Dataset Information

DOES - A multimodal dataset for supervised and unsupervised analysis of steel scrap.

ABSTRACT: DOES - Dataset of European scrap classes. Today, scrap is already an important raw material for industry. Due to the transformation to green steel, the secondary raw material scrap will become increasingly important in the coming years. With DOES a free dataset is presented, which represents common non-alloyed European scrap classes. Two important points were considered in this dataset. First, scrap oxidizes under normal external conditions and the visual appearance changes, which plays an important role in visual inspections. Therefore, DOES includes scrap images of different degrees of corrosion attack. Second, images of scrap metal (mostly scrap piles) usually have no intrinsic order. For this reason, a technique to extract many overlapping rectangles from raw images was used, which can be used to train deep learning algorithms without any disadvantage. This dataset is very suitable to develop industrial applications or to research classification algorithms. The dataset was validated by experts and through machine learning models.

SUBMITTER: Schafer M

PROVIDER: S-EPMC10632433 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DOES - A multimodal dataset for supervised and unsupervised analysis of steel scrap.

Schäfer Michael M Faltings Ulrike U Glaser Björn B

Scientific data 20231108 1

DOES - Dataset of European scrap classes. Today, scrap is already an important raw material for industry. Due to the transformation to green steel, the secondary raw material scrap will become increasingly important in the coming years. With DOES a free dataset is presented, which represents common non-alloyed European scrap classes. Two important points were considered in this dataset. First, scrap oxidizes under normal external conditions and the visual appearance changes, which plays an impor ...[more]

PMID: 37938587

Similar Datasets

Project description:Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised (n = 6), semi-supervised (n = 1), and unsupervised (n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors (Gymnogyps californianus). Unsupervised K-means and EM (expectation-maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: -0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

Dataset Information

DOES - A multimodal dataset for supervised and unsupervised analysis of steel scrap.

Publications

DOES - A multimodal dataset for supervised and unsupervised analysis of steel scrap.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets