Identifying content and levels of representation in scientific data

Wickett, Karen M.; Sacchi, Simone; Dubin, David; Renear, Allen H.

doi:10.1002/meet.14504901199

Cited by 14 publications

(11 citation statements)

References 14 publications

(21 reference statements)

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“…The datasets in Affective Computing field mainly belong to Resource of Community Data Collections, but the state of this category is still immature. Even datasets serve the community of AC researchers and there are some standards like emotional states models, there are no reusable lexicons and ontologies [9] as well as unified data representations [10].…”

Section: Motivationsmentioning

confidence: 99%

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Zawadzka

Wierciński

Meller

et al. 2021

Sensors

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Data reusability is an important feature of current research, just in every field of science. Modern research in Affective Computing, often rely on datasets containing experiments-originated data such as biosignals, video clips, or images. Moreover, conducting experiments with a vast number of participants to build datasets for Affective Computing research is time-consuming and expensive. Therefore, it is extremely important to provide solutions allowing one to (re)use data from a variety of sources, which usually demands data integration. This paper presents the Graph Representation Integrating Signals for Emotion Recognition and Analysis (GRISERA) framework, which provides a persistent model for storing integrated signals and methods for its creation. To the best of our knowledge, this is the first approach in Affective Computing field that addresses the problem of integrating data from multiple experiments, storing it in a consistent way, and providing query patterns for data retrieval. The proposed framework is based on the standardized graph model, which is known to be highly suitable for signal processing purposes. The validation proved that data from the well-known AMIGOS dataset can be stored in the GRISERA framework and later retrieved for training deep learning models. Furthermore, the second case study proved that it is possible to integrate signals from multiple sources (AMIGOS, ASCERTAIN, and DEAP) into GRISERA and retrieve them for further statistical analysis.

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Section: Motivationsmentioning

confidence: 99%

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Zawadzka

Wierciński

Meller

et al. 2021

Sensors

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“…A major problem is that we have no shared formal conceptual model of data representation that is both accurate and sufficiently detailed to support the data needs of scientists belonging to different scientific disciplines [15]. The traditional relational data model is not adequate to represent the data needs of most of the scientific disciplines [16].…”

Section: Data Representationmentioning

confidence: 99%

Research Data Reusability: Conceptual Foundations, Barriers and Enabling Technologies

Thanos

2017

Publications

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High-throughput scientific instruments are generating massive amounts of data. Today, one of the main challenges faced by researchers is to make the best use of the world's growing wealth of data. Data (re)usability is becoming a distinct characteristic of modern scientific practice. By data (re)usability, we mean the ease of using data for legitimate scientific research by one or more communities of research (consumer communities) that is produced by other communities of research (producer communities). Data (re)usability allows the reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others.

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“…The Committee on Revisions to the Common Rule for the Protection of Human Subjects in Research in the Behavioral and Social Sciences concedes that traditional consent protocols are impractical in the context of big data and has recommended a new category of “excused” research (National Research Council, 2014). If big data is not to become Big Brother, users need to be recruited as co-collectors, co-analyzers, co-researchers—equal parties in the data-driven decisions that may today be made over their own lives (Nichols, Twidale, & Cunningham, 2012; Renear & Palmer, 2009; Wickett, Sacchi, Dubin, & Renear, 2012).…”

Section: Implications For Research and Data Infrastructurementioning

confidence: 99%

Big Data Comes to School

Cope

Kalantzis

2016

AERA Open

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The prospect of “big data” at once evokes optimistic views of an information-rich future and concerns about surveillance that adversely impacts our personal and private lives. This overview article explores the implications of big data in education, focusing by way of example on data generated by student writing. We have chosen writing because it presents particular complexities, highlighting the range of processes for collecting and interpreting evidence of learning in the era of computer-mediated instruction and assessment as well as the challenges. Writing is significant not only because it is central to the core subject area of literacy; it is also an ideal medium for the representation of deep disciplinary knowledge across a number of subject areas. After defining what big data entails in education, we map emerging sources of evidence of learning that separately and together have the potential to generate unprecedented amounts of data: machine assessments, structured data embedded in learning, and unstructured data collected incidental to learning activity. Our case is that these emerging sources of evidence of learning have significant implications for the traditional relationships between assessment and instruction. Moreover, for educational researchers, these data are in some senses quite different from traditional evidentiary sources, and this raises a number of methodological questions. The final part of the article discusses implications for practice in an emerging field of education data science, including publication of data, data standards, and research ethics.

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Identifying content and levels of representation in scientific data

Cited by 14 publications

References 14 publications

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Graph Representation Integrating Signals for Emotion Recognition and Analysis

Research Data Reusability: Conceptual Foundations, Barriers and Enabling Technologies

Big Data Comes to School

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