## 1 Introduction
### Multi-Source Data Sharing, Management and Analysis Techniques  
Compared to single-source data analysis, multi-source can not only validate interdependence experimental results to obtain more reliable and accurate results, but it also eliminate contradictions and complement each other [26; 27]. Data sharing improves the research exchange with other organizations and enable more researchers to use and utilize existing data resources. In addition, it can reduce the cost of data management and the work of data collection [28]. Although desktop-based offline analysis techniques for plant electrical signals have been improved, there are still challenges in analysis of plant electrophysiological data, e.g., overlapped noise in the raw recordings, variation of response waveform owing to different stimulations, varieties, and ongoing plant electrophysiological data collection. Therefore, online computing and analysis can promote in-depth research on plant electrical signals interpretation and data mining for finding useful information of crop stress tolerance. For these reasons, we retrieved the electronic resources, i.e., Ei Compendex, Web of Science, PubMed, Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, Springer Link, Google etc. Through reviewing literatures and the retrieving key words are plant electrical signal, membrane potential, action potential (AP), variation potential (VP), sharing, data storage, large scale data, and online analysis. In 2014, Spain, Britain and the PLEASED team pioneered the web-based data storage and popular science for plant electrical signals aided by EU's Seventh Framework Program, in which they opened several electrophysiological data set stored in business Mega cloud storage. It inspires us to do the in-depth research for data sharing, especially for online analysis. Currently, there is no public web-based plant electrophysiological platform for data sharing, online computing and analysis. Luckily, the abundant data analysis and sharing technologies for animal and human body electrophysiological data can enlighten us. With respect to current important electrophysiological data sharing platforms for animal and human body, we have summarized in a review paper [29]. Many of them are active and allow users to create datasets on web page and access datasets online through a web browser or custom desktop software. The electrophysiological data include electrical signals, experimental metadata, annotation data and image data. Hence, efficient semi-structure and unstructured data management is a key challenge. To realize electrophysiological data sharing and analysis for experimental and clinical research for humans, many web platforms have been developed for human electrophysiological data management in the past years. Among these systems, PhysioNet has the powerful functions like rich data types [30] (PhysioBank includes EEG, ECG, EMG etc. ), a large number of offline analysis tools (PhysioTookit), basic online data browsing function (Lightwave [31], PhysioBank ATM) and scientific research cooperation among different teams (PhysioNetWorks). The similar platforms also contain INCF (International Neuroinformatics Coordinating Facility), NEO [32], G-Node (German Neuroinformatics Node) [33], IEEG-Portal (The International Epilepsy Electrophysiology Portal) [34] and CARMEN (The Code Analysis Repository & Modelling for E-Neuroscience) [35; 36]. Cloudwave is a visualization and analysis platform of electrophysiology (EEG, ECG) [37]. It is a part of the Prevention and Risk Identification of SUDEP Mortality (PRISM) project, using highly scalable open source cloud computing infrastructure European Data Format (EDF) and relational database for data storage [38]. However, Cloudwave focuses on the EEG/ECG visualization and data storage model, it does not involve the algorithms in data analysis. For data management and storage, Hadoop [39], typically as a distributed storage and parallel instance, is an open source, scalable distributed computing, and storage platform.