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Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrir̈e, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zinn.

Language and languages --- Linked data. --- Study and teaching. --- Research. --- Data, Linked --- Linked open data --- LOD (Linked data) --- Open linked data --- Opendata, Linked --- Metadata --- Semantic Web --- Uniform Resource Identifiers --- Foreign languages --- Languages --- Anthropology --- Communication --- Ethnology --- Information theory --- Meaning (Psychology) --- Philology --- Linguistics --- Foreign language study --- Language and education --- Language schools --- Open data, Linked --- Library linked data --- Language and languages Study and teaching --- Study and teaching --- open source --- open data --- open knowledge --- open access --- open science --- Language data and metadata --- Linguistic Linked Open Data --- research data management --- sustainability --- interoperability --- language acquisition --- linguistic annotation --- multilingualism --- communities of practice --- data-intensive research --- CHILDES --- Data Transcription and AnalysisTool --- digital curation --- preservation --- and scholarship --- knowledge infrastructure --- linguistic ontology --- linked data cloud --- metadata interchange --- metatagging --- morphosyntax --- multimedia --- Open Linguistics Working Group --- phonological development --- RDF --- standards --- stewardship --- TALKBANK --- terminology --- under-resourced languages

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Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining.

crisis reporting --- chatbots --- journalists --- news media --- COVID-19 --- textbook research --- digital humanities --- digital infrastructures --- data analysis --- content base image retrieval --- semantic information retrieval --- deep features --- multimedia document retrieval --- data science --- open government data --- governance and social institutions --- economic determinants of open data --- geoinformation technology --- fractal dimension --- territorial road network --- box-counting framework --- script Python --- ArcGIS --- internet of things --- LoRaWAN --- ICT --- The Things Network --- ESP32 microcontroller --- decision systems --- rule based systems --- databases --- rough sets --- prediction by partial matching --- spatio-temporal --- activity recognition --- smart homes --- artificial intelligence --- automation --- e-commerce --- machine learning --- big data --- customer relationship management (CRM) --- distracted driving --- driving behavior --- driving operation area --- data augmentation --- feature extraction --- authorship --- text mining --- attribution --- neural networks --- deep learning --- forensic intelligence --- dashboard --- WebGIS --- data analytics --- SARS-CoV-2 --- Big Data --- Web Intelligence --- media analytics --- social sciences --- humanities --- linked open data --- adaptation process --- interdisciplinary research --- media criticism --- classification --- information systems --- public health --- data mining --- ioCOVID19 --- n/a