dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis We examined our theoretical framework using cross-sectional data with the help of variance based structural equation modelling software. Despite recent criticisms, we evaluated the arguments of some scholars to finally decide to proceed with the variance based PLS-SEM technique to validate the theoretical framework (see,  Peng and Lai, 2012; Hair et al. , 2020). WarPLS 7. 0 was used for two main reasons. Firstly, "WarPLS 7. 0 explicitly identifies the non-linear functions connecting pairs of latent variables in SEM models and it determines multivariate coefficients of association"  (Kock, 2021, p. 6). Secondly, this software provides a unique combination of the PLS algorithms and factor-based PLS algorithms for SEM. Kock (2021, p. 6 ) argues that: "… factor-based PLS algorithms generate estimates of both true composites and factors, fully accounting for measurement error".
The literature search was conducted using SPIDER and PICO tools for qualitative, quantitative, and mixed-study designs. Inclusion criteria included articles that investigated patients with NCDs and considered exercise interventions. Systematic searches of PubMed, Web of Science, MEDLINE, EMBASE, and ScienceDirect were undertaken on 26 July 2022 and all reference lists were manually searched. Data processing was performed using EndNote 2. 0 software and data charts were used for numerical summary and thematic analysis. Results: There were 10,951 articles retrieved, of which 28 met the inclusion criteria. Based on the evidence, exercise was a feasible, safe, and acceptable method to prevent and manage non-communicable diseases in inpatient and outpatient settings. Six research directions were identified and discussed.
Search strategy An initial limited search of PubMed was performed to identify relevant articles, ensuring the validity of the proposed idea, confirming and prescribing search items, avoiding duplication of previously addressed questions, and assuring that there were enough articles for conducting the analysis. In the preliminary search, no current scoping reviews focusing on exercise interventions with non-communicable diseases in inpatient and outpatient settings were noted. Following the preliminary search, a comprehensive systematic search was conducted independently of five electronic databases on  26.
Data charting process Identified records were exported to EndNote 20 software for de-duplication. The titles and abstracts were screened independently of all retrieved articles, and the potentially relevant full texts of the remaining articles were screened using eligibility criteria. The following data items were extracted from the included studies: the name of the first author, years and region of publication, characteristics of the patients (sample size, sex, age, medical status, etc. ), study design, exercise interventions contents, comparison condition, outcome measures, and the implementation findings of each study. Information relating to publication year, gender characteristics sample size, research design, exercise interventions type, non-communicable diseases categories, and implementation of outcomes was numerically summarized.
Additionally, a thematic data analysis from the key findings was also performed. The thematic analysis considered: exercise guidelines, recruitment and adherence, acceptability and resources, existing research, study limitation, experimental context, exercise interventions provider, and future research direction. Data extraction was performed using EndNote 2. 0 software. Manually created data charts were used for numerical summary and thematic analysis. The first author developed the data extraction form and a second author checked the form. Any disagreements between authors were resolved through discussion and consultation with a third author.
Data collection Selected participants matched the defined sampling requirements (i. e. , millennials between 18 and 39 years of age for customers, and know-how in the field for luxury experts). A convenience sample of 72 customer respondents was built through "the person on the street" method, interviewing customers exiting flagship luxury stores in two fashion districts in Beijing (i. e. , Beijing Sanlitun Fashion District) and Shanghai (i. e. , Shanghai Xintiandi fashion area). 12 experts were identified through the researchers' professional network and through snowballing (Table  1 ). Most of the experts' interviews were held online. Each meeting lasted approximatively 45 minutes. Data related to customers' behaviours and shopping purchase were held at one AI pervasive flagship store in Shanghai and one in Beijing.
Data Analysis The study relied on the DAST framework to structure the interview scheme. Following  Gioia et al. (2013) , a systemic approach was structured to introduce the different resulting codes and themes  (Corley and Gioia, 2011). Interviews were analysed following three analytic paths. In the first analysis, a narrative of experiences and interactions with respect to AI technologies' terms associated to the in-store experience were identified and, through codes, transcribed into categories. The codes from the first-order analysis were narrowed down through the connection to the emergent second-order themes associated to the DAST components. The thirdorder analysis conceived the data into main themes-based comments and definitions mentioned by respondents and researchers' understanding.
For this purpose, for example, students' test results and acquired certificates are examined. However, other data such as gender and the social background of the students are often collected as well. Here, too, privacy issues arise, and the principle of data minimisation needs to be observed. In this way, AI can be used as a class monitor to support learners and teachers. In this case, AI software provides teachers with essential clues about what is happening in the classroom by collecting and evaluating a wide range of data gathered from pedagogical interactions in the classroom. In the process, cameras and other input and recording media capture and assess the following data: classroom interaction, scanning faces, face recognition, and classification and storing of data (students' behaviour, and facial emotions by applying deep learning methods with neuronal networks).
This will make each of us a little maker, and it allows us to reduce production costs and wasting materials. On the other hand, I believe that companies… will give the customer the option to buy complete furniture with all the accessories, or buy furniture with access to a database of accessories for the customer to print at home and assemble. In the industrial sector, I believe that the trend is for printers to be increasingly part of the production process and not just part of the prototyping process. There are already automotive companies that produce vehicles with printed components, and this will force the printing process to become faster. In the health area, with research into new materials, I think that we will increasingly have prostheses and even organs made using 3D printing biocompatible materials and genetic materials from the recipient, providing prostheses with a longer lifespan, with better compatibility with the patient, minimizing the risks of material rejection.
Literature Review Following the classic GT approach, we used the literature as an additional source of data during the later phases of our research. However, the BDA and capability literature is presented in the second section in order to facilitate reading and understanding of the present article. Also, as a means of describing our findings' contribution to the literature and providing a summary of BDA capabilities in the literature, we used the systematic literature review (SLR) approach. There is no consensus among researchers on how to define BDA and construct BDAC, hence, it was vital to conduct a SLR guidelines by  Kitchenham and Charters (2007)  to define BDA and understand how different constructs build BDAC. The process started by searching for several terms. Research articles were screened from the "Google Scholar" database with publication years ranging from 2010 to 2020.
Data Collection and Analysis We conducted interviews with open and flexible questions. The number of participants and questions were not pre planned. We theoretically sampled data-driven firms that had been working with BDA over the past few years, and should have been able to gain and retain customers, advance sales, profitability and return on investment. All interviews began with an open question aimed at eliciting a comprehensive definition of BDA as well as identifying the main concerns of the participants. As the research progressed and the concepts and relationships emerged, we proceeded with theoretical sampling as  Glaser and Strauss (1967)  explained, and the interview questions became narrower to address the different properties of emerging concepts and their relationships better.
We stopped collecting data after reaching a saturation level and new themes/categories were not discovered. We conducted 25 interviews (coded from P1 to P25) with C-suite executives (i. e. CEOs, CIOs and CDOs), from different types of industries. Confidentiality about datatreatment was formally guaranteed. In some cases, interviews were conducted twice; participants were motivated to continue the discussion, and in other cases, the second interview was conducted to refine concepts/categories, or collect more information. In the first interview with each interviewee, the duration ranged from 60 to 90 min and in the second interview, the duration ranged from 30 to 60 min (Appendix).
BDA Technology Capability (BDA-TEC) BDA technology capability represents technological adaptability to meet BDA characters (5Vs), while implements advanced analytics swiftly: "We use the right technology at the right time" (P8 1  ). Through the coding process five common technological features were found to explain BDA technological capability: (i) Compatibility: enables the technology to integrate and analyze various types of data (structured, semistructured, non-structured) and implement sophisticated analytics to create value. "BDA refers to flexible systems that can easily adapt to new datasets" (P19). Compatibility describes the technological ability to transform a firm's data without limitations on data structure and format, in order to enhance innovation  (Akter et al. , 2016)  and accelerate system redeployment: "Our systems support all types of data formats" (P15), "you need a compatible system to bring flexibility to data processing" (P24).
While investing in novel commodities and hardware may result in cost overruns and executives' disagreement, a scalable system addresses the cost concern of BDA investment: "Our system is fully scalable to meet our current and future data needs" (P8). Though BDA is the revolution of Business Intelligence and Analytics  (Chen et al. , 2012) , conventional and classic information systems are less efficient at processing BD. Slowness in responsiveness, lack of scalability and adaptability to diverse datasets, to support analytics are the main challenges of traditional IT. Furthermore, scalability is crucial for data processing and analysis: "Hadoop Mapreduce has capacity for parallel computing of distributing large data sets across multiple servers (P24), "it is scalable, everything is shared and delivered on it, as a service on a massive scale.
The experimental results are shown in Table  3. The optimal value of T2 in Segmentation data set is 350. 4 Design and research of cross-border e-commerce product recommendation system 4. 1 System requirements analysis addition to product recommendation, product search and query, and online purchase of products, the system also needs to provide a comprehensive back-end support system to supplement the maintenance of product information system and system data, create rich product and resource maintenance and collection in the background, and provide a data platform for product follow-up marketing. Users of the scoring system are classified according to users of the system. Different users different operation permissions, such as advertising management on user pages and users in charge of product management.
In the role of permission allocation, in addition to the function of mass customization, each user also realizes the control of different rights. Each user password assignment should be in a mixture of letters, numbers, and special symbols, and the user password should show security in a hierarchy. Every operation of the user must be recorded in the background run log. All operations can be tracked through the run log to update system data, and the maintenance process can be handled better. The login system will automatically freeze the status of the user who has not logged in for a long time. If the user is frozen, just contact the system administrator. The user can restart and activate the user status. The system can synchronize and back up some operations such as deleting and updating. When a user makes a deletion or change error, the integrity of the original data can be ensured by a retraction.
Methods and Data We conducted fieldwork building on three complementary data-gathering approaches. First, we collated news coverage of the environment during the EU referendum campaign by using Google Alerts during the EU referendum campaign (with key words 'EU referendum' AND 'Environment') and was subsequently supplemented by a Nexis Lexis search (key words 'EU referendum' OR Brexit AND 'environment'). This allowed us to collect both evidence of UK politicians and UK ENGOs raising environmental issues during the campaign. Second, we collated campaigning material and supporting research -by think tanks, ENGOs, the official campaigns -to analyse whether and how they addressed environmental issues. Third, we conducted a series of 12 in-depth, semi-structured interviews with campaigners within the broader UK environmental movement (from a sample of organisations which followed a variety of strategies) to discuss the campaigning choices and strategies of their respective organisations.
All proved at one point active on EU policy issues, by undertaking lobbying or supporting EU legislation or funding programmes. Table  1  reflects the diversity of the nine ENGOs part of the study. It includes seven organisations relying on membership fees or individual donors (FOE England Wales and Northern Ireland, the RSPB, TWTs, WWF UK; the CPRE, Greenpeace UK, ClientEarth), one think tank (E3G), and an umbrella organisation for ENGOs (Green Alliance). We also included the ad hoc and more informal coalition created for the referundum by former high-profile environmental activists as part of subgroup of the European Movement (Environmentalists for Europe (E4E)). These data were subsequently analysed with a pragmatist framework, combining a study of external and internal pressures, which structure our empirical analysis.
Purpose -This paper aims to provide a discussion on the interface and interactions between data, analytical techniques and impactful research in humanitarian health supply chains. New techniques for data capturing, processing and analytics, such as big data, blockchain technology and artificial intelligence, are increasingly put forward as potential "game changers" in the humanitarian field. Yet while they have potential to improve data analytics in the future, larger data sets and quantification per se are no "silver bullet" for complex and wicked problems in humanitarian health settings. Humanitarian health supply chains provide health care and medical aid to the most vulnerable in development and disaster relief settings alike. Unlike commercial supply chains, they often lack resources and long-term collaborations to enable learning from the past and to improve further.
Design/methodology/approach -Based on a combination of the authors' research experience, interactions with practitioners throughout projects and academic literature, the authors consider the interface between data and analytical techniques and highlight some of the challenges inherent to humanitarian health settings. The authors apply a systems approach to represent the multiple factors and interactions between data, analytical techniques and collaboration in impactful research. Findings -Based on this representation, the authors discuss relevant debates and suggest directions for future research to increase the impact of data analytics and collaborations in fostering sustainable solutions. Originality/value -This study distinguishes itself and contributes by bringing the interface and interactions between data, analytical techniques and impactful research together in a systems approach, emphasizing the interconnectedness.
The focus of this paper is on humanitarian health supply chains. In this paper, we define humanitarian health supply chains encompassing health care and medical programs in both development (e. g. childhood immunization, family planning, HIV prevention, diagnosis and treatment in LMICs) and relief operations (epidemic outbreaks such as Ebola, COVID-19, plague and measles). Important differentiating factors between humanitarian health supply chains and regular health supply chains often include the presence of multiple stakeholders or the fragmentation of humanitarian supply chains. New techniques for data capturing, processing and analytics, such as big data, blockchain technology and artificial intelligence (AI), are increasingly put forward as potential "game changers" in the humanitarian field  (Dubey et al.
Another distinguishing feature between commercial and humanitarian supply chains is that the former tends to be proactive, whereas the latter, especially in relief settings, is reactive  (Dubey et al. , 2021). The ultimate goal of (new techniques of) data capture and analysis in humanitarian settings is to help practitioners in humanitarian health supply chains solve real-life problems. To achieve this goal, collaboration between academics and researchers is indispensable. This collaboration is needed both for obtaining the necessary input data and for eventually implementing suggested interventions in practice (e. g. by creating rigorous implementation research frameworks to gauge the impact of these interventions;  Maric et al. , 2021). The interactions between data, analytical techniques and collaboration between academics and practitioners in impactful research in humanitarian health environments are legion, forming a complex system of interconnected loops.
Only by understanding these loops, it is possible to see why data analytics may not be a "silver bullet", how it depends on collaborations and ask what can be done about it. The simplified causal loop diagram (CLD) presented in this paper clearly shows these interconnections. A CLD is a system dynamics (SD) tool used to visualize how different variables in a system are interrelated  (Sterman, 2000). Based on the CLD and a combination of research experience, interactions with practitioners throughout projects and academic literature, we discuss the challenges inherent to humanitarian health settings as a basis for developing recommendations to steer future research in a direction where collaborations and analytics can foster sustainable solutions, resulting in both impactful practical recommendations and relevant research output.
We indicate how this can be accomplished through an exemplary case and by suggesting future research directions. Our paper aims to improve the understanding of the complex system encompassing both data analytics and collaborations among researchers and practitioners in humanitarian health supply chains. The remainder of this paper is organized as follows: Section 2 presents three real-life examples of experiences gained by the authors from research projectsboth successes and failures, to provide the reader with contextual information and insights. Section 3 describes the different factors between data, analytical techniques and collaborative research. We represent the interactions between them in a CLD and highlight the challenges inherent to humanitarian health supply chains. In Section 4, we identify ways to increase the impact of data analytics and collaborations in fostering sustainable solutions, illustrated by a case study (Section 4.
The organization systematically captures considerable data regarding clients visiting the mobile outreach teams (through software that allows them to record client information), which itself is rare in the humanitarian health field. However, the data are "dirty"names and reasons for the visit as well as information about arrival and departure of the mobile outreach team are often poorly recorded, village names are entered in different ways and geographical coordinates are imprecise. In short, it is difficult to use the data for analysis without cleaning them and making linkages, for example, to characteristics of the village in terms of population, poverty, marketing efforts and client numbers. It is important to note that this NGO invests substantial efforts in data gathering, making it a clear leader in the sector.
([humanitarian operations] OR [development operations] OR [global health operations] ). We then selected articles that describe challenges related to data, analytical techniques and collaboration between academics and practitioners in humanitarian health environments. It is not our aim to provide an exhaustive overview of papers using data analytics for humanitarian health operationsfor this, interested readers can consult, for example, Akter and Wamba (  2019  Using the CLD, we conclude that unless a problem is identified and unless relevant and reliable data are coupled with knowledge about the context, data analytics have limited use in these settings because the proposed solutions will neither model a real-life problem nor solve it.
, 2021). The idea still prevails in the field that humanitarian organizations should focus their energy and resources on delivering medical care to people in need rather than developing IT applications  (Falagara Sigala et al. , 2020). Donors often consider the latter as overheads which should be minimized. Nearly every humanitarian organization performs an Emergency Response Evaluation after a severe crisis and poorly engineered data systems are frequently pinpointed as failures, yet investment in systems and apps does not materialize, as it is not their core business. 3. 2. 2 Data overload versus data that are not available, reliable and useful 3. 2. 2. 1 Challenge 2: Collecting context-related data takes time. Most commercial companies have a large database. In humanitarian health settings, data are seldom properly collected, stored or shared, and information systems are lacking (de Vries and Van Wassenhove, 2020; Pedraza-Martinez and Van Wassenhove, 2016).
Current data storage and IT systems used by organizations are diverse, siloed and offer limited scope for collaboration  (Akter and Wamba, 2019; Dubey et al. , 2020). Sometimes, the required data are not directly available but could be extracted from "open sources" like geographical data and social media  (Warnier et al. , 2020). It is possible for a data overload to exist where comprehensive, cross-functional and accurate data are missing, resulting in a fragmented and volatile information landscape  (Comes et al. , 2020; Griffith et al. , 2019; Prasad et al. , 2018). Difficulty of access may be because of different priorities (Section 3. 2. 1) or because multiple stakeholders need to grant access. Hence, it is important to consider data ownership, governance and security in humanitarian health settings.