Managing, storing, and sharing long-form recordings and their annotations

Gautheron, Lucas; Rochat, N; Cristià, Alejandrina

doi:10.31234/osf.io/w8trm

Cited by 6 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Corpus organization, processing, and preliminary analyses were done with ChildProject (Gautheron, Rochat, & Cristia, 2022). Additionally, transparency was ensured by publicly posting all of our materials (https://osf.io/t8r5j/?view_only=4e6f8a3b37f84da681b414bc058deca4, Anonymized, 2022), including code to reproduce results thanks to RMarkdown (Baumer & Udwin, 2015) on R (R Consortium Team, 2013), as well as DataLad (Wagner et al, 2020) and GIN (https://gin.g-node.org/).…”

Section: Analysesmentioning

confidence: 99%

Vocal input and output among infants in a multilingual context: Evidence from long-form recordings in Vanuatu

Cristià¹,

Gautheron²,

Colleran³

2023

Preprint

View full text Add to dashboard Cite

What are the vocal experiences of children growing up on Malakula island, Vanuatu, where multilingualism is the norm? Long-form audio-recordings captured spontaneous speech behavior by, and around, 38 children (5-33 months, 23 girls) from 11 villages. Automated analyses revealed most children's vocal input came from female adults and other children’s voices, with small contributions from male adult voices. The greatest changes with age involved an increase in the input vocalizations from other children. Total input (collapsing across child-directed and overheard speech, and across languages) was ~11 minutes per hour, which was at least 5 minutes (31%) lower than that found in other populations studied using comparable methods in previous literature, as well as in archival American data analyzed with the same algorithm. In contrast, children's own vocalization counts were two to four times higher than previous reports for North-American English-learning monolingual infants at matched ages, and comparable to estimates from archival American data, consistent with a resilient language-learning cognitive system for this aspect of vocal development. The strongest association between input and output was with vocalizations by other children, rather than those by adults, which is consistent with research in anthropology but less so with current theoretical trends in developmental psychology. These results invite further research in populations that are under-represented in developmental science.

show abstract

Section: Analysesmentioning

confidence: 99%

Vocal input and output among infants in a multilingual context: Evidence from long-form recordings in Vanuatu

Cristià¹,

Gautheron²,

Colleran³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…DataLad can be used as an independent tool to access and manage data (see e.g. Wittkuhn & Schuck (2021), Gautheron et al (2021), Gautheron (2021)) or as a core technology behind another tool or a larger platform (e.g. Far et al (2021)).…”

Section: External Uses and Integrationsmentioning

confidence: 99%

DataLad: distributed system for joint management of code, data, and their relationship

Halchenko¹,

Meyer²,

Poldrack³

et al. 2021

JOSS

115

View full text Add to dashboard Cite

Summary DataLad is a Python-based tool for the joint management of code, data, and their relationship, built on top of a versatile system for data logistics ( git-annex ) and the most popular distributed version control system ( Git ). It adapts principles of open-source software development and distribution to address the technical challenges of data management, data sharing, and digital provenance collection across the life cycle of digital objects. DataLad aims to make data management as easy as managing code. It streamlines procedures to consume, publish, and update data, for data of any size or type, and to link them as precisely versioned, lightweight dependencies. DataLad helps to make science more reproducible and FAIR ( Wilkinson et al., 2016 ). It can capture complete and actionable process provenance of data transformations to enable automatic re-computation. The DataLad project ( datalad.org ) delivers a completely open, pioneering platform for flexible decentralized research data management (RDM) ( Hanke, Pestilli, et al., 2021 ). It features a Python and a command-line interface, an extensible architecture, and does not depend on any centralized services but facilitates interoperability with a plurality of existing tools and services. In order to maximize its utility and target audience, DataLad is available for all major operating systems, and can be integrated into established workflows and environments with minimal friction.

show abstract

“…Cychosz et al (2021) created a set of Python scripts for efficiently sampling and annotating daylong audio recordings for various features of natural linguistic input (e.g., addressee, language); these scripts are described in detail in the manuscript and freely shared on GitHub (https://github.com/megseekosh/Categorize_app_v2). In addition to these examples, a variety of guidelines, scripts and packages have been created and shared by researchers to facilitate the collection, coding and analysis of naturalistic input (e.g., Anderson et al, 2021; Casillas & Scaff, 2021; Gautheron et al, 2021; Manning et al, 2020; Sanchez et al, 2019; Woon et al, 2021). All of these products serve as excellent examples of how to increase the openness and transparency of descriptive work, but they are also important resources that researchers can implement themselves when coding naturalistic behaviour.…”

Section: Open Coding Procedures and Materialsmentioning

confidence: 99%

Open science considerations for descriptive research in developmental science

Kosie

Lew‐Williams

2022

Infant and Child Development

View full text Add to dashboard Cite

Descriptive developmental research seeks to document, describe, and analyze the conditions under which infants and children live and learn. Here, we articulate how open-science practices can be incorporated into descriptive research to increase its transparency, reliability, and replicability. To date, most open-science practices have been oriented toward experimental rather than descriptive studies, and it can be confusing to figure out how to translate openscience practices (e.g., preregistration) for research that is more descriptive in nature. We discuss a number of unique considerations for descriptive developmental research, taking inspiration from existing open-science practices and providing examples from recent and ongoing studies. By embracing a scientific culture where descriptive research and open science coexist productively, developmental science will be better positioned to generate comprehensive theories of development and understand variability in development across communities and cultures. Highlights Descriptive research is vital to the development of comprehensive theories of early development, and open-science practices can increase the transparency and replicability of this effort. Many existing open-science practices are oriented toward experimental rather than descriptive research. Considerations related to the adoption of open-science practices in descriptive research are discussed, complemented by examples of successful adoption.

show abstract

Managing, storing, and sharing long-form recordings and their annotations

Cited by 6 publications

References 29 publications

Vocal input and output among infants in a multilingual context: Evidence from long-form recordings in Vanuatu

Vocal input and output among infants in a multilingual context: Evidence from long-form recordings in Vanuatu

DataLad: distributed system for joint management of code, data, and their relationship

Open science considerations for descriptive research in developmental science

Contact Info

Product

Resources

About