Data-Intensive Research

2013 ◽  
pp. 545-548
Author(s):  
Sabina Leonelli
Author(s):  
Shawn T. Brown ◽  
Paola Buitrago ◽  
Edward Hanna ◽  
Sergiu Sanielevici ◽  
Robin Scibek ◽  
...  

2018 ◽  
pp. 16-37
Author(s):  
Andrew Krumm ◽  
Barbara Means ◽  
Marie Bienkowski

Author(s):  
K. Hiraki ◽  
M. Inaba ◽  
J. Tamatsukuri ◽  
R. Kurusu ◽  
Y. Ikuta ◽  
...  

Bibliosphere ◽  
2020 ◽  
pp. 97-102 ◽  
Author(s):  
Tibor Koltay

Reacting to the appearance of data-intensive research prompts academic libraries to become service providers for scholars, who work with research data. Although this is an imperative for libraries worldwide, due to the differences between countries and institutions, the level of readiness to engage in related activities differs from country to country. While some of the related tasks are fairly novel, others heavily build on librarians’ traditional, well-known skills. To identify these tasks, as well as making an inventory of the required skills and abilities, this paper, based on a non-exhaustive review of the recent literature, presents both theoretical and practical issues. It is demonstrated that the most obvious directions of the service development in academic libraries to support data-intensive science are research data management, data curation, data literacy education for users, and data literacy education for librarians.


2018 ◽  
Author(s):  
R. Stuart Geiger ◽  
Dan Sholler ◽  
Aaron Culich ◽  
Ciera Martinez ◽  
Fernando Hoces de la Guardia ◽  
...  

What are the challenges and best practices for doing data-intensive research in teams, labs, and other groups? This paper reports from a discussion in which researchers from many different disciplines and departments shared their experiences on doing data science in their domains. The issues we discuss range from the technical to the social, including issues with getting on the same computational stack, workflow and pipeline management, handoffs, composing a well-balanced team, dealing with fluid membership, fostering coordination and communication, and not abandoning best practices when deadlines loom. We conclude by reflecting about the extent to which there are universal best practices for all teams, as well as how these kinds of informal discussions around the challenges of doing research can help combat impostor syndrome.


2017 ◽  
Vol 14 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Edward S Dove ◽  
Chiara Garattini

Life sciences research is increasingly international and data-intensive. Researchers work in multi-jurisdictional teams or formally established research consortia to exchange data and conduct research using computation of multiple sources and volumes of data at multiple sites and through multiple pathways. Despite the internationalization and data intensification of research, the same ethics review process as applies to single-site studies in one country tends to apply to multi-site studies in multiple countries. Because of the standard requirement for multi-jurisdictional or multi-site ethics review, international research projects are subjected to multiple ethics reviews of the same research protocol. Consequently, the reviews may be redundant and resource-consuming, whilst the opinions delivered by ethics committees may be inconsistent both within and across jurisdictions. In this article, we present findings based on interviews conducted with international experts in research ethics on the topic of ethics review mutual recognition. We explore the issues associated with ethics committee review of multi-jurisdictional data-intensive research projects, identifying current problems, real-life experiences, and potential solutions that are both bottom-up (via researchers, participants and publics) and top-down (via statutory regulation), as well as challenges in achieving both. On the whole, participants recommended multiple changes to the current ethics review regime for data-intensive international research with the aim of reducing inefficiency and inconsistency. But, the changes recommended differ in terms of degree and scope. In general, participants stressed that key drivers of success in a reformed system should be strong leadership (on the ground and in government) and demonstration of value.


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