Ishtiak Al Mamoon , Zakaria Shams Siam , Abdul Akhir Al Galib , Theophil Dango , Kalin Chakma , Pranto Dev , Rubyat Tasnuva Hasan , Muhammad E.H. Chowdhury
{"title":"基于机器学习分类的古普塔弓箭手型钱币新数据集","authors":"Ishtiak Al Mamoon , Zakaria Shams Siam , Abdul Akhir Al Galib , Theophil Dango , Kalin Chakma , Pranto Dev , Rubyat Tasnuva Hasan , Muhammad E.H. Chowdhury","doi":"10.1016/j.dib.2024.110934","DOIUrl":null,"url":null,"abstract":"<div><div>In the field of numismatics, classifying ancient coins, especially those that have diverse information and cultural heritage is a difficult task. Machine learning algorithms have recently made remarkable advancements in these types of tasks. However, these algorithms largely rely on relevant datasets. This article presents a novel dataset of ancient Gupta archer-type coin images, collected from verified private collections and three popular auction houses with their permission. The images exclusively comprise authentic specimens of ancient Gupta archer-type coins. We aim to establish a reliable resource that adheres to the highest standards of numismatic research. These coins, characterized by their distinctive archer motifs, present a significant challenge in terms of identification due to their scarcity and the intricate nature of their design. To address this, we meticulously curated a dataset by annotating each coin through a combination of visual examination and leveraging insights from numismatic literatures. These coins inherit ancient Indian archaeological insights, and studying these coins could provide insights into ancient Indian archaeology.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel dataset of Gupta archer type coins for machine learning-based classification\",\"authors\":\"Ishtiak Al Mamoon , Zakaria Shams Siam , Abdul Akhir Al Galib , Theophil Dango , Kalin Chakma , Pranto Dev , Rubyat Tasnuva Hasan , Muhammad E.H. Chowdhury\",\"doi\":\"10.1016/j.dib.2024.110934\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In the field of numismatics, classifying ancient coins, especially those that have diverse information and cultural heritage is a difficult task. Machine learning algorithms have recently made remarkable advancements in these types of tasks. However, these algorithms largely rely on relevant datasets. This article presents a novel dataset of ancient Gupta archer-type coin images, collected from verified private collections and three popular auction houses with their permission. The images exclusively comprise authentic specimens of ancient Gupta archer-type coins. We aim to establish a reliable resource that adheres to the highest standards of numismatic research. These coins, characterized by their distinctive archer motifs, present a significant challenge in terms of identification due to their scarcity and the intricate nature of their design. To address this, we meticulously curated a dataset by annotating each coin through a combination of visual examination and leveraging insights from numismatic literatures. These coins inherit ancient Indian archaeological insights, and studying these coins could provide insights into ancient Indian archaeology.</div></div>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352340924008977\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340924008977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
A novel dataset of Gupta archer type coins for machine learning-based classification
In the field of numismatics, classifying ancient coins, especially those that have diverse information and cultural heritage is a difficult task. Machine learning algorithms have recently made remarkable advancements in these types of tasks. However, these algorithms largely rely on relevant datasets. This article presents a novel dataset of ancient Gupta archer-type coin images, collected from verified private collections and three popular auction houses with their permission. The images exclusively comprise authentic specimens of ancient Gupta archer-type coins. We aim to establish a reliable resource that adheres to the highest standards of numismatic research. These coins, characterized by their distinctive archer motifs, present a significant challenge in terms of identification due to their scarcity and the intricate nature of their design. To address this, we meticulously curated a dataset by annotating each coin through a combination of visual examination and leveraging insights from numismatic literatures. These coins inherit ancient Indian archaeological insights, and studying these coins could provide insights into ancient Indian archaeology.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.