scholarly journals Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo

2017 ◽  
Author(s):  
Michael J Madison
Keyword(s):  
Big Data ◽  
Citizen Science ◽  
Large Scale ◽  
Data Science ◽  
Digital Data ◽  
Peer Production ◽  
Data Intensive ◽  
Knowledge Commons ◽  
Large Numbers ◽  
Citizen Science Project

The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, sometimes known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.

scholarly journals The epistemic culture in an online citizen science project: Programs, antiprograms and epistemic subjects

2018 ◽  
Vol 48 (4) ◽  
pp. 564-588 ◽  
Author(s):  
Dick Kasperowski ◽  
Thomas Hillman
Keyword(s):  
Citizen Science ◽  
Distributed Cognition ◽  
Large Scale ◽  
Science Project ◽  
Large Sets ◽  
The Galaxy ◽  
Epistemic Culture ◽  
Classification Of Images ◽  
Citizen Science Project

In the past decade, some areas of science have begun turning to masses of online volunteers through open calls for generating and classifying very large sets of data. The purpose of this study is to investigate the epistemic culture of a large-scale online citizen science project, the Galaxy Zoo, that turns to volunteers for the classification of images of galaxies. For this task, we chose to apply the concepts of programs and antiprograms to examine the ‘essential tensions’ that arise in relation to the mobilizing values of a citizen science project and the epistemic subjects and cultures that are enacted by its volunteers. Our premise is that these tensions reveal central features of the epistemic subjects and distributed cognition of epistemic cultures in these large-scale citizen science projects.

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

scholarly journals A nationwide assessment of plastic pollution in the Danish realm using citizen science

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kristian Syberg ◽  
Annemette Palmqvist ◽  
Farhan R. Khan ◽  
Jakob Strand ◽  
Jes Vollertsen ◽  
...  
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Public Awareness ◽  
Decision Makers ◽  
Plastic Pollution ◽  
Political Decision ◽  
Pollution Research ◽  
Beach Litter ◽  
Citizen Science Project ◽  
Scientific Survey

Abstract Plastic pollution is considered one of today’s major environmental problems. Current land-based monitoring programs typically rely on beach litter data and seldom include plastic pollution further inland. We initiated a citizen science project known as the Mass Experiment inviting schools throughout The Danish Realm (Denmark, Greenland and the Faeroe Islands) to collect litter samples of and document plastic pollution in 8 different nature types. In total approximately 57,000 students (6–19 years) collected 374,082 plastic items in 94 out of 98 Danish municipalities over three weeks during fall 2019. The Mass Experiment was the first scientific survey of plastic litter to cover an entire country. Here we show how citizen science, conducted by students, can be used to fill important knowledge gaps in plastic pollution research, increase public awareness, establish large scale clean-up activities and subsequently provide information to political decision-makers aiming for a more sustainable future.

scholarly journals Taking a ‘Big Data’ approach to data quality in a citizen science project

AMBIO ◽  
2015 ◽  
Vol 44 (S4) ◽  
pp. 601-611 ◽  
Author(s):  
Steve Kelling ◽  
Daniel Fink ◽  
Frank A. La Sorte ◽  
Alison Johnston ◽  
Nicholas E. Bruns ◽  
...  
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Citizen Science ◽  
Science Project ◽  
Citizen Science Project

scholarly journals Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection

2018 ◽  
Author(s):  
Jen Schradie
Keyword(s):  
Big Data ◽  
Data Science ◽  
Digital Data ◽  
The Internet ◽  
Sociological Research ◽  
Marginalized Populations ◽  
Online Data ◽  
Persistent Problem ◽  
Current State ◽  
Using Data

With a growing interest in data science and online analytics, researchers are increasingly using data derived from the Internet. Whether for qualitative or quantitative analysis, online data, including “Big Data,” can often exclude marginalized populations, especially those from the poor and working class, as the digital divide remains a persistent problem. This methodological commentary on the current state of digital data and methods disentangles the hype from the reality of digitally produced data for sociological research. In the process, it offers strategies to address the weaknesses of data that is derived from the Internet in order to represent marginalized populations.

scholarly journals Data Science: Trends, Perspectives, and Prospects

2021 ◽  
Author(s):  
Chaolemen Borjigin ◽  
Chen Zhang
Keyword(s):  
Big Data ◽  
Data Science ◽  
Scientific Discovery ◽  
Big Data Analytics ◽  
Theoretical Studies ◽  
Data Intensive ◽  
Data Ethics ◽  
Big Data Visualization ◽  
Data Products ◽  
And Performance

Abstract Data Science is one of today’s most rapidly growing academic fields and has significant implications for all conventional scientific studies. However, most of the relevant studies so far have been limited to one or several facets of Data Science from a specific application domain perspective and fail to discuss its theoretical framework. Data Science is a novel science in that its research goals, perspectives, and body of knowledge is distinct from other sciences. The core theories of Data Science are the DIKW pyramid, data-intensive scientific discovery, data science lifecycle, data wrangling or munging, big data analytics, data management and governance, data products development, and big data visualization. Six main trends characterize the recent theoretical studies on Data Science: growing significance of DataOps, the rise of citizen data scientists, enabling augmented data science, diversity of domain-specific data science, and implementing data stories as data products. The further development of Data Science should prioritize four ways to turning challenges into opportunities: accelerating theoretical studies of data science, the trade-off between explainability and performance, achieving data ethics, privacy and trust, and aligning academic curricula to industrial needs.

scholarly journals Emergent Technologies in Big Data Sensing: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Sheng Xiao ◽  
Qingquan Zhang ◽  
Yu Gu ◽  
Ping Yi ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Mobile Sensing ◽  
Emergent Technologies ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Research Architecture

When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

scholarly journals Caring for (Big) Data: An Introduction to Research Methodologies and Ethical Challenges in Digital Migration Studies

2021 ◽  
pp. 1-21
Author(s):  
Marie Sandberg ◽  
Luca Rossi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Activity ◽  
Data Access ◽  
Digital Data ◽  
Data Sets ◽  
Ethical Challenges ◽  
Migration Studies ◽  
Critical Approach ◽  
Migration Research

AbstractDigital technologies present new methodological and ethical challenges for migration studies: from ensuring data access in ethically viable ways to privacy protection, ensuring autonomy, and security of research participants. This Introductory chapter argues that the growing field of digital migration research requires new modes of caring for (big) data. Besides from methodological and ethical reflexivity such care work implies the establishing of analytically sustainable and viable environments for the respective data sets—from large-scale data sets (“big data”) to ethnographic materials. Further, it is argued that approaching migrants’ digital data “with care” means pursuing a critical approach to the use of big data in migration research where the data is not an unquestionable proxy for social activity but rather a complex construct of which the underlying social practices (and vulnerabilities) need to be fully understood. Finally, it is presented how the contributions of this book offer an in-depth analysis of the most crucial methodological and ethical challenges in digital migration studies and reflect on ways to move this field forward.

An Enhancement of Cloud Based Sentiment Analysis and BDAAs Using SVM Based Lexicon Dictionary and Adaptive Resource Scheduling

2018 ◽  
Vol 15 (2) ◽  
pp. 437-445 ◽  
Author(s):  
S. Radha ◽  
C. Nelson Kennedy Babu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Resource Scheduling ◽  
Data Integrity ◽  
Digital Data ◽  
Large Set ◽  
Compression Technique ◽  
Adaptive Resource

At present, the cloud computing is emerging technology to run the large set of data capably, and due to fast data growth, processing of large scale data is becoming a main point of information method and customers can estimate the quality of brands of products employing the information given by new digital marketing channels in social media. Thus, every enterprise requires finding and analyzing a big amount of digital data in order to develop their reputation among the customers. Therefore, in this paper, SLA (Service Level Agreement) based BDAAs (Big Data Analytic Applications) using Adaptive Resource Scheduling and big data with cloud based sentiment analysis is proposed to provide the deep web mining, QoS and to analyze the customer behaviors about the product. In this process, the spatio-temporal compression technique can be applied to data compression for reduction of big data. The data is classified in to positive, negative or neutral by employing the SVM with lexicon dictionary based on the customers' behaviors about brand or products. In cloud computing environment, complex to the reduction of resources cost and fluctuation of resource requirements with BDAAs. As a result, it is needed to have a common Analytics as a Service (AaaS) platform that provides a BDAAs to customers in different fields as unpreserved services in a simple to utilize a way with lower cost. Therefore, SLA based BDAAs is developed to utilize the adaptive resource scheduling depending on the customer behaviors and it can provide visualization and data integrity. Our method can give privacy of cloud owner's information with help of data integrity and authentication process. Experimental results of proposed system shows that the sentiment analysis method for online product using cloud based big data is able to classify the opinions of customers accurately and effective of the algorithm in guarantee of SLA.

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