scholarly journals The Reuse of Public Datasets in the Life Sciences: Potential Risks and Rewards

2020 ◽  
Author(s):  
Katharina Frey ◽  
Alenka Hafner ◽  
Boas Pucker
Keyword(s):  
Big Data ◽  
Life Sciences ◽  
Wide Distribution ◽  
Public Sharing ◽  
Potential Risks ◽  
Public Datasets

The 'big data revolution' has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the challenges, limitations and risks associated with it. Due to the prominence, abundance and wide distribution of sequencing results, we focus on the reuse of publicly available sequence datasets. Through selected examples of successful reuse of different data (genome, transcriptome, proteome, metabolome, phenotype and ecosystem), with their respective limitations and risks, we illustrate the enormous potential of the practice. A checklist to determine the reuse value and potential of particular dataset is also provided.

scholarly journals The reuse of public datasets in the life sciences: potential risks and rewards

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9954
Author(s):  
Katharina Sielemann ◽  
Alenka Hafner ◽  
Boas Pucker
Keyword(s):  
Life Sciences ◽  
Research Integrity ◽  
Wide Distribution ◽  
Data Reuse ◽  
Published Data ◽  
Sequencing Data ◽  
Public Sharing ◽  
Open Discussion ◽  
Potential Risks ◽  
Public Datasets

The ‘big data’ revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define ‘successful reuse’ as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.

scholarly journals Making citizen science newsworthy in the era of big data

2017 ◽  
Vol 16 (02) ◽  
pp. C05
Author(s):  
Stuart Allan ◽  
Joanna Redden
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Citizen Science ◽  
Historical Context ◽  
Early Interventions ◽  
Science Journalism ◽  
Potential Risks ◽  
Transparency And Accountability

This article examines certain guiding tenets of science journalism in the era of big data by focusing on its engagement with citizen science. Having placed citizen science in historical context, it highlights early interventions intended to help establish the basis for an alternative epistemological ethos recognising the scientist as citizen and the citizen as scientist. Next, the article assesses further implications for science journalism by examining the challenges posed by big data in the realm of citizen science. Pertinent issues include potential risks associated with data quality, access dynamics, the difficulty investigating algorithms, and concerns about certain constraints impacting on transparency and accountability.

scholarly journals Based on the Software Complexity Measurement of Complex Networks under Big Data Technology

2021 ◽  
Vol 2066 (1) ◽  
pp. 012014
Author(s):  
Xiaobin Hong
Keyword(s):  
Big Data ◽  
Complex Networks ◽  
Computer Technology ◽  
Software Complexity ◽  
Internal Network ◽  
External Threat ◽  
Potential Risks ◽  
Complex Construction ◽  
The Times ◽  
Big Data Technology

Abstract With the development of the times, computer technology is booming, so the network is becoming more and more complex, software design is becoming more and more complex, because of the protection against a variety of internal or external risks. The internal risk is that the traffic carried by the system is too large to cause the system to crash or the system to crash caused by the code operation error, and the external threat is that hackers use computer technology to break into the system according to security vulnerabilities, so the purpose of this paper is based on big data technology, the software complexity of complex networks is measured and studied. With the consent of the school, we used the school’s internal network data, and after consulting the literature on the complex construction and analysis of complex networks and software, modeled and analyzed it using the improved particle group algorithm. The experimental results show that there is a certain correlation between complex network and software complexity. Because complex networks determine that software requires complex construction to withstand potential risks to keep the software running properly.

scholarly journals Semantic Web technologies for the big data in life sciences

2014 ◽  
Vol 8 (4) ◽  
pp. 192-201 ◽  
Author(s):  
Hongyan Wu ◽  
Atsuko Yamaguchi
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Life Sciences ◽  
Semantic Web Technologies ◽  
Web Technologies
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