Initial data release of regular blood drip stain created by varying fall height, angle of impact and source dimension

The dataset developed consists of 108 blood drip stains developed with fresh porcine blood, blood admixed with different dosage of Warfarin and Heparin, respectively. For each particular blood type (i.e. fresh blood, blood admixed with Warfarin at different dosage and blood admixed with Heparin at v...

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Bibliographic Details
Main Authors: Nabanita Basu, Samir Kumar Bandyopadhyay
Format: Article
Language:English
Published: Elsevier 2016-09-01
Series:Data in Brief
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340916304383
Description
Summary:The dataset developed consists of 108 blood drip stains developed with fresh porcine blood, blood admixed with different dosage of Warfarin and Heparin, respectively. For each particular blood type (i.e. fresh blood, blood admixed with Warfarin at different dosage and blood admixed with Heparin at varied dosage) stain patterns were created by passive dripping of blood from a 2.5 cm3 subcutaneous syringe with needle filled to capacity, at 30°, 60° and 90° angle of impact with corresponding fall height of 20, 40 and 60 cm respectively. In the other dataset of 162 datapoints, 81 regular drip stains were formed from blood that had dripped passively from a subcutaneous syringe without needle at the aforementioned angle of impact and fall height, while the other stains were formed as a result of dripping of blood from a subcutaneous syringe with needle. In order to compare stains formed, all stains were recorded on the same representative, non-porous, smooth target surface under similar physical conditions. The interpretations relevant to the dataset are available in the article titled ‘2D Source Area prediction based on physical characteristics of a regular, passive blood drip stain’ (Basu and Bandyopadhyay, 2016) [7]. An image pre-processing algorithm for extracting ROI has also been incorporated in this article. Keywords: Drip stain, Bloodstain Pattern Analysis, Source Dimension prediction
ISSN:2352-3409