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  "author": null,
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  "author": null,
            
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  "code": "7b.b.0c",
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  "code": "7b.b.0c",
            
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  "contributor": "NIOZ Royal Netherlands Institute for Sea Research",
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  "contributor": "NIOZ Royal Netherlands Institute for Sea Research",
            
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  "creators": 
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  "creators": 
            
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tname\":\"Jeroen\",\"lastname\":\"Hoekendijk\",\"affiliation\":\"Royal 
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tname\":\"Jeroen\",\"lastname\":\"Hoekendijk\",\"affiliation\":\"Royal 
            
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Netherlands Institute for Sea 
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Netherlands Institute for Sea 
            
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ess\":\"Jeroen.hoekendijk@nioz.nl\",\"iscorrespondingauthor\":true}]",
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ess\":\"Jeroen.hoekendijk@nioz.nl\",\"iscorrespondingauthor\":true}]",
            
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  "dates": "",
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  "dates": "",
            
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  "deposit_date": "2021-11-01",
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  "deposit_date": "2021-11-01",
            
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  "depositor": "Jeroen Hoekendijk",
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  "depositor": "Jeroen Hoekendijk",
            
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  "distribution_date": "2021-11-01",
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  "distribution_date": "2021-11-01",
            
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  "distributor": "Research Data Management(NIOZ Royal Netherlands 
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  "distributor": "Research Data Management(NIOZ Royal Netherlands 
            
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Institute for Sea Research)",
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Institute for Sea Research)",
            
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  "doi_date_published": "2021-12-01",
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  "doi_date_published": "2021-12-01",
            
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  "funding_references": "[{\"name\":\"NWO \",\"awardnumber\":\"project 
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  "funding_references": "[{\"name\":\"NWO \",\"awardnumber\":\"project 
            
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ALWPP.2017.003\",\"awardtitle\":\"\"}]",
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  "name": "7bb0c",
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  "name": "7bb0c",
            
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  "notes": "Many ecological studies rely on count data and involve 
30
  "notes": "Many ecological studies rely on count data and involve 
            
31
manual counting of objects of interest, which is time-consuming and 
31
manual counting of objects of interest, which is time-consuming and 
            
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especially disadvantageous when time in the field or lab is limited. 
32
especially disadvantageous when time in the field or lab is limited. 
            
33
However, an increasing number of works uses digital imagery, which 
33
However, an increasing number of works uses digital imagery, which 
            
34
opens opportunities to automatise counting tasks. In this study, we 
34
opens opportunities to automatise counting tasks. In this study, we 
            
35
use machine learning to automate counting objects of interest without 
35
use machine learning to automate counting objects of interest without 
            
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the need to label individual objects. By leveraging already existing 
36
the need to label individual objects. By leveraging already existing 
            
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image-level annotations, this approach can also give value to 
37
image-level annotations, this approach can also give value to 
            
38
historical data that were collected and annotated over longer time 
38
historical data that were collected and annotated over longer time 
            
39
series (typical for many ecological studies), without the aim of deep 
39
series (typical for many ecological studies), without the aim of deep 
            
40
learning applications. We demonstrate deep learning regression on two 
40
learning applications. We demonstrate deep learning regression on two 
            
41
fundamentally different counting tasks: (i) daily growth rings from 
41
fundamentally different counting tasks: (i) daily growth rings from 
            
42
microscopic images of fish otolith (i.e., hearing stone) and (ii) 
42
microscopic images of fish otolith (i.e., hearing stone) and (ii) 
            
43
hauled out seals from highly variable aerial imagery. In the otolith 
43
hauled out seals from highly variable aerial imagery. In the otolith 
            
44
images, our deep learning-based regressor yields an RMSE of 3.40 
44
images, our deep learning-based regressor yields an RMSE of 3.40 
            
45
day-rings and an R^2 of 0.92. Initial performance in the seal images 
45
day-rings and an R^2 of 0.92. Initial performance in the seal images 
            
46
is lower (RMSE of 23.46 seals and R^2 of 0.72), which can be 
46
is lower (RMSE of 23.46 seals and R^2 of 0.72), which can be 
            
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attributed to a lack of images with a high number of seals in the 
47
attributed to a lack of images with a high number of seals in the 
            
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initial training set, compared to the test set. We then show how to 
48
initial training set, compared to the test set. We then show how to 
            
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improve performance substantially (RMSE of 19.03 seals and R^2 of 
49
improve performance substantially (RMSE of 19.03 seals and R^2 of 
            
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0.77) by carefully selecting and relabelling just 100 additional 
50
0.77) by carefully selecting and relabelling just 100 additional 
            
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training images based on initial model prediction discrepancy. The 
51
training images based on initial model prediction discrepancy. The 
            
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regression-based approach used here returns accurate counts (R^2 of 
52
regression-based approach used here returns accurate counts (R^2 of 
            
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0.92 and 0.77 for the rings and seals, respectively), directly usable 
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0.92 and 0.77 for the rings and seals, respectively), directly usable 
            
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in ecological research.",
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in ecological research.",
            
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