Large-scale Quantitative Study Research Articles

Industrial design rights and the market value of firms

This paper studies how investments in design affect the market value of firms, using European Registered Community Designs and national Swedish design rights data combined with R&D, patent and trademark data, for the period 2003–2013. This paper is the first large-scale quantitative study using industrial designs rights to measure design. Nonlinear least-squares regressions are used to investigate the association between these variables and the market value of Swedish firms. The results show that industrial designs rights have a positive and significant relationship to the market value of firms. It provides both new evidence on the effect of industrial design rights on the market value of firms and internationally comparable results for other intangible assets such as patents, trademarks and R&D. Theoretically, the paper contributes to the understanding of design knowledge and the valuation of design rights.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network.

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregards frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.

Peculiarities of gender disambiguation and ordering of non-English authors’ names for Economic papers beyond core databases①

Abstract Purpose To supplement the quantitative portrait of Ukrainian Economics discipline with the results of gender and author ordering analysis at the level of individual authors, special methods of working with bibliographic data with a predominant share of non-English authors are used. The properties of gender mixing, the likelihood of male and female authors occupying the first position in the authorship list, as well as the arrangements of names are studied. Design/methodology/approach A data set containing bibliographic records related to Ukrainian journal publications in the field of Economics is constructed using Crossref metadata. Partial semi-automatic disambiguation of authors’ names is performed. First names, along with gender-specific ethnic surnames, are used for gender disambiguation required for further comparative gender analysis. Random reshuffling of data is used to determine the impact of gender correlations. To assess the level of alphabetization for our data set, both Latin and Cyrillic versions of names are taken into account. Findings The lack of well-structured metadata and the poor use of digital identifiers lead to numerous problems with automatization of bibliographic data pre-processing, especially in the case of publications by non-Western authors. The described stages for working with such specific data help to work at the level of authors and analyse, in particular, gender issues. Despite the larger number of female authors, gender equality is more likely to be reported at the individual level for the discipline of Ukrainian Economics. The tendencies towards collaborative or solo-publications and gender mixing patterns are found to be dependent on the journal: the differences for publications indexed in Scopus and/or Web of Science databases are found. It has also been found that Ukrainian Economics research is characterized by rather a non-alphabetical order of authors. Research limitations Only partial authors’ name disambiguation is performed in a semi-automatic way. Gender labels can be derived only for authors declared by full First names or gender-specific Last names. Practical implications The typical features of Ukrainian Economic discipline can be used to perform a comparison with other countries and disciplines, to develop an informed-based assessment procedure at the national level. The proposed way of processing publication data can be borrowed to enrich metadata about other research disciplines, especially for non-English speaking countries. Originality/value To our knowledge, this is the first large-scale quantitative study of Ukrainian Economic discipline. The results obtained are valuable not only at the national level, but also contribute to general knowledge about Economic research, gender issues, and authors’ names ordering. An example of the use of Crossref data is provided, while this data source is still less used due to a number of drawbacks. Here, for the first time, attention is drawn to the explicit use of the features of the Slavic authors’ names.

VULNERABLE LEADERS AND TEACHERS IN ENGLISH SCHOOLS IN THE COVID-19 PANDEMIC: IMPLICATIONS FOR LEADERSHIP OF WELLBEING, RETENTION AND EQUITY

This paper explores the wellbeing of school leaders and teachers in England who assessed a specific health risk during the Covid-19 pandemic in 2020. It explores how this cohort, guided to physically shield, was affected in attitudes towards work and the extent to which leadership behaviours supported a positive and perhaps ‘thriving’ physical return in a post-Covid-19 environment for effective schools and staff retention.Educational systems worldwide have become a high-stakes accountability landscape, with leaders questioning how they can lead with integrity. Staff wellbeing has moved up the agenda in England, forming part of England’s inspection framework, with one criterion being that staff consistently report high levels of support for wellbeing issues (Ofsted, 2019).Insufficient numbers of teachers, with an early exit of novices, is a particularly negative structural feature of the English school system (OECD 2017). A large scale quantitative study indicates that teacher retention is crucial to meet rising pupil numbers (Worth and van den Brande 2020). In the same study, unmanageable workloads and low job satisfaction are cited as significant factors determining teachers’ retention decisions and where teacher autonomy is strongly related to the extent to which teachers regard their workload as manageable (ibid).The study is as concerned with leadership capability as the wellbeing and retention of staff and decision-making driving processes. It is in the development of leaders faced with extraordinary stresses that researchers can understand where gaps lie in leadership development and organisational structure and culture and how these gaps might be strategically addressed. The empirical research on which the paper is based explores some facets for leading effectively in extraordinary circumstances. The paper presents findings on how external structural government directives have challenged schools in carrying out their instructions, amplifying inequality.

Upstreamist leaders: how risk factors for unscheduled return visits (URV) to the emergency department can inform integrated healthcare

PurposeThis paper aims to report on research undertaken in an National Health Service (NHS) emergency department in the north of England, UK, to identify which patients, with which clinical conditions are returning to the emergency department with an unscheduled return visit (URV) within seven days. This paper analyses the data in relation to the newly introduced Integrated Care Boards (ICBs). The continued upward increase in demand for emergency care services requires a new type of “upstreamist”, health system leader from the emergency department, who can report on URV data to influence the development of integrated care services to reduce further demand on the emergency department.Design/methodology/approachPatients were identified through the emergency department symphony data base and included patients with at least one return visit to emergency department (ED) within seven days. A sample of 1,000 index visits between 1 January 2019–31 October 2019 was chosen by simple random sampling technique through Excel. Out of 1,000, only 761 entries had complete data in all variables. A statistical analysis was undertaken using Poisson regression using NCSS statistical software. A review of the literature on integrated health care and its relationship with health systems leadership was undertaken to conceptualise a new type of “upstreamist” system leadership to advance the integration of health care.FindingsOut of all 83 variables regressed with statistical analysis, only 12 variables were statistically significant on multi-variable regression. The most statistically important factor were patients presenting with gynaecological disorders, whose relative rate ratio (RR) for early-URV was 43% holding the other variables constant. Eye problems were also statistically highly significant (RR = 41%) however, clinically both accounted for just 1% and 2% of the URV, respectively. The URV data combined with “upstreamist” system leadership from the ED is required as a critical mechanism to identify gaps and inform a rationale for integrated care models to lessen further demand on emergency services in the ED.Research limitations/implicationsAt a time of significant pressure for emergency departments, there needs to be a move towards more collaborative health system leadership with support from statistical analyses of the URV rate, which will continue to provide critical information to influence the development of integrated health and care services. This study identifies areas for further research, particularly for mixed methods studies to ascertain why patients with specific complaints return to the emergency department and if alternative pathways could be developed. The success of the Esther model in Sweden gives hope that patient-centred service development could create meaningful integrated health and care services.Practical implicationsThis research was a large-scale quantitative study drawing upon data from one hospital in the UK to identify risk factors for URV. This quality metric can generate important data to inform the development of integrated health and care services. Further research is required to review URV data for the whole of the NHS and with the new Integrated Health and Care Boards, there is a new impetus to push for this metric to provide robust data to prioritise the need to develop integrated services where there are gaps.Originality/valueTo the best of the authors’ knowledge, this is the first large-scale study of its kind to generate whole hospital data on risk factors for URVs to the emergency department. The URV is an important global quality metric and will continue to generate important data on those patients with specific complaints who return back to the emergency department. This is a critical time for the NHS and at the same time an important opportunity to develop “Esther” patient-centred approaches in the design of integrated health and care services.

A study of inline assembly in solidity smart contracts

The Solidity programming language is the most widely used language for smart contract development. Improving smart contracts’ correctness, security, and performance has been the driving force for research in vulnerability detection, program analysis, and compiler techniques for Solidity. Similar to system-level languages such as C, Solidity enables the embedding of low-level code in programs, in the form of inline assembly code. Developers use inline assembly for low-level optimizations, extending the Solidity language through libraries, and using blockchain-specific opcodes only available through inline assembly. Nevertheless, inline assembly fragments are not well understood by an average developer and can introduce security threats as well as affect the optimizations that can be applied to programs by the compiler; it also significantly limits the effectiveness of source code static analyzers that operate on the Solidity level. A better understanding of how inline assembly is used in practice could in turn increase the performance, security, and support for inline assembly in Solidity. This paper presents a large-scale quantitative study of the use of inline assembly in 6.8M smart contracts deployed on the Ethereum blockchain. We find that 23% of the analyzed smart contracts contain inline assembly code, and that the use of inline assembly has become more widespread over time. We further performed a manual qualitative analysis for identifying usage patterns of inline assembly in Solidity smart contracts. Our findings are intended to help practitioners understand when they should use inline assembly and guide developers of Solidity tools in prioritizing which parts of inline assembly to implement first. Finally, the insights of this study could be used to enhance the Solidity language, improve the Solidity compiler, and to open up new research directions by driving future researchers to build appropriate methods and techniques for replacing inline assembly in Solidity programs when there is no real necessity to use it.

The colonization of Wikipedia: evidence from characteristic editing behaviors of warring camps

PurposeTo add new empirical knowledge to debates about social practices of peer production communities, and to conversations about bias and its implications for democracy. To help identify Wikipedia (WP) articles that are affected by systematic bias and hopefully help alleviate the impact of such bias on the general public, thus helping enhance both traditional (e.g. libraries) and online information services (e.g. Google) in ways that contribute to democracy. This paper aims to discuss the aforementioned objectives.Design/methodology/approachQuantitatively, the authors identify edit-warring camps across many conflict zones of the English language WP, and profile and compare success rates and typologies of camp edits in the corresponding topic areas. Qualitatively, the authors analyze the edit war between two senior WP editors that resulted in imbalanced and biased articles throughout a topic area for such editorial characteristics through a close critical reading.FindingsThrough a large-scale quantitative study, the authors find that winner-take-all camps exhibit biasing editing behaviors to a much larger extent than the camps they successfully edit-war against, confirming findings of prior small-scale qualitative studies. The authors also confirm the employment of these behaviors and identify other behaviors in the successful silencing of traditional medicinal knowledge on WP by a scientism-biased senior WP editor through close reading.Social implicationsWP sadly does, as previously claimed, appear to be a platform that represents the biased viewpoints of its most stridently opinionated Western white male editors, and routinely misrepresents scholarly work and scientific consensus, the authors find. WP is therefore in dire need of scholarly oversight and decolonization.Originality/valueThe authors independently verify findings from prior personal accounts of highly power-imbalanced fights of scholars against senior editors on WP through a third-party close reading of a much more power balanced edit war between senior WP editors. The authors confirm that these findings generalize well to edit wars across WP, through a large scale quantitative analysis of unbalanced edit wars across a wide range of zones of contention on WP.

Social connectedness of carers: An Australian national survey of carers.

Carers of people living with a disability, mental health problems, alcohol or drug dependency, chronic condition, terminal illness or who are frail due to age may experience negative caregiving impacts. Although carers' social isolation has been reported in many qualitative studies, it has largely been neglected in quantitative studies. Using data collected in the Carers NSW 2020 National Carer Survey, this large-scale quantitative study aimed to identify the extent of Australian carers' social connectedness and what factors may be related to their social well-being. The validated Friendship Scale was used to measure social connectedness of 5585 carers. More than half (56.2%) of these carers were socially isolated. Analysis found that a longer duration of caring, more time spent weekly caring, living with the care recipient, caring for a greater number of people, receiving no help from others, higher reported psychological distress and reporting perceived needs were all associated with greater social isolation. Identifying as female or nonbinary/gender diverse, identifying with a culturally and linguistically diverse background, and caring as a parent, former partner or young carer were also related to poorer social connectedness. Health and social services need to consider the needs of carers, identify carers who are socially isolated and provide resources to promote social connectedness. Greater attention in practice and research to focus on carers' social connectedness to address this crucial caregiving experience is required.

Quantification of human embryonic ζ-globin chains in Southeast Asian deletion (--SEA) carriers

AimsReactivation of embryonic ζ-globin is a promising strategy for genetic treatment of α-thalassaemia. However, quantification of ζ-globin as a quantitative trait in α-thalassaemia carriers and patients remains incompletely understood. In...

Bots Amplify and Redirect Hate Speech in Online Discourse About Racism During the COVID-19 Pandemic

Online talk about racism has been salient throughout the COVID-19 pandemic. Yet while such social media conversations reflect existing tensions in the offline world, the same discourse has also become a target for information operations aiming to heighten social divisions. This article examines Twitter discussions of racism in the first and sixth months since COVID-19 was accorded pandemic status by the World Health Organization and uncovers dynamic associations with bot activity and hate speech. Humans initially constituted the most hateful accounts in online conversations about racism in March, but in August, bots dominated hate speech. Over time, greater bot activity likewise amplified levels of hate speech a week later. Moreover, while discourse about racism in March primarily featured an organic focus on racial identities like Asian and Chinese, we further observed a bot-dominated focus in August toward political identities like president, Democrat, and Republican. Although hate speech targeting Asian groups remained present among racism discussions in August, these findings suggest a bot-fueled redirection from focusing on racial groups at the onset of the pandemic to targeting politics closer to the 2020 US elections. This work enhances understanding of the complexity of racism discussions during the pandemic, its vulnerability to manipulation through information operations, and the large-scale quantitative study of inorganic hate campaigns in online social networks.

A global-scale analysis of the sharing economy model – an AirBnB case study

The sharing economy model has changed the way in which people engage in a variety of activities, including travelling, trading, working, and lending/borrowing money. Several studies exist that aim to understand, quantify and model such phenomenon, but most such studies are geographically focused on countries in the Western World. Knowledge about the penetration and adoption of this novel market model in non-Western countries is much more limited, and almost completely lacking when it comes to emerging markets, where it was touted to bring the biggest benefits and be a game changer to uplift people economically. To close the gap, we chose Airbnb as an example of sharing economy model with worldwide market penetration, and performed a large-scale quantitative study of its penetration and adoption in seven cities in Asia, five cities in Latin America. We compared findings against seven cities in the Western World, and observed patterns to be similar across all locales, with two notable exceptions: the geographic penetration of such services, and the experience that guests travelling to such destinations shared in their reviews.

Investigating Installers of Security Software in 20 Countries: Individual- and Country-Level Differences

This article provides detailed evidence about the installers of online security software on personal computers according to differences among clusters of countries and various other country characteristics. The study presents unique data based on real installations around the world. The data are based on a large-scale quantitative study ( N = 18,727 ) which was prepared in cooperation with an international security company. The cluster analyses revealed four distinct clusters of software installers: those who install the software for a different user, those who are IT technicians and mostly install the software for other users, those who install the software for themselves and others on a shared computer, and those who install the software only for themselves. A second cluster analysis revealed four different country clusters. Within these clusters, countries handle online security software installation similarly; however, there are differences for the clusters according to industrialized, English-speaking countries and the cluster of developing countries. This study presents unique cluster analyses of the countries to shed light on the cross-culture differences in security software adoption and installation. The results implicate that software companies should consider providing different versions of the security software to match the country characteristics.

BETTER LATE THAN NEVER! INVESTIGATING DETERMINANTS OF AND DIFFERENCES BETWEEN TEMPORARY AND CONTINUOUS INNOVATION REJECTIONS

Little research effort has been dedicated to investigate the nature and determinants of and the differences between temporary and continuous consumer rejections of innovations. To shed light on both types of consumer rejection behaviours and their underlying psychological processes, this paper applies a mixed-method approach. First, we conducted a qualitative study to investigate whether known determinants comprehensively cover reasons for both types of consumer rejections. Based on the qualitative study’s evidence, we introduce a new type of innovation resistance (transactional innovation resistance) to complement the well-known concepts of active and passive innovation resistance as drivers of rejection behaviour. Second, we validated the derived framework using a large-scale quantitative study to empirically determine each determinant’s relative importance for temporary and continuous consumer rejection of innovations. Our results demonstrate that consumers who continuously reject an innovation are primarily driven by a combinatory effect of passive and active innovation resistance. In contrast, consumers who only temporarily reject are largely motivated by a combinatory effect of transactional and active innovation resistance.

Teaching Multilingual Students During the COVID-19 Pandemic in Austria: Teachers' Perceptions of Barriers to Distance Learning.

Providing high-quality education for students with emergent proficiency in the language of instruction (referred to here as multilingual students) presents a challenge to inclusion for educational systems the world over. In Austria, a new German language support model was implemented in the school year 2018/19 which provides language support in separate classrooms up to 20 h a week. Since its implementation, the model has been strongly criticized for excluding multilingual students from the mainstream classroom, which is argued to reinforce the educational disadvantages that they face. The study presented here provides unprecedented qualitative insight into how schooling for students within the so-called German language support classes (GLSC) was organized during the COVID-19 pandemic. It builds on results of a previous large-scale quantitative study (n = 3,400 teachers), which was conducted during the first lockdown (spring 2020) and indicated a high risk of exclusion for marginalized students, especially for multilingual students in GLSC. To gain deeper insights into the situation of these students during school closures, 37 teachers who work in these classes at both primary and lower-secondary schools in Vienna were interviewed, of which 18 interviews were considered for analysis. The interviews focus on the situation during the first and second school closures in the city of Vienna. A thematic analysis of the interview data reveals teachers’ perceptions of aspects which harmed or promoted inclusion for students in GLSC during these periods of school closure. Teachers’ perceptions of the most harming factors for students included strong language barriers between teachers and students, restricted access to technical equipment and supportive learning spaces, and low parental engagement. A development that promoted inclusion of these students was the option to allow them to come to school during the second school closure. Since existing studies on the schooling of students during school closures have hardly addressed the situation of students in GLSC, this study contributes to closing this research gap.

A comparison of children's play and non-play behavior before and after schoolyard greening monitored by video observations

This paper investigates the impact of greening schoolyards on children’s (age 7-11) play and non-play behavior during recess. Five primary schools in The Netherlands took part in a longitudinal prospective intervention study using a pre-post design with a baseline and two-year follow-up. At baseline, all schoolyards were paved. Between baseline and follow-up all schools greened their schoolyards. During recess at baseline and follow-up, the play behavior of children in grades 4, 5 and 6 in different target areas was videotaped with multiple cameras and afterwards coded using the cognitive play categories and non-play categories of the Play Observation Scale (Rubin, 2001). Video recordings of 352 children at baseline, and 325 children in 66 target areas at follow-up, were divided in equal time frames of 30 seconds. Each child’s behavior was coded at the 30th second of the time frame, yielding a dataset of 17046 observations. Results show an increase in observed play, as compared to non-play, behavior, after greening. Furthermore, there was an increase in games-with-rules, a small increase in constructive and explorative play behavior, and a decrease in passive non-play behaviors. This impact of greening was stronger for girls compared to boys. These findings strengthen the empirical basis for greening schoolyards by providing data from a large scale quantitative study with a controlled, longitudinal pre-post design.

Push and pull motivations of international voluntary workers on organic farms (the WWOOFers)

ABSTRACT Why do some tourists leave the beaten track and spend time and energy working in remote farms for food and accommodation through the WWOOF (World Wide Opportunities on Organic Farms) network? WWOOF now celebrates its 50th anniversary. This study draws on a Norwegian survey of 1184 WWOOF respondents from 92 countries. It is the first large-scale quantitative study to establish a motivation profile for volunteer tourists, who are members of the WWOOF. The analyses revealed seven primary motivations: Immigration, Escapism, Genuine Norwegian Interest, Sustainable Lifestyle, Cheap Travel, Constraints, and Secondary Image. Pull motivations were stronger than push motivations. The former represents an urge to take on a journey while the latter represents the attraction to a certain goal. Two motivation factors combined push and pull motivations. Six of the factors were interpreted as self-interest and one as altruistic motivation. The results both support some and challenge other studies on this subject.

Understanding Software-2.0

Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using learned models , i.e., Software-2.0 , has gained substantial adoption. However, we do not know what challenges developers encounter when they use ML libraries. With this knowledge gap, researchers miss opportunities to contribute to new research directions, tool builders do not invest resources where automation is most needed, library designers cannot make informed decisions when releasing ML library versions, and developers fail to use common practices when using ML libraries. We present the first large-scale quantitative and qualitative empirical study to shed light on how developers in Software-2.0 use ML libraries, and how this evolution affects their code. Particularly, using static analysis we perform a longitudinal study of 3,340 top-rated open-source projects with 46,110 contributors. To further understand the challenges of ML library evolution, we survey 109 developers who introduce and evolve ML libraries. Using this rich dataset we reveal several novel findings. Among others, we found an increasing trend of using ML libraries: The ratio of new Python projects that use ML libraries increased from 2% in 2013 to 50% in 2018. We identify several usage patterns including the following: (i) 36% of the projects use multiple ML libraries to implement various stages of the ML workflows, (ii) developers update ML libraries more often than the traditional libraries , (iii) strict upgrades are the most popular for ML libraries among other update kinds, (iv) ML library updates often result in cascading library updates, and (v) ML libraries are often downgraded (22.04% of cases). We also observed unique challenges when evolving and maintaining Software-2.0 such as (i) binary incompatibility of trained ML models and (ii) benchmarking ML models. Finally, we present actionable implications of our findings for researchers, tool builders, developers, educators, library vendors, and hardware vendors.

Evolution of Entropy in Art Painting Based on the Wavelet Transform.

Quantitative studies of art and aesthetics are representative of interdisciplinary research. In this work, we conducted a large-scale quantitative study of 36,000 paintings covering both Eastern and Western paintings. The information entropy and wavelet entropy of the images were calculated based on their complexity and energy. Wavelet energy entropy is a feature that can characterize rich information in images, and this is the first study to introduce this feature into aesthetic analysis of art paintings. This study shows that the process of entropy change coincides with the development process of art painting. Further, the experimental results demonstrate an important change in the evolution of art painting, and since the rise of modern art in the twentieth century, the entropy values in painting have started to become diverse. In comparison with Western paintings, Eastern paintings have distinct low entropy characteristics in which the wavelet entropy feature of the images has better results in the machine learning classification task of Eastern and Western paintings (i.e., the F1 score can reach 97%). Our study can be the basis for future quantitative analysis and comparative research in the context of Western and Eastern art aesthetics.

Online Activity and Achievements in Elementary School Mathematics: A Large-Scale Exploration

In a large-scale quantitative study that adopted a learning analytics approach, we searched for associations between students' activity in a game-based online mathematics learning environment and their mathematics achievements on a national standardized test. Students were active in the environment throughout the school year, and particularly during an online National Mathematics Olympiad (N = 239 schools). Overall, our findings point to a positive association between online game-based activity during the Olympiad and mathematics test scores. This association remained even after controlling for socioeconomic factors, which are known to contribute to academic achievements. The paper discusses these findings and suggests evidence for causality, in particular cognitive, meta-cognitive, and affective effects of the online activity that may have positively affected test scores.

Biostimulant impacts of Glutacetine® and derived formulations (VNT1 and VNT4) on the bread wheat grain proteome

Nitrogen (N) fertilizer is essential to ensure grain yield and quality in bread wheat. Improving N use efficiency is therefore crucial for wheat grain protein quality. In the present work, we analysed the effects on the winter wheat grain proteome of biostimulants containing Glutacetine® or two derived formulations (VNT1 and 4) when mixed with urea-ammonium-nitrate fertilizer. A large-scale quantitative proteomics analysis of two wheat flour fractions produced a dataset of 4369 identified proteins. Quantitative analysis revealed 9, 39 and 96 proteins with a significant change in abundance after Glutacetine®, VNT1 and VNT4 treatments, respectively, with a common set of 11 proteins that were affected by two different biostimulants. The major effects impacted proteins involved in (i) protein synthesis regulation (mainly ribosomal and binding proteins), (ii) defence and responses to stresses (including chitin-binding protein, heat shock 70 kDa protein 1 and glutathione S-transferase proteins), (iii) storage functions related to gluten protein alpha-gliadins and starch synthase and (iv) seed development with proteins implicated in protease activity, energy machinery, and the C and N metabolism pathways. Altogether, our study showed that Glutacetine®, VNT1 and VNT4 biostimulants positively affected protein composition related to grain quality.Data are available via ProteomeXchange with identifier PXD021513. SignificanceWe performed a large-scale quantitative proteomics study of the total protein extracts from flour samples to determine the effect of Glutacetine®-based biostimulants treatment on the protein composition of bread wheat grain. To our knowledge, only a few studies in the literature have applied proteomic approaches to study bread wheat grains and in particular to investigate the effect of biostimulants on the grain proteome of this cereal crop. In addition, most approaches used fractional extraction of proteins to target reserve proteins followed electrophoresis which leads to low identification rate of proteins. We identified and quantified a large protein dataset of 4369 proteins and determined ontological class of proteins affected by biostimulants treatments. Our proteomics investigation revealed the important role of these new biostimulants in achieving significant changes in protein synthesis regulation, storage functions, protease activity, energy machinery, C and N metabolism pathways and responses to biotic and abiotic stresses in grain.