1.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

2.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

3.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

4.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

5.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

6.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

7.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

8.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

9.

The posterior distribution per program type (general vs subject specific) of the 7 topics of the CTM model.

1.

Number of program and course descriptions per university included in the analysis.

2.

Number of program and course descriptions per university included in the analysis.

3.

Number of program and course descriptions per university included in the analysis.

4.

Number of program and course descriptions per university included in the analysis.

5.

Number of program and course descriptions per university included in the analysis.

6.

Number of program and course descriptions per university included in the analysis.

7.

Number of program and course descriptions per university included in the analysis.

8.

Number of program and course descriptions per university included in the analysis.

9.

Number of program and course descriptions per university included in the analysis.

1.

Manual changes made to the texts as part of data pre-processing.

2.

Manual changes made to the texts as part of data pre-processing.

3.

Manual changes made to the texts as part of data pre-processing.

4.

Manual changes made to the texts as part of data pre-processing.

5.

Manual changes made to the texts as part of data pre-processing.

6.

Manual changes made to the texts as part of data pre-processing.

7.

Manual changes made to the texts as part of data pre-processing.

8.

Manual changes made to the texts as part of data pre-processing.

9.

Manual changes made to the texts as part of data pre-processing.

1.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

2.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

3.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

4.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

5.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

6.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

7.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

8.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

9.

Top ten most common words per topic for the CTM where K = 7 with the topic label in last row.

1.

Sorted skills based on manually named topics according to CTM.

2.

Sorted skills based on manually named topics according to CTM.

3.

Sorted skills based on manually named topics according to CTM.

4.

Sorted skills based on manually named topics according to CTM.

5.

Sorted skills based on manually named topics according to CTM.

6.

Sorted skills based on manually named topics according to CTM.

7.

Sorted skills based on manually named topics according to CTM.

8.

Sorted skills based on manually named topics according to CTM.

9.

Sorted skills based on manually named topics according to CTM.

1.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

2.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

3.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

4.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

5.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

6.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

7.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

8.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

9.

Order of most important manually labelled topic terms per university based on posterior values for the CTM.

1.

La situation forestière de l'Afrique Occidentale Française

2.

La situation forestière de l'Afrique Occidentale Française

3.

La situation forestière de l'Afrique Occidentale Française

4.

La situation forestière de l'Afrique Occidentale Française

1.

Etude d'une parcelle de savane mise en défens (République Centrafricaine). Reboisement artificiel ou naturel

2.

Etude d'une parcelle de savane mise en défens (République Centrafricaine). Reboisement artificiel ou naturel

3.

Etude d'une parcelle de savane mise en défens (République Centrafricaine). Reboisement artificiel ou naturel

4.

Etude d'une parcelle de savane mise en défens (République Centrafricaine). Reboisement artificiel ou naturel

1.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

2.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

3.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

4.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

5.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

6.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

7.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

8.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

9.

Extension tools for forest or forest-related habitats ; Des outils de vulgarisation relatif aux habitats forestiers ou associés à la forêt.

1.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

2.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

3.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

4.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

5.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

6.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

7.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

8.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...

9.

Early detection of IAS in Flanders:From centralised reporting to effective early warning ; Vroegtijdige detectie van invasieve uitheemse soorten in Vlaanderen:Van gecentraliseerde...