Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts

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Martinsen, Iver; Sørensen, Steffen Aagaard; Ortega, Samuel; Godtliebsen, Fred; Tejedor, Miguel; Myrvoll-Nilsen, Eirik, 2024, "Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts", https://doi.org/10.18710/QGBZZK, DataverseNO, V1

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Description	This dataset contains PNG images of individual foraminifera and sediment grains. The dataset contains a set of training images, and a set of test images. Each of the sets contains four subfolders for each of the four classes in the dataset. The four classes are: A (Benthic Agglutinated foraminifera), B (Benthic Calcareous foraminifera), P (Planktic foraminifera), and S (Sediment grains). The training dataset consists of 258 from class A, 380 from class B, 332 from class P, and 341 from class S (1311 in total). The test dataset consists of 65 images from each class (260 in total). The resolution varies from image to image. The photos were produced by capturing images using a 51 megapixel Canon EOS 5DS R mounted on a Leica M420 stereo microscope. A Leica cls 150x twin gooseneck combination light was used for lighting at fixed lumen. The photographed specimens were handpicked, allowing a very high degree of certainty for the labeling. All photographed materials (foraminifera and sediment) were extracted from sediment cores retrieved at water depth ranging from about 100–700m from both fjord and shelf settings in the Arctic Barents Sea region. (2024-11-14)
Subject	Earth and Environmental Sciences
Keyword	microfossils, foraminifera, benthic, planktic, sediment, classification, deep learning, machine learning, computer vision, image classificaton
Related Publication	Currently under review
License/Data Use Agreement	CC0 1.0

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Citation Metadata

Persistent Identifier	doi:10.18710/QGBZZK
Publication Date	2024-12-02
Title	Replication Data for: Quantifying Uncertainty in Foraminifera Classification: How Deep Learning Methods Compare to Human Experts
Author	Martinsen, Iver (UiT The Arctic University of Norway) - ORCID: 0000-0002-7003-9213 Sørensen, Steffen Aagaard (UiT The Arctic University of Norway) - ORCID: 0000-0002-0698-6478 Ortega, Samuel (UiT The Arctic University of Norway/NOFIMA) - ORCID: 0000-0002-7519-954X Godtliebsen, Fred (UiT The Arctic University of Norway) - ORCID: 0000-0001-7896-8634 Tejedor, Miguel (Norwegian Centre for e-Health Research) - ORCID: 0009-0006-7696-1272 Myrvoll-Nilsen, Eirik (UiT The Arctic University of Norway) - ORCID: 0000-0002-1643-5661
Point of Contact	Use email button above to contact. Martinsen, Iver (UiT The Arctic University of Norway)
Description	This dataset contains PNG images of individual foraminifera and sediment grains. The dataset contains a set of training images, and a set of test images. Each of the sets contains four subfolders for each of the four classes in the dataset. The four classes are: A (Benthic Agglutinated foraminifera), B (Benthic Calcareous foraminifera), P (Planktic foraminifera), and S (Sediment grains). The training dataset consists of 258 from class A, 380 from class B, 332 from class P, and 341 from class S (1311 in total). The test dataset consists of 65 images from each class (260 in total). The resolution varies from image to image. The photos were produced by capturing images using a 51 megapixel Canon EOS 5DS R mounted on a Leica M420 stereo microscope. A Leica cls 150x twin gooseneck combination light was used for lighting at fixed lumen. The photographed specimens were handpicked, allowing a very high degree of certainty for the labeling. All photographed materials (foraminifera and sediment) were extracted from sediment cores retrieved at water depth ranging from about 100–700m from both fjord and shelf settings in the Arctic Barents Sea region. (2024-11-14)
Subject	Earth and Environmental Sciences
Keyword	microfossils foraminifera benthic planktic sediment classification deep learning machine learning computer vision image classificaton
Related Publication	Currently under review
Language	English
Producer	UiT The Arctic University of Norway (UiT) https://en.uit.no/
Production Date	2024-11-14
Production Location	UiT The Arctic University of Norway
Contributor	Data Curator : Sørensen, Steffen Aagaard Data Curator : Ortega, Samuel Data Curator : Martinsen, Iver Project Member : Godtliebsen, Fred Project Member : Myrvoll-Nilsen, Eirik Project Member : Tejedor, Miguel
Funding Information	The Research Council of Norway: 332901
Distributor	UiT The Arctic University of Norway (UiT) https://dataverse.no/dataverse/uit
Depositor	Martinsen, Iver
Deposit Date	2024-11-14
Date of Collection	Start Date: 2006-01-01 ; End Date: 2016-12-31
Data Type	Curated survey data; images
Data Source	The source of this data are sediment cores retrieved and processed from 2006-2016 all under the UiT umbrella from various research cruises. This includes Cage cruises (https://septentrio.uit.no/index.php/cage), BARCUT/EWMA cruises (https://nofima.brage.unit.no/nofima-xmlui/handle/11250/2837979), GlaciBar cruises, and others. The cores were collected in the Eastern Fram Strait, the Bear Island Trough, Ingøydjupet, the central Barents Sea and in the fjords Repparfjorden and Andfjorden in northern Norway. The sediments were achieved via Multi corer, Box corer, Gravity corer, Piston corer and Kastenlot corer. The sediments are all of late glacial to modern age. The forams were extracted from the sediment following the methods outlined in Knudsen (1998). The cores were photographed and processed between 2020 and 2024. Knudsen, K. L. (1998): Foraminiferer i Kvartær stratigrafi: Laboratorie- og fremstillingsteknik samt udvalgte eksempler, Geol. Tidsskr., 3, pp. 1-25.

Geospatial Metadata

Geographic Coverage	Svalbard and Jan Mayen, Eastern Fram Strait, Svalbard Svalbard and Jan Mayen, Bear Island Trough Norway, Finnmark, Ingøydjupet Norway, Central Barents Sea Norway, Finnmark, Repparfjorden Norway, Troms, Andfjorden

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