{"title":"How to make your results reproducible with UCR-star and spider","authors":"Tomal Majumder, A. Eldawy","doi":"10.1145/3557994.3565975","DOIUrl":"https://doi.org/10.1145/3557994.3565975","url":null,"abstract":"With the rise of data science, there has been a sharp increase in data-driven techniques that rely on both real and synthetic data. At the same time, there is a growing interest from the scientific community in the reproducibility of results. Some conferences include this explicitly in their review forms or give special badges to reproducible papers. This tutorial describes two systems that facilitate the design of reproducible experiments on both real and synthetic data. UCR-Star is an interactive repository that hosts terabytes of open geospatial data. In addition to the ability to explore and visualize this data, UCR-Star makes it easy to share all or parts of these datasets in many standard formats ensuring that other researchers can get the same exact data mentioned in the paper. Spider is a spatial data generator that generates standardized spatial datasets with full control over the data characteristics which further promotes the reproducibility of results. This tutorial will be organized into two parts. The first part will exhibit the key features of UCR-star and Spider where participants can get hands-on experience in interacting with real spatial datasets, generating synthetic data with varying distributions, and downloading them to a local machine or a remote server. The second part will explore the integration of both UCR-Star and Spider into existing systems such as QGIS and Apache AsterixDB.","PeriodicalId":299822,"journal":{"name":"Proceedings of the 4th ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122791726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 4th ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","authors":"","doi":"10.1145/3557994","DOIUrl":"https://doi.org/10.1145/3557994","url":null,"abstract":"","PeriodicalId":299822,"journal":{"name":"Proceedings of the 4th ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114562426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}