Making Mountains out of Molehills: Challenges for Implementation of Cross-Disciplinary Research in the Big Data Era

We present a “Researcher’s Hierarchy of Needs” (loosely based on Maslow’s Hierarchy of Needs) in the context of interdisciplinary research in a “big data” era. We discuss multiple tensions and difficulties that researchers face in today’s environment, some current efforts and suggested policy changes to address these shortcomings and present our vision of a future interdisciplinary ecosystem

Key opportunities and challenges for the use of big data in migration research and policy

Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, has been proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the UN, humanitarian NGOs, policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns; and addressing political narratives. We advocate the need for increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities

Key opportunities and challenges for the use of big data in migration research and policy

Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidencebased migration policy. Big data, defined as large, complex data from diverse sources, is regularly proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the United Nations (UN), humanitarian non-governmental organisations (NGOs), policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns around the use of big data; and addressing political narratives. We advocate the need for careful consideration of the challenges faced by the use of big data, as well as increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities

Big data in Financial Management a structured literature review and Opportunities for IS research

Information Systems to support Finance and Accounting functions within organisations form the backbone of modern commerce. Big data has brought a transformational change to this research space the effects of which are starting to be felt in industry and academia. This paper examines the potential research opportunities for the use of “Big data” in the cross disciplinary space of Information Systems, Accounting and Finance. We examine literature at the confluence of these three disciplines to identify current research approaches. An analysis is presented of 47 accounting and finance and information systems (IS) journals from 2007-2016 to identify key themes emerging. These are presented as a conceptual matrix and explored by means of this matrix and a theoretical framework that situates the emerging themes across the three cogent disciplines. The concept matrix reveals potential areas for further research

Data Management Plans: the Importance of Data Management in the BIG‐MAP Project[]**

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU−H2020 BIG-MAP project—a cross-disciplinary project targeting disruptive battery-material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG-MAP and the European BATTERY 2030+ initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR (findable, accessible, interoperable, reusable). This paper describes the process we followed and how we structured the plan

Business intelligence and big data in hospitality and tourism: a systematic literature review

Purpose This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization. Findings Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data

Tarpkalbiniai ir tarpdalykiniai mokslo kalbos tyrimai: medžiagos ir metodų pasirinkimo iššūkiai tyrėjams

Recent trends in academic discourse analysis reveal a keen scholarly interest in cross-disciplinary and cross-linguistic variation in academic texts. While most of the research is still on the English language, the last few decades have seen an upsurge of interest in academic discourse produced in other languages, frequently comparing it to patterns of writing and argumentation in Anglo-American scientific texts. Numerous studies attempt to outline the universal features of academic discourse as well as to highlight the specific ones, typical only of some of the disciplines or cultural communities. Thus, features of academic discourse are often interpreted within the “big” (i. e. national) and “small” (i. e. disciplinary) culture context (cf. Atkinson 2004). The paper briefly reviews trends in current academic discourse research, mainly in the genre of the research article. The purpose of the paper is to discuss the challenges that researchers of academic discourse face while compiling specialized comparable corpora for their cross-disciplinary and cross-linguistic analyses and to highlight certain methodological issues which are important in this type of analyses. As noted by many researchers in the field, the reliability of the results and a better empirical grounding primarily depend on the appropriately selected common ground of comparison. An overview of recently published research on cross-linguistic and cross-disciplinary aspects of academic discourse reveals various methodological solutions to corpus design and data analysis.Straipsnyje trumpai apžvelgiamos dominuojančios šiuolaikinių mokslo kalbos tyrimų kryptys, tačiau pagrindinis dėmesys skiriamas mokslinio straipsnio, kaip akademinio žanro, tyrimams ir jų metodikai. Mokslinio straipsnio struktūra ir jos ypatumai, autoriaus argumentavimo strategijos yra akademinio diskurso bruožai, sulaukę itin didelio kalbininkų susidomėjimo pasaulinėje lingvistikoje. Šio apžvalginio straipsnio tikslas – aptarti tarpdalykinių ir tarpkalbinių tyrimų specifiką ir išryškinti kai kuriuos metodologinius aspektus, svarbius sudarant specialiuosius palyginamuosius mokslo kalbos tekstynus. Remiantis naujausiais šios srities tyrimais, apžvelgiami pasaulio tyrėjų sprendimai tekstyno sandaros ir medžiagos analizės klausimais. Straipsnis galėtų būti naudingas studentams, jauniesiems tyrėjams, besidomintiems specialiųjų mokslo kalbos tekstynų sudarymu ir analize

Deep and cognitive learning applied to Precision Medicine: the initial experiments linking (epi)genome to phenotypes-disease characteristics

Present-day era of Big Data provides the unique opportunity to develop innovative approaches for data analysis to find new insights into specialized fields of biomedical research such as Precision Medicine [1]. Precision Medicine is defined as the integration of molecular research with clinical data in order to deliver better diagnoses and treatments tailored to the individual characteristics of each patient. Advanced analysis of health related data that is specific to a given individual must focus on both clinical information (e.g. clinical reports, medical images, patient histories) and biological data (e.g. gene and protein sequences, functions and pathways). This wealth of information has the potential to inspire systematic ways of making sense from the massive and heterogeneous stream of data and providing a unified view. In the regards, Deep Learning (DL) [2] and Cognitive Computing (CC) [3] are two branches of Artificial Intelligence (AI) representing convenient choices to tackle the problem of Big Data integration for Precision Medicine. DL comprises several machine learning techniques modeling multiple representations of data through many layers of nonlinear processing units. CC is a cross-disciplinary technology for adaptive and contextual knowledge representation and reasoning through sophisticated analytics aiming to mimic human learning mechanisms

Sustaining the Momentum: Archival Analysis of Enterprise Resource Planning Systems (2006–2012)

The domain of Enterprise Resource Planning (ERP) systems is an enduring paradigm for Information Systems (IS) researchers. The Enterprise System paradigm provides a rich environment to test fundamental concepts like system adoption, system use and system success, while acknowledging changes derived through longer system lifecycles and multiple user cohorts. On the other hand, ERP systems are in the centre of new contemporary radical changes in technologies on cloud computing, mobile platforms and big data. Moreover, ERP Systems provide the context for cross disciplinary research such as change management, knowledge management, project management and business process management research. This article provides a critique of 219 papers published on ERP Systems from 2006–2012, making observations of ERP research and make recommendations for future research directions

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future