Scientific queries are usually domain-specific and resource needs usually come with multiple constraints.<\/h3>\n<\/blockquote>\n<\/h2>\n\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t
\n\t\t\t<\/div>\n\t\t<\/div> \n\t\t\n\t\t\tI am Na Li, a PhD student in computer science at University of Amsterdam, currently exploring notebook search for scientific users. In this post I will introduce my observations for researchers\u2019 intents when they search for research resources, which mainly derive from communication with members at the CVBLab in UPV.\u00a0\n\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n<\/span><\/p>\nI finished my secondment in the Universitat Polit\u00e8cnica de Val\u00e8ncia (UPV) on 31st December. Being able to do the secondment on-site is very important since seeing people in person and working closely everyday make the connection real. Discussions are much easier and efficient. And many valuable information and findings came from informal communication. <\/span><\/p>\nDuring the secondment, I mainly focused on notebook search (Notebook search refers to retrieving notebooks from a database given natural language queries). The members in CVBLab are actually the end-users of a notebook search system. To understand the intentions of researchers when searching for research resources, we collected 37 natural language queries that express their resource needs within CVBLab. Examples are \u201csegmentation of epidermis in histopathological images\u201d, \u201cwhole-slide images classification tensorflow code\u201d,<\/span>\u201cDeep Kernel Learning\u201d and \u201cmitosis detection in histological images\u201d.<\/p>\nBy analyzing these queries (extracting the information of task, method, model, data type involved in queries; talking to experts who provide the queries), we have the following observations: <\/span><\/p>\n\n- <\/span>Scientific queries are usually domain-specific<\/span><\/strong>, which means the used terms are professional and thus not covered by common knowledge. <\/span><\/li>\n
- <\/span>Resource needs usually come with multiple constraints<\/span><\/strong>. Taking \u201csegmentation of epidermis in histopathological images\u201d as an example, the desired notebooks must target the epidermis segmentation task and the data should be histopathological images. <\/span><\/li>\n<\/ul>\n
\n\t\t\t<\/span><\/p>\n I finished my secondment in the Universitat Polit\u00e8cnica de Val\u00e8ncia (UPV) on 31st December. Being able to do the secondment on-site is very important since seeing people in person and working closely everyday make the connection real. Discussions are much easier and efficient. And many valuable information and findings came from informal communication. <\/span><\/p>\n During the secondment, I mainly focused on notebook search (Notebook search refers to retrieving notebooks from a database given natural language queries). The members in CVBLab are actually the end-users of a notebook search system. To understand the intentions of researchers when searching for research resources, we collected 37 natural language queries that express their resource needs within CVBLab. Examples are \u201csegmentation of epidermis in histopathological images\u201d, \u201cwhole-slide images classification tensorflow code\u201d,<\/span>\u201cDeep Kernel Learning\u201d and \u201cmitosis detection in histological images\u201d.<\/p>\n By analyzing these queries (extracting the information of task, method, model, data type involved in queries; talking to experts who provide the queries), we have the following observations: <\/span><\/p>\n\n
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