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IEEE Transactions on Big Data (TBD): http://www.computer.org/web/tbd
Special Issue on Big Scholar Data Discovery and Collaboration
Important Dates:
** Mar. 31, 2015:  Paper Submission Due
** Jul. 1, 2015:  Author Notification 
** Aug. 15, 2015: Revision Submission Due
** Oct. 1, 2015:  Camera-Ready Due

Qiang Yang, Hong Kong University of Science and Technology

Guest Editors 
Yu-Ru Lin, University of Pittsburgh ([log in to unmask])
Hanghang Tong, Arizona State University ([log in to unmask])
Jie Tang, Tsinghua University ([log in to unmask])
K. Selçuk Candan, Arizona State University ([log in to unmask])

## Overview ##
Academics and researchers worldwide continue to produce large numbers of scholarly documents including papers, books, technical reports, etc. and associated data such as tutorials, proposals, and course materials. The abundance of data sources enables researchers to study scholarly collaboration at a very large scale. The ever increasing diversity of disciplines and complexity of research problems, particularly multi-disciplinary research, requires collaboration. Besides the traditional venues of collaboration where scholars typically meet annually at conferences or meetings, the Internet provides a wide range of platforms for scholars to engage with other scholars. These new platforms include academic search-oriented Web engines such as Google Scholar, social media sites such as Academia.edu, ResearchGate and Mendeley, more interactive social sites such as Twitter and Facebook, and Wiki-style virtual collaboration sites. These services allow scholars to share academic resources, exchange opinions, follow each other’s research, keep up with current research trends, and build their professional networks. Researchers increasingly realize that scholarly achievements should not merely be the final published articles. The datasets used in study and many other intermediary results are equally important for supporting research. Therefore, a set of rapidly developing research topics, research data management, data curation/stewardship, data sharing policy, etc. are becoming important issues for research communities. This special issue aims at bringing together researchers with diverse interdisciplinary backgrounds interested in scholarly big data. In addition, this special issue will feature a “Scholar Data Challenge” associated with a dataset consisting over 2 million papers and more than 8 million citation relationships. Submissions with a use of this dataset are optional. More details can be found at: http://aminer.org/big-scholar-challenge/

## Topics ##
The topics of interest include, but are not limited to:
* Data analytical tools for studying scholarly discovery and collaboration, including:
  - New approaches to measure and predict the impact of research and researchers in a particular fields of study;
  - Searching and mining large digital libraries, repositories for scholarly publications and patents and linking to other data sources such as funded proposals and patents;
  - Novel data search and mining tools for studying scholarly collaboration structure using big data, including scalable graph mining, etc.;
  - Data infrastructure that supports scalable computation, e.g., document indexing with cloud computing services;
  - Algorithms for accessing, extracting and recommending scholarly articles, experts and findings.
* Online scholar data platforms and systems consideration for scholarly discovery and collaboration, including:
  - Heterogenous data source integration, especially with open-access, novel datasets (e.g., Wikipedia, government census data, patent data, etc.);
  - Storage, indexing and query processing for research data;
  - Design considerations for effectively support scholars’ engagement in using online and social platforms;
  - Social and collaborative support for scholarly discovery and collaboration;
  - Privacy and security issues and management in online scholarly collaboration.
* Digital data curation and management for scholarly discovery and collaboration, including:
  - Issues and solutions to data curation, management, and archival;
  - Existing practices for managing research data;
  - Scalability and usability of managing research data
  - Research reproducibility and data sharing policy.
* Other aspects of scholarly discovery and collaboration, including:
  - Design of next generation collaboration platforms;
  - Information professionals’ role in engaging in online scholarly collaboration;
  - Cultural and community acceptance and evaluation of activities in online scholarly collaboration.

## Scholar Data Challenge ##
One of the greatest challenges is the difficulty of collecting massive research dataset in the public domain. Hence, in addition to the above topics, this special issue will feature a “Scholar Data Challenge” associated with a dataset consisting over 2 million papers and more than 8 million citation relationships. The goal is to encourage approaches from different fields to explore the disparate facets of the same datasets in order to stimulate an interdisciplinary yet more focused research discussion. Submissions with a use of this dataset are optional. Information about the dataset can be found at: http://aminer.org/big-scholar-challenge/

## Submission Information ##
All manuscripts must be directly submitted via the IEEE TBD submission web site: https://mc.manuscriptcentral.com/tbd-cs 
Submissions must follow instructions for formatting and length as regular paper described at: http://www.computer.org/web/tbd/author 

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