Suchergebnisse

Purpose
The purpose of this paper is to discuss the challenges of translating qualitative data from Arabic to English within the field of gender and management studies.


Design/methodology/approach
This paper presents a real experience of translating qualitative data from Arabic to English within the management area in Saudi Arabia. Data were collected from interviewing female managers working in the Saudi public sector.


Findings
Translating qualitative data from Arabic to English is not a straightforward process. During the translation of this cross-cultural qualitative research, three challenges were faced: the embeddedness of the language meaning in the culture, positionality and its influence on data production and language differences. The study found that, in absence of a standard way for translating cross-linguistic qualitative research, a combination of methods is found to be useful to increase the validity and reliability of the study findings. Researchers who conduct qualitative insider research and who translate their own data are in a better position to do cross-language data analysis.


Originality/value
This paper contributes to the literature by showing that translating Arabic texts regarding gender, management and leadership is embedded within historical, cultural, political and institutional contexts, requiring a deep understanding of both language and culture to produce a depth of knowledge. Also, the novelty of this study is that it highlights the importance of being an insider qualitative researcher and translating the research data, as researcher offers significant opportunities for close attention to certain points in the text; and this could add value to the analysis as a way to establish validity of interpretations.

The FRAMES (Fibre Reinforced thermoplAstics Manufacturing for stiffEned, complex, double curved Structures) project, supported by Clean Sky 2 Joint Undertaking and the DLR, intend to develop advanced knowledge and manufacturing solutions for a full scale thermoplastic aircraft rear end. The project is part of the Clean Sky 2 initiative focused on developing concepts and enabling technologies for an optimum rear fuselage an empennage. FRAMES main objective is to validate and assess a manufacturing approach of an integral thermoplastic rear end with critical design features. Key technologies developed within FRAMES will be used into a mid-scale advanced rear end demonstrator manufactured by the Deutsches Zentrum für Luft- und Raumfahrt (DLR), part of a Clean Sky 2 technology platform for large passenger aircrafts. Project was launched on July 2020 and targets to deliver a xenon heating device simulation model for thermoplastic composites fiber placement, efficient double curved TP-stiffeners manufacturing solution and associated prototypes as well as a self-heating tooling equipment able to perform a coconsolidation of the skin and stiffeners in one shot. The Data Management Plan describes the data management life cycle for the data to be collected, processed and generated by the FRAMES project. FRAMES DMP is intended to be a living document to be updated throughout the project with generated results. To ensure accessibility of the data, FRAMES DMP applies through the whole duration of activities until the last update of data.

AbstractThis methodological and theoretical note is the cornerstone of empirical research I began this past summer (2023) on bear safety in backcountry wilderness settings. I am using this pilot data to inform the future iterations of the study to learn more about people's relationships to bears and their knowledge of safe practices while recreating in the presence of bears. This note, written in the early months of 2024, focuses on refining the pilot survey and developing the research program which will be expanded to four locations in the U.S. to assess the knowledge of backcountry hikers regarding bear safety and their preferences for bear management. This essay ponders the development of future iterations of this research agenda, including methodology, theoretical development, and practical value.

ObjectivesThe Ministry of Justice's pioneering data linking programme Data First, funded by Administrative Data Research UK, links administrative datasets across the justice system and with other government departments to enable research providing critical new insights on justice system users, their pathways, and outcomes across a range of public services.
ApproachThe first two datasets shared under the Data First project are magistrates' courts and Crown Court data which have been deidentified, deduplicated and linked to provide a joined-up picture of criminal court defendant and case journeys. Accredited researchers can access this data using the ONS Secure Research Service to conduct research. Administrative Data Research UK has funded four Research Fellows to conduct analysis using this linked data. Additionally, analysts within the Ministry of Justice Data First team have published a research report showcasing the potential of the linked criminal courts data. The presentation will primarily focus on this work.
ResultsThe Data First criminal courts datasets have enabled, for the first time, the extent and nature of repeat users to be explored at scale for research. In March 2022, the Ministry of Justice published exploratory analysis of returning defendants and the potential of linked criminal courts data. The key findings of this report will be covered in the presentation. The research demonstrates more than half of defendants returned to the courts within the data period, but this was highest for specific offence groups, including theft, robbery and drug offences. Locality-based analysis on Crown Court defendants highlights important insights on the backgrounds of justice system users, showing an over-representation of defendants residing in the most deprived areas in England and Wales compared to the general population.
ConclusionThe presentation will demonstrate how linked administrative data available through the ground-breaking Data First programme can be effectively used for research. This insight improves our understanding of individuals in the justice system as well as providing a rich resource to develop the evidence base for government policy and practice.

This Data Management Plan (DMP) addresses the relevant aspects of making data FAIR – findable, accessible, interoperable and re-usable. ; This deliverable is a part of a project receiving funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 875052.

The data management plan (DMP) covers the principles and protocols for data collection, processing, sharing and storage. The DMP will ensure that data management is compliant with the EU's Guidelines on Findable, Accessible, Interoperable and Reusable (FAIR) Data Management in Horizon 2020, and with relevant national data protection laws and institutional data management policies.

The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community.We present our insights developing FAIR thresholds in a domain specific context for self-governance by a community (agricultural research). 'Minimum thresholds' for FAIR data are required to align expectations for data delivered from providers' distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed).Data providers were supported to make data holdings more FAIR. There was a range of different FAIR starting points, organisational goals, and end user needs, solutions, and capabilities. This informed the distilling of a set of FAIR criteria ranging from 'Minimum thresholds' to 'Stretch targets'. These were operationalised through consensus into a framework for governance and implementation by the agricultural research domain community.Improving the FAIR maturity of data took resourcing and incentive to do so, highlighting the challenge for data federations to generate value whilst reducing costs of participation. Our experience showed a role for supporting collective advocacy, relationship brokering, tailored support, and low-bar tooling access particularly across the areas of data structure, access and semantics that were challenging to domain researchers. Active democratic participation supported by a governance framework like AgReFed's will ensure participants have a say in how federations can deliver individual and collective benefits for members.

"The world is saturated with data. We are regularly presented with data in words, tables, and graphics. Students from many academic fields are now expected to be educated about data in one form or another. Yet the typical sequence of courses--introductory statistics and research methods--does not provide sufficient information about data, learning to work with data sets, or how to present data to various audiences. This book is designed for these purposes. It discusses how data are used in research projects, where to get data, how to manage them with software, and how to present them so that one's message comes through clearly. With few expectations beyond some familiarity with basic statistics and research methods, this book provides a comprehensive set of principles for understanding and using data as part of a research project"--Provided by publisher

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.

The Handbook of Health Services Research is a reference for all aspects of the field of health services and outcomes research. It addresses the increasing need for comprehensive, yet balanced, information in a field that welcomes various disciplines: medicine, public health, statistics, economics, management, policy, and information technology. This well-organized reference is an indispensable source of information for everyone who seeks to develop understanding of health systems and to learn about historical, political, and socioeconomic factors that influence health policies at the global, national, regional and local level. Specifically, the Handbook helps readers: Recognize core concepts of health services and outcomes research, such as, need, access, equity, quality and safety; Become familiar with social, political, organizational, behavioral and economic theories that have influenced health systems designs; Learn about frameworks developed for evaluating the organization, financing, delivery, utilization and outcomes of health services; Get an introduction to methods of comparative effectiveness research, program evaluation, health technology assessment and health economics; Identify types and sources of data appropriate for generating valid and reliable information about the delivery of health services; Learn about strengths and weaknesses of various research designs used to study health services and policy issues. The online version of the Handbook of Health Services Research is in the format of a dynamically updated knowledge base, offering search tools, cross-referencing across chapters and linking to supplement data, other major reference works and external articles. The Handbook of Health Services Research is accessible at the level of graduate students even if it is not their focus area. This includes students with various backgrounds: medicine, public health, statistics, economics, management or information technology