FAIR data principles and their application to speech and oral archives

Calamai, Silvia; Frontini, Francesca

doi:10.1080/09298215.2018.1473449

Cited by 12 publications

(9 citation statements)

References 12 publications

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“…OH archives constitute only a portion of all audio/video collections. There are various scholarly practices and communities that generate and maintain similar collections [19]. Even though the research and archiving practices for these show differences, the benefits of developments at the intersection of social signal processing and archival practices will be felt within all these domains.…”

Section: Discussionmentioning

confidence: 99%

A Computational Look at Oral History Archives

Pessanha

Salah

2021

J. Comput. Cult. Herit.

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Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale.

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Section: Discussionmentioning

confidence: 99%

A Computational Look at Oral History Archives

Pessanha

Salah

2021

J. Comput. Cult. Herit.

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“…Discussions on the FAIR principle are also expanding in digital archives and libraries. 32 Koster and Woutersen-Windhouwer propose the FAIR principle suitable for LAM (libraries, archives, museums) collections and suggest a practical method to increase the reusability of digital cultural heritage. 33 JAPANESE MILITARY "COMFORT WOMEN" KNOWLEDGE GRAPH 4 PARK AND KIM…”

Section: Reuse Of Ontology Vocabularies and Fair Data Principlesmentioning

confidence: 99%

Japanese Military “Comfort Women” Knowledge Graph

Park¹,

Kim²

2023

ITAL

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Materials related to Japanese military “comfort women” in Korea are managed by several institutions. Each digital archive has their own metadata schema and management policies. So far, a standard or a common guideline for describing digital records is not formalized. We propose a Japanese military “comfort women” knowledge graph to semantically interlink the digital records from distributed digital archives. To build a Japanese military “comfort women” knowledge graph, digital records and descriptive metadata were collected from existing digital archives. A list of metadata was defined by analyzing commonly used properties and a knowledge model designed by reusing standard vocabularies. Knowledge was constructed by interlinking the collected records, external data sources, and enriching data. The knowledge graph was evaluated using the FAIR data maturity model.

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“…DBgen is designed to reduce the barrier to creating databases for complicated data sources in accordance with the FAIR data principle [1]. The FAIR principle, which states that data should be Findable, Accessible, Interoperable, and Reusable, is widely accepted and has shown utility in a variety of fields of scientific research including medicine [2,3,4], meteorology & oceanography [5,6,7,8], oral speech studies [9], botany [10], mass spectrometry [11], and many others. Although many agree that it is important to make data FAIR, a great deal of scientific data remains stored in a way that does not meet these principles.…”

Section: Motivation and Significancementioning

confidence: 99%

DBgen: A Python Library for Defining Scalable, Maintainable, Accessible, Reconfigurable, Transparent (SMART) Data Pipelines

Statt¹,

Brown

Suram³

et al. 2021

Preprint

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In this work, we present DBgen, a Python library that provides a framework for defining extract-transform-load (ETL) pipelines to create and populate SQL databases. DBgen is most useful when the underlying data has complex relationships, requires multi-step analysis, is large-scale, and the type of data being collected changes frequently. Scientific data often fits this description. With current tooling, defining ETL pipelines for this particularly difficult- to-manage data is so onerous that a great deal of it does not end up being stored in a database and is opaque. DBgen is designed to fill the gap in the current tooling and reduce the barrier to defining ETL pipelines such data.

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FAIR data principles and their application to speech and oral archives

Cited by 12 publications

References 12 publications

A Computational Look at Oral History Archives

A Computational Look at Oral History Archives

Japanese Military “Comfort Women” Knowledge Graph

DBgen: A Python Library for Defining Scalable, Maintainable, Accessible, Reconfigurable, Transparent (SMART) Data Pipelines

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