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These are the daily closing prices of three stock indices including Shanghai Securities Composite Index (SSEC), the Shenzhen Securities Component Index (SZI) and the Standard & Poor 500 Index (SPX). The data is obtained from Yahoo Finance (https://finance.yahoo.com) and collated. The SSEC data is from December 19,1990 to May 25, 2023. The data of SZI is from April 3, 1991 to May 25, 2023, and the data of SPX is from December 3, 1990 to May 25, 2023.
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During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.
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<p>This dataset consists of 200 occurrences extracted from three fields of a web analytics tool over time, along with labels indicating the service availability status at that moment. The data pertains to customer accesses of a real financial institution. The columns are named with a type and a unique identifier number. The column TX_ACAO_EVT represents an action performed by the customer, such as a click, system message, or background application action. The column TX_CTGR_EVT represents the category of the action, such as an error message or a specific type of action.
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The datasets in discussion present detailed records for two of the world's most cultivated crops: wheat and rice. These datasets aim to provide comprehensive insights into various environmental and soil-related factors that are traditionally considered influential in determining the yield of these crops. By analyzing these datasets, researchers, agronomists, and farmers can gain a better understanding of the interplay between different attributes and how they might impact the overall crop yield.
Wheat Dataset:
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This dataset was derived from a questionnaire survey, including online surveys and field surveys, to test the reliability and validity of the media convergence perception scale. The questionnaire consisted of two parts. The first part was related to the 38 items generated in the prior step. The socio-demographic characteristic information of interviewees was captured in the second part. In addition, there was an additional question about whether respondents have used CLCM before, which is essential for the following data analysis.
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A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient.
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Abstract- Cardiovascular diseases (CVDs) remain a sig- nificant global health challenge, emphasizing the critical need for accurate predictive models to address early detec- tion and intervention. This study presents a comprehensive framework for heart disease prediction using advanced ma- chine learning techniques.
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Drug development is a process that is incredibly expensive and time-consuming. Computational drug repurposing can help to assign new indications for approved drugs. It is capable to reduce the cost of drug developments. Machine learning models have been introduced to repurpose drugs long before. Recent studies formulate computational drug repurposing problem as a latent link prediction task on a heterogenous network. A number of computational methods have been developed based on graph neural networks.
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The requirements, their types and priorities are gathered from 43 project teams which will be uselful to automate the phases of requirement engineering i.e. requirements classification and prioritisation. As the publicly available datasets do not contain the complete information (type and priority) about requirements, the dataset is created by collecting the data from 43 BTech project groups. This dataset includes 11 different types of software requirements. The dependency of requirements is also considered while gathering requirements from the project teams.
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