	0.420
0.971	0.648
0.980	0.565
0.986	0.710
0.992	0.441
0.998	0.752
1.004	0.420
1.013	0.690
1.018	0.565
1.033	0.628
1.039	0.732
1.039	0.835
1.045	0.483
1.051	0.503
1.054	0.835
1.057	0.421
1.063	0.836
1.066	0.587
1.071	0.773
1.071	0.504
1.077	0.836
1.080	0.587
1.089	0.732
1.089	0.836
1.089	0.898
1.095	0.587
1.098	0.670
1.101	0.898
1.110	0.940
1.113	0.753
1.113	0.691
1.113	0.525
1.124	0.380
1.127	0.836
1.127	1.023
1.130	0.940
1.136	0.712
1.136	0.836
1.136	0.919
1.136	1.085
1.145	0.587
1.151	1.148
1.160	0.754
1.163	0.671
1.166	0.899
1.172	1.023
1.177	0.505
1.177	0.754
1.177	1.148
1.180	0.588
1.180	0.982
1.183	1.127
1.189	1.314
1.189	0.795
1.192	0.609
1.192	1.148
1.204	0.650
1.210	0.858
1.210	0.962
1.213	1.128
1.216	1.252
1.219	0.692
1.219	0.920
1.222	1.148
1.228	1.045
1.230	0.754
1.230	1.273
1.242	0.837
1.242	0.941
1.245	1.335
1.248	1.252
1.248	1.128
1.257	1.086
1.257	0.962
1.263	1.211
1.275	1.315
1.278	1.294
1.278	1.481
1.281	0.962
1.281	1.149
1.289	1.066
1.289	1.274
1.289	1.398
1.289	1.481
1.292	0.838
1.301	1.336
1.304	0.962
1.307	1.025
1.307	0.631
1.310	1.440
1.319	1.419
1.322	1.295
1.325	1.025
1.325	1.129
1.325	1.419
1.331	0.921
1.331	1.295
1.331	1.523
1.336	0.818
1.336	1.004
1.336	1.606
1.342	1.087
1.345	1.191
1.348	0.921
1.348	1.502
1.351	1.004
1.357	1.648
1.357	1.689
1.360	1.254
1.360	1.171
1.363	1.378
1.366	1.606
1.372	1.689
1.372	1.337
1.375	1.171
1.375	0.984
1.378	1.627
1.378	1.482
1.381	0.901
1.395	1.067
1.395	1.420
1.395	1.607
1.395	1.752
1.398	1.814
1.404	1.503
1.407	1.358
1.410	1.752
1.413	1.607
1.416	1.192
1.419	1.503
1.425	1.752
1.431	1.192
1.431	1.379
1.431	1.607
1.431	1.918
1.434	1.690
1.440	2.001
1.443	1.192
1.443	1.337
1.443	1.607
1.443	1.835
1.451	2.001
1.451	1.358
1.451	1.462
1.454	1.877
1.460	1.815
1.460	1.690
1.460	1.545
1.463	1.379
1.466	2.001
1.466	2.126
1.469	1.649
1.472	1.545
1.472	0.902
1.475	1.234
1.478	2.188
1.481	1.919
1.481	2.002
1.487	1.525
1.490	1.276
1.490	1.815
1.490	2.147
1.493	1.753
1.493	1.753
1.496	1.255
1.496	1.172
1.498	2.064
1.501	1.525
1.504	2.292
1.510	1.753
1.510	2.251
1.513	1.608
1.513	1.753
1.513	2.168
1.519	1.421
1.522	1.753
1.522	2.044
1.528	1.961
1.534	2.313
1.537	1.587
1.537	1.732
1.537	1.795
1.537	2.127
1.540	2.230
1.540	2.396
1.543	1.940
1.549	1.691
1.552	2.459
1.554	2.521
1.560	0.944
1.560	1.587
1.560	1.961
1.560	1.836
1.560	2.106
1.563	1.422
1.563	1.546
1.563	1.691
1.566	2.334
1.566	2.915
1.569	2.459
1.572	1.318
1.572	1.961
1.572	2.666
1.584	2.335
1.584	2.604
1.584	1.920
1.587	2.438
1.590	2.044
1.593	1.795
1.596	2.542
1.596	1.733
1.607	1.920
1.610	2.169
1.610	1.733
1.610	2.729
1.613	2.107
1.613	2.293
1.616	2.854
1.619	1.339
1.619	2.563
1.619	2.667
1.625	1.816
1.628	3.061
1.631	1.837
1.631	1.837
1.631	2.065
1.631	2.356
1.631	2.501
1.631	2.812
1.634	1.609
1.634	2.937
1.637	2.273
1.637	2.273
1.643	1.941
1.643	2.895
1.646	2.273
1.649	1.692
1.649	1.173
1.655	2.003
1.655	2.149
1.655	2.771
1.658	2.667
1.658	3.082
1.660	1.837
1.660	2.439
1.663	2.439
1.666	1.983
1.666	2.958
1.666	3.062
1.678	1.630
1.678	2.398
1.678	2.398
1.678	2.253
1.678	2.937
1.681	2.087
1.684	2.170
1.684	2.626
1.684	2.813
1.684	3.083
1.690	2.273
1.693	2.937
1.696	1.610
1.696	2.522
1.702	2.709
1.705	2.004
1.708	2.108
1.708	3.145
1.714	2.564
1.714	2.896
1.716	2.543
1.719	2.336
1.722	3.290
1.722	3.394
1.725	3.062
1.725	3.207
1.728	2.419
1.734	2.855
1.737	3.083
1.737	3.208
1.737	3.457
1.749	2.606
1.749	2.876
1.749	3.083
1.752	2.378
1.752	2.067
1.752	3.374
1.758	2.316
1.761	2.585
1.761	2.710
1.761	2.980
1.764	1.984
1.764	3.498
1.769	2.689
1.772	2.814
1.772	3.125
1.775	2.482
1.775	3.125
1.775	3.457
1.775	3.623
1.781	2.959
1.784	3.291
1.784	3.187
1.787	2.669
1.790	3.146
1.790	3.457
1.793	2.565
1.796	2.752
1.799	3.623
1.802	3.270
1.802	3.229
1.805	2.503
1.808	3.685
1.817	2.150
1.817	2.233
1.817	3.167
1.820	2.752
1.820	3.665
1.820	3.582
1.825	2.503
1.825	2.959
1.825	3.271
1.828	2.711
1.828	3.893
1.831	3.582
1.834	2.586
1.834	3.063
1.834	3.416
1.837	3.748
1.840	3.167
1.840	2.897
1.846	3.831
1.849	3.748
1.849	3.935
1.852	2.794
1.852	3.416
1.855	3.292
1.867	2.835
1.870	3.209
1.873	3.790
1.873	3.997
1.876	3.914
1.876	4.246
1.878	2.773
1.878	4.080
1.881	3.499
1.884	2.711
1.890	2.545
1.890	3.790
1.890	3.666
1.890	3.396
1.890	3.977
1.893	4.101
1.893	4.392
1.899	3.977
1.902	2.794
1.908	3.479
1.911	3.396
1.914	3.811
1.914	4.122
1.917	2.981
1.917	4.247
1.917	4.599
1.926	3.521
1.926	4.434
1.929	3.770
1.929	4.330
1.931	4.620
1.937	4.143
1.940	3.894
1.949	4.081
1.949	4.268
1.949	4.579
1.952	2.982
1.958	3.065
1.958	3.251
1.961	4.372
1.973	3.749
1.973	3.957
1.976	4.538
1.976	4.123
1.979	3.044
1.984	4.247
1.999	3.999
1.999	5.077
2.008	4.393
2.014	4.912
2.020	3.978
2.023	5.327
2.032	4.497
2.049	4.995
2.052	4.684
2.061	5.223
2.067	4.165
2.093	5.141
2.096	4.684
2.102	5.825
2.126	5.452];  
units.LW_f   = {'cm', 'g'};  label.LW_f = {'carapace length', 'wet weight', 'female'};  
bibkey.LW_f = 'DoiYoko2008';
comment.LW_f = 'Data for females';
%
data.LW_m = [ ... % carapace length (cm), wet weight (g)
0.532	0.124
0.626	0.165
0.703	0.247
0.703	0.143
0.726	0.205
0.735	0.164
0.744	0.309
0.744	0.184
0.768	0.329
0.782	0.350
0.782	0.205
0.794	0.204
0.800	0.246
0.806	0.370
0.826	0.412
0.835	0.453
0.838	0.328
0.847	0.266
0.859	0.411
0.865	0.432
0.873	0.307
0.882	0.411
0.885	0.494
0.891	0.286
0.897	0.515
0.903	0.473
0.915	0.473
0.915	0.348
0.929	0.494
0.941	0.493
0.944	0.576
0.953	0.410
0.962	0.493
0.970	0.638
0.979	0.701
0.985	0.576
1.000	0.700
1.003	0.721
1.003	0.555
1.003	0.431
1.015	0.576
1.020	0.493
1.023	0.742
1.026	0.825
1.035	0.534
1.038	0.741
1.038	0.638
1.044	0.389
1.050	0.575
1.053	0.492
1.059	0.720
1.065	0.492
1.070	0.907
1.070	0.866
1.070	0.824
1.070	0.783
1.085	0.616
1.097	0.741
1.097	0.450
1.100	0.513
1.106	0.886
1.109	0.990
1.112	0.658
1.117	0.927
1.129	0.948
1.129	0.823
1.132	0.595
1.153	1.072
1.153	0.927
1.156	0.823
1.156	0.740
1.159	1.072
1.167	0.740
1.170	1.072
1.179	0.678
1.182	1.051
1.182	0.823
1.194	1.134
1.211	1.072
1.212	0.698
1.217	1.217
1.217	1.092
1.223	0.926
1.226	0.636
1.238	0.988
1.247	1.258
1.247	1.154
1.250	0.926
1.250	0.822
1.250	0.760
1.261	1.092
1.270	1.361
1.270	0.988
1.273	1.258
1.276	0.967
1.288	1.403
1.291	1.154
1.294	0.926
1.297	1.444
1.314	1.568
1.314	1.403
1.314	1.257
1.320	1.153
1.323	0.987
1.332	1.589
1.332	1.008
1.332	0.925
1.344	1.568
1.344	1.485
1.344	1.402
1.344	1.257
1.358	1.340
1.361	0.904
1.367	1.651
1.370	1.277
1.370	1.153
1.379	1.589
1.379	1.506
1.385	0.842
1.388	1.132
1.388	0.987
1.391	1.257
1.397	1.817
1.397	1.775
1.397	1.713
1.400	1.485
1.400	1.298
1.402	1.879
1.411	1.713
1.414	1.319
1.417	1.609
1.426	1.879
1.426	1.796
1.432	1.443
1.432	1.318
1.441	1.858
1.441	1.671
1.447	1.588
1.449	2.065
1.455	1.754
1.455	1.401
1.458	1.484
1.461	1.982
1.464	1.608
1.467	1.899
1.470	1.608
1.473	1.982
1.479	1.442
1.491	2.085
1.494	1.982
1.497	1.774
1.500	1.629
1.500	1.484
1.508	1.857
1.508	1.733
1.511	1.546
1.514	2.168
1.514	2.002
1.517	2.064
1.520	1.608
1.523	1.815
1.532	1.898
1.538	2.375
1.540	2.438
1.541	1.711
1.544	1.815
1.549	2.292
1.549	1.815
1.552	2.126
1.555	2.002
1.564	2.271
1.564	1.794
1.567	2.645
1.567	2.458
1.567	2.043
1.570	2.562
1.576	1.732
1.579	2.209
1.579	2.064
1.579	1.877
1.582	2.416
1.590	2.520
1.590	2.313
1.591	1.732
1.591	1.566
1.593	2.686
1.593	2.582
1.596	1.898
1.602	2.126
1.605	2.852
1.614	2.624
1.614	2.478
1.617	2.769
1.617	2.229
1.623	2.022
1.626	2.976
1.626	2.810
1.629	2.167
1.635	2.603
1.635	2.291
1.643	2.084
1.644	1.980
1.652	2.810
1.655	2.229
1.658	3.017
1.658	2.727
1.658	2.001
1.661	3.142
1.661	1.835
1.664	2.644
1.664	2.457
1.670	3.017
1.673	2.415
1.679	2.561
1.682	2.830
1.682	2.353
1.688	2.312
1.688	2.229
1.693	3.287
1.693	3.224
1.693	3.100
1.696	2.623
1.696	2.519
1.696	2.353
1.699	2.892
1.702	3.390
1.702	3.058
1.708	2.955
1.708	2.145
1.711	3.328
1.711	2.228
1.714	3.224
1.720	3.307
1.720	2.664
1.728	3.556
1.731	3.141
1.732	2.830
1.734	3.514
1.735	2.560
1.740	3.037
1.743	3.390
1.743	2.436
1.749	3.307
1.755	3.307
1.755	2.933
1.758	3.493
1.758	2.830
1.761	3.390
1.761	3.058
1.764	2.145
1.767	3.722
1.767	3.224
1.767	2.601
1.770	3.618
1.770	3.307
1.773	2.809
1.776	3.078
1.776	2.975
1.779	2.228
1.784	3.887
1.787	3.369
1.793	3.784
1.796	3.597
1.796	3.493
1.796	2.559
1.799	3.265
1.799	2.227
1.802	3.887
1.802	3.804
1.802	2.974
1.808	3.680
1.811	2.933
1.814	4.012
1.817	2.476
1.819	3.887
1.820	3.389
1.820	3.202
1.825	3.597
1.826	3.244
1.831	3.804
1.831	3.721
1.832	2.829
1.834	3.016
1.837	4.011
1.840	3.451
1.840	3.244
1.843	3.721
1.843	2.642
1.852	3.306
1.852	2.953
1.858	4.385
1.858	3.804
1.861	4.053
1.863	4.758
1.864	3.949
1.864	3.223
1.864	2.932
1.869	4.364
1.870	3.119
1.870	2.849
1.872	4.052
1.873	3.326
1.875	3.534
1.878	4.592
1.881	4.052
1.881	3.679
1.884	3.783
1.884	3.534
1.890	4.550
1.890	4.446
1.899	4.114
1.899	3.969
1.911	3.679
1.911	3.492
1.916	4.550
1.917	2.786
1.919	4.654
1.919	4.425
1.919	4.363
1.922	4.218
1.922	3.803
1.925	4.799
1.943	4.031
1.943	3.927
1.946	4.653
1.946	4.529
1.946	3.575
1.946	3.409
1.952	4.799
1.955	3.512
1.957	4.716
1.957	4.550
1.958	4.010
1.960	4.881
1.967	3.844
1.969	4.985
1.969	4.902
1.969	4.508
1.975	5.047
1.978	5.151
1.981	4.798
1.984	5.441
1.984	4.985
1.990	5.172
1.990	4.736
1.990	4.508
1.990	4.383
1.990	3.429
1.999	4.943
2.001	5.130
2.002	4.321
2.005	4.632
2.013	5.400
2.013	5.171
2.013	4.881
2.016	5.794
2.016	4.072
2.019	5.462
2.025	5.711
2.025	4.839
2.034	5.565
2.042	6.354
2.043	4.715
2.048	5.856
2.049	3.926
2.051	6.042
2.054	5.275
2.054	5.129
2.060	5.565
2.063	6.706
2.063	5.026
2.072	5.399
2.084	5.835
2.087	5.544
2.089	6.270
2.089	6.083
2.090	5.067
2.095	5.834
2.095	5.627
2.104	6.083
2.104	5.295
2.104	5.067
2.107	5.959
2.119	5.627
2.131	5.627
2.134	5.834
2.145	6.705
2.154	5.170
2.166	6.435
2.172	6.705
2.180	6.497
2.186	6.871
2.201	6.663
2.224	7.493
2.236	6.082
2.248	7.866
2.251	7.493
2.262	7.949
2.330	8.778
2.459	9.586];  
units.LW_m   = {'cm', 'g'};  label.LW_m = {'carapace length', 'wet weight', 'male'};  
bibkey.LW_m = 'DoiYoko2008';
comment.LW_m = 'Data for males';

% L-N data
data.LN = [ ... % carapace length (cm), 10^4 eggs (#)
1.268	0.885
1.331	0.612
1.373	0.887
1.385	0.879
1.421	0.888
1.478	1.267
1.520	1.019
1.521	1.594
1.528	1.860
1.552	1.827
1.568	1.509
1.583	2.025
1.605	1.201
1.608	1.544
1.623	1.794
1.627	1.493
1.643	1.940
1.687	2.147
1.713	1.847
1.751	2.714
1.763	1.805
1.772	2.947
1.773	2.354
1.854	3.403
1.855	2.699
1.855	2.287
1.889	3.850
1.896	3.473
1.915	2.014
1.984	2.719
2.012	3.269];  
units.LN   = {'cm', '10^4 eggs'};  label.LN = {'carapace length', 'fecundity'};  
temp.LN    = C2K(21);  units.temp.LN = 'K'; label.temp.LN = 'temperature';
bibkey.LN = 'DoiYoko2008';

%% set weights for all real data
weights = setweights(data, []);
weights.tL_f = 10 * weights.tL_f;
weights.tL_m = 10 * weights.tL_m;

%% 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 = 'Females with the fullest distended seminal receptacles peaked in June, which was used for time at birth';
D2 = 'Males are assumed to differ from females by {p_Am} and del_M only';
metaData.discussion = struct('D1', D1, 'D2', D2);

%% Facts
F1 = 'Swimming larval stages: zoea and megalopa';
metaData.bibkey.F1 = 'DoiYoko2008'; 
metaData.facts = struct('F1', F1);

%% Links
metaData.links.id_CoL = '5XPQV'; % Cat of Life
metaData.links.id_ITIS = '199968'; % ITIS
metaData.links.id_EoL = '4267035'; % Ency of Life
metaData.links.id_Wiki = 'Charybdis_(genus)'; % Wikipedia
metaData.links.id_Taxo = '3910334'; % Taxonomicon
metaData.links.id_WoRMS = '442874'; % WoRMS

%% References
bibkey = 'sealifebase'; type = 'Misc'; bib = ...
'howpublished = {\url{http://www.sealifebase.org/summary/Charybdis-bimaculata.html}}';
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 = 'DoiYoko2008'; type = 'Article'; bib = [ ... 
'author = {W. Doi and M. Yokota and C. A. Strussmann and S. Watanabe}, ' ... 
'year = {2008}, ' ...
'title = {Growth and reproduction of the portunid crab \emph{Charybdis bimaculata} ({D}ecapoda: {B}rachyura) in {T}okyo {B}ay}, ' ...
'journal = {Journal of Crustacean Biology}, ' ...
'volume = {28}, ' ...
'number = {4}, '...
'pages = {641-651}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];
%
bibkey = 'seatemp'; type = 'Misc'; bib = ...
'howpublished = {\url{http://seatemperature.info/tokyo-water-temperature.html}}';
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];


