This study provides a comprehensive overview of research ethics in science using an approach that combine bibliometric analysis and systematic review. The importance of ethical conduct in scientific research to maintain integrity, credibility, and societal relevance has been highlighted. The findings revealed a growing awareness of ethical issues, as evidenced by the development of numerous guidelines, codes of conduct, and oversight institutions. However, significant challenges persist, including the lack of standardized approaches for detecting misconduct, limited understanding of the factors contributing to unethical behavior, and unclear definitions of ethical violations. To address these issues, this study recommends promoting transparency and data sharing, enhancing education, and training programs, establishing robust mechanisms to identify and address misconduct, and encouraging collaborative research and open science practices. This study emphasizes the need for a collaborative approach to restore public confidence in science, protect its positive impact, and effectively address global challenges, while upholding the principles of social responsibility and justice. This comprehensive approach is crucial for maintaining research credibility, conserving resources, and safeguarding both the research participants and the public.

Advances in the field of multimodal learning research is often accomplished by actively exploring new technologies and techniques related to the collection and analysis of data. However, the exploration of the ethical principles and procedures for governing the usage of the new technologies and techniques is not as eagerly pursued. As multimodal learning data grows in complexity, and to an extent invasiveness, a need is arising to scrutinize the ethical aspects of multimodal learning research. The process of informed consent in multimodal learning research may be an appropriate starting point as the results of bioethical studies suggest that many informed consent processes do not adequately support participant comprehension of the studies they consent to. Studies have shown that enhancing consent forms can improve participant comprehension and contribute to validating the consent received. The following data set include two types of enhanced consent forms: one form is written from a researcher perspective and the other from a participant perspective. Results of the study involving first year undergraduate students suggest that the overall level of comprehension did not differ between conditions. Yet, the participant-oriented consent form had significantly lower rates of enrollment.

The data set is composed by:

Anonymized Data Sheet: participant responses to the Informed Consent Test for Educational Technology and survey questions

Enhanced Consent Forms: Enhanced consent forms for educational technology research for each condition in the study and in both English and Spanish. Form A is written from a research perspective (researcher-oriented) – which is how most forms are written. Form B is written from a participant perspective (participant-oriented).

Ethical Data ManagementExecutive SummaryIn the age of data and information, it is imperative that the City of Virginia Beach strategically utilize its data assets. Through expanding data access, improving quality, maintaining pace with advanced technologies, and strengthening capabilities, IT will ensure that the city remains at the forefront of digital transformation and innovation. The Data and Information Management team works under the purpose:“To promote a data-driven culture at all levels of the decision making process by supporting and enabling business capabilities with relevant and accurate information that can be accessed securely anytime, anywhere, and from any platform.”To fulfill this mission, IT will implement and utilize new and advanced technologies, enhanced data management and infrastructure, and will expand internal capabilities and regional collaboration.Introduction and JustificationThe Information technology (IT) department’s resources are integral features of the social, political and economic welfare of the City of Virginia Beach residents. In regard to local administration, the IT department makes it possible for the Data and Information Management Team to provide the general public with high-quality services, generate and disseminate knowledge, and facilitate growth through improved productivity.For the Data and Information Management Team, it is important to maximize the quality and security of the City’s data; to develop and apply the coherent management of information resources and management policies that aim to keep the general public constantly informed, protect their rights as subjects, improve the productivity, efficiency, effectiveness and public return of its projects and to promote responsible innovation. Furthermore, as technology evolves, it is important for public institutions to manage their information systems in such a way as to identify and minimize the security and privacy risks associated with the new capacities of those systems.The responsible and ethical use of data strategy is part of the City’s Master Technology Plan 2.0 (MTP), which establishes the roadmap designed by improve data and information accessibility, quality, and capabilities throughout the entire City. The strategy is being put into practice in the shape of a plan that involves various programs. Although these programs was specifically conceived as a conceptual framework for achieving a cultural change in terms of the public perception of data, it basically covers all the aspects of the MTP that concern data, and in particular the open-data and data-commons strategies, data-driven projects, with the aim of providing better urban services and interoperability based on metadata schemes and open-data formats, permanent access and data use and reuse, with the minimum possible legal, economic and technological barriers within current legislation.Fundamental valuesThe City of Virginia Beach’s data is a strategic asset and a valuable resource that enables our local government carry out its mission and its programs effectively. Appropriate access to municipal data significantly improves the value of the information and the return on the investment involved in generating it. In accordance with the Master Technology Plan 2.0 and its emphasis on public innovation, the digital economy and empowering city residents, this data-management strategy is based on the following considerations.Within this context, this new management and use of data has to respect and comply with the essential values applicable to data. For the Data and Information Team, these values are:Shared municipal knowledge. Municipal data, in its broadest sense, has a significant social dimension and provides the general public with past, present and future knowledge concerning the government, the city, society, the economy and the environment.The strategic value of data. The team must manage data as a strategic value, with an innovative vision, in order to turn it into an intellectual asset for the organization.Geared towards results. Municipal data is also a means of ensuring the administration’s accountability and transparency, for managing services and investments and for maintaining and improving the performance of the economy, wealth and the general public’s well-being.Data as a common asset. City residents and the common good have to be the central focus of the City of Virginia Beach’s plans and technological platforms. Data is a source of wealth that empowers people who have access to it. Making it possible for city residents to control the data, minimizing the digital gap and preventing discriminatory or unethical practices is the essence of municipal technological sovereignty.Transparency and interoperability. Public institutions must be open, transparent and responsible towards the general public. Promoting openness and interoperability, subject to technical and legal requirements, increases the efficiency of operations, reduces costs, improves services, supports needs and increases public access to valuable municipal information. In this way, it also promotes public participation in government.Reuse and open-source licenses. Making municipal information accessible, usable by everyone by default, without having to ask for prior permission, and analyzable by anyone who wishes to do so can foster entrepreneurship, social and digital innovation, jobs and excellence in scientific research, as well as improving the lives of Virginia Beach residents and making a significant contribution to the city’s stability and prosperity.Quality and security. The city government must take firm steps to ensure and maximize the quality, objectivity, usefulness, integrity and security of municipal information before disclosing it, and maintain processes to effectuate requests for amendments to the publicly-available information.Responsible organization. Adding value to the data and turning it into an asset, with the aim of promoting accountability and citizens’ rights, requires new actions, new integrated procedures, so that the new platforms can grow in an organic, transparent and cross-departmental way. A comprehensive governance strategy makes it possible to promote this revision and avoid redundancies, increased costs, inefficiency and bad practices.Care throughout the data’s life cycle. Paying attention to the management of municipal registers, from when they are created to when they are destroyed or preserved, is an essential part of data management and of promoting public responsibility. Being careful with the data throughout its life cycle combined with activities that ensure continued access to digital materials for as long as necessary, help with the analytic exploitation of the data, but also with the responsible protection of historic municipal government registers and safeguarding the economic and legal rights of the municipal government and the city’s residents.Privacy “by design”. Protecting privacy is of maximum importance. The Data and Information Management Team has to consider and protect individual and collective privacy during the data life cycle, systematically and verifiably, as specified in the general regulation for data protection.Security. Municipal information is a strategic asset subject to risks, and it has to be managed in such a way as to minimize those risks. This includes privacy, data protection, algorithmic discrimination and cybersecurity risks that must be specifically established, promoting ethical and responsible data architecture, techniques for improving privacy and evaluating the social effects. Although security and privacy are two separate, independent fields, they are closely related, and it is essential for the units to take a coordinated approach in order to identify and manage cybersecurity and risks to privacy with applicable requirements and standards.Open Source. It is obligatory for the Data and Information Management Team to maintain its Open Data- Open Source platform. The platform allows citizens to access open data from multiple cities in a central location, regional universities and colleges to foster continuous education, and aids in the development of data analytics skills for citizens. Continuing to uphold the Open Source platform with allow the City to continually offer citizens the ability to provide valuable input on the structure and availability of its data. Strategic areasIn order to deploy the strategy for the responsible and ethical use of data, the following areas of action have been established, which we will detail below, together with the actions and emblematic projects associated with them.In general, the strategy pivots on the following general principals, which form the basis for the strategic areas described in this section.Data sovereigntyOpen data and transparencyThe exchange and reuse of dataPolitical decision-making informed by dataThe life cycle of data and continual or permanent accessData GovernanceData quality and accessibility are crucial for meaningful data analysis, and must be ensured through the implementation of data governance. IT will establish a Data Governance Board, a collaborative organizational capability made up of the city’s data and analytics champions, who will work together to develop policies and practices to treat and use data as a strategic asset.Data governance is the overall management of the availability, usability, integrity and security of data used in the city. Increased data quality will positively impact overall trust in data, resulting in increased use and adoption. The ownership, accessibility, security, and quality, of the data is defined and maintained by the Data Governance Board.To improve operational efficiency, an enterprise-wide data catalog will be created to inventory data and track metadata from various data sources to allow for rapid data asset discovery. Through the data catalog, the city will

A bibliometric analysis of publications on the ethical considerations of sex robots (2003‒2022)

Blockchain has empowered computer systems to be more secure using a distributed network. However, the current blockchain design suffers from fairness issues in transaction ordering. Miners are able to reorder transactions to generate profits, the so-called miner extractable value (MEV). Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. However, previous studies have mostly analyzed blockchain data, which might not capture the impacts of MEV in a much broader AI society. Thus, in this research, we applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags and analyzed their topics. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV. We also identify the co-movements of MEV activities on blockchain and social media platforms. Our study contributes to the literature at the interface of blockchain security, MEV solutions, and AI ethics. (2023-07-06)

The dataset presents the outcomes of a PhD study investigating how municipalities manage the ethical dilemmas arising from the competing interests of multiple stakeholders in governing the shared accommodation industry. Platform enterprises operating in SA have altered how people think about paying for a place to stay, whether for social housing, business or leisure purposes. Some of these changes have had mixed results, leaving municipalities to deal with ethical dilemmas from a management and governance perspective. The inquiry was conducted through a qualitative multiple case study method using the cities of Cape Town and eThekwini municipalities as units of analysis. Semi-structured interviews and observations were the primary techniques for collecting the data from 20 research participants drawn from both municipalities, as well as from external private and public sector and community organisations. The study used the purposeful, snowballing and opportunistic sampling techniques to maximize the opportunity to get more insights from the multiple research participants. Thematic analysis of the qualitative data from semi-structured interviews was used. Following Collis and Hussey (2021), the analysis of data commenced immediately during the transcription process of the interviews. Upon completion of the interviews, the qualitative data underwent content analysis, employing Otter.ai for transcription and identifying response patterns. The first transcriptions of the interviews were then cross-checked with memos and observation notes made by the researcher during the interview phases. Following the feedback, the transcribed interview data was coded and concepts were produced. These concepts were then merged to form categories. The categories and the interpretations of the interviews were triangulated using memos, observation notes, and documents obtained from the two municipalities and organisations such as Airbnb and Tourism Grading Council of South Africa. The researcher adopted the common ways of coding recommended by other qualitative researchers (Myers, 2019; Rashid et al., 2019; Yin, 2018). The adopted procedure involves following a four-step approach for interpreting the research material, viz: preparation, exploration, specification, and integration. The four-step technique provided a more organised and systematic method of interpretation, which proved useful in the presentation of the research data. Once the individual interviews were transcribed with rigorous analysis, the responses to both sets of research questions were extracted and organised to produce into two data summary tables. One data summary table recorded the research participants’ key responses to the primary research questions, separating the responses of the internal research participants (municipal employees) from the external research participants (stakeholders including businesses and community organisations). In the same manner, the second data summary table recorded the research participants’ key responses to the secondary research questions. These data summary tables included the research participants’ recommendations for improved governance for both municipalities. A separate consolidated data summary table was developed to capture the data of the research participants with a national footprint including their recommendations. The dataset include the customised "Interview questionnaire" that were used in interviewing the two categories of research participants in each municipaity; and a third “Interview Questionnaire” for the research participants with a national footprint.

IntroductionDigital mental health care system includes the interventions delivered via digital technologies, such as mobile apps, websites, or virtual reality (VR). A recent upsurge in the digital mental healthcare care services has been observed in the last 5 years. With its core advantage of reaching the unreached, wider coverage, cost and time effectivity, all eyes are on the digital mental health care system. It is definitely a mechanism to cater rising prevalence of mental health concern, stigma towards mental health, accessibility and cost and uplift the psychological wellbeing. Success of the digital mental health care system has been researched world-wide. However, the same is not unaffected by the ethical concerns.MethodsThis study aims to perform a comprehensive bibliometric analysis of scholarly articles on ethical concerns and dilemmas of digital mental health care by utilizing data extracted from the Scopus database from 2000 to 2024 by analysing 123 research articles. Statistical descriptive analysis in combination with performance analysis and co-word analysis was used to understand the research trends, leading countries and country collaborations studying ethical concerns related to digital mental healthcare.Result and discussionThe first publication appeared in 2000 with zero research till the year 2005. In this decade till 2010 we can observe only 4 publications. Consistent publishing started trending upward through 2018, observing the largest increase during pandemic in 2020 and onwards constituting 100 publications. The United States of America is the leading country studying ethical dilemmas in Digital Mental healthcare, with 42 papers followed by United Kingdom with 23 publications. The most influential peace of research with 490 citations is article co-authored by Barak et al. (2009), which is defining internet-supported therapeutic interventions and related concerns. BMJ Open is noted as the leading journal which is publishing issues concerning Digital Mental Healthcare with 18 publications, followed by Frontiers in Psychiatry and JMIR Mental Health. Analyses reflects that the top cited articles on Digital Mental healthcare are specifically directed on bringing out some of the key concerns of data privacy, emergency response, therapist competency and consent which requires appropriate handling Otherwise they may be cause of distress to client and question the trustworthiness of the Digital Mental Health Care system.ConclusionThe concerns brought out through this bibliometric analysis could be important guiding principles for online mental health services. Alongside, mental health professionals operating online must have orientation on the ethical concerns surrounding online mental healthcare.

This research on governance challenges employed qualitative data collected through 10 semi-structured interviews with experts who were either directly involved in or indirectly connected to the TradeLens project. The participants included administrative officials, stakeholders, and researchers possessing firsthand knowledge or significant insight into the governance dynamics of TradeLens. The interviews explored key themes such as transparency, participatory decision-making, and resource management, following a framework grounded in the existing literature. All interviews were conducted either in person or via online platforms, and explicit consent was obtained from each participant prior to recording. Participants were informed about the purpose of the study, the voluntary nature of their involvement, and confidentiality measures in place. The collected data were analyzed using NVivo software to ensure systematic coding and thematic analysis. Ethical considerations were rigorously observed, with approval granted by the institutional ethics committee, ensuring that the study adhered to ethical standards and safeguarded participant confidentiality. This methodological approach provided deep and nuanced insights into the governance challenges encountered within the TradeLens ecosystem.

Survey instrument and anonymised responses collected as part of Sub-Project B4 “Provenance of Social Media” of the larger Social Media - Developing Understanding, Infrastructure & Engagement (Social Media Enhancement) award (ES/M001628/1). The survey aimed to further our understanding of the current practices and attitudes towards the provenance of data collected from social media platforms and its analysis by researchers in the social sciences. This includes all forms of social media, such as Twitter, Facebook, Wikipedia, Quora, blogs, discussion forums, etc. The survey was conducted as an online-survey using Google Forms. Findings from this survey influenced the work of the sub-project, and the development of tools to support researchers who wish to increase the transparency of their research using social media data.

Dataset of collected survey responses, and pdf versions of the Google Forms online survey instrument. Each PDF file denotes one possible survey path that depended on the response of a participant to the question “What level of experience do you have using data from a social media platforms as part of your research?” The three paths are:

(1) SurveyInstrument-Path-1.pdf - is used if the participant selected the option "I have used/am currently using social media data as part of my research."

(2) SurveyInstrument-Path-2.pdf - is used if the participant selected the option "I am aware of others using social media data as part of their research and may consider using it within mine."

(3) SurveyInstrument-Path-3.pdf - is used if the participant selected the option "Neither of the above."

There is now a broad consensus that new forms of social data emerging from people’s day-to-day activities on the web have the potential to transform the social sciences. However, there is also agreement that current analytical techniques fall short of the methodological standards required for academic research and policymaking and that conclusions drawn from social media data have much greater utility when combined with results drawn from other datasets (including various public sector resources made available through open data initiatives). In this proposal we outline the case for further investigations into the challenges surrounding social media data and the social sciences. Aspects of the work will involve analysis of social media data in a number of contexts, including: -transport disruption around the 2014 Commonwealth Games (Glasgow) - news stories about Scottish independence and UK-EU relations - island communities in the Western Isles. Guided by insights from these case studies we will: - develop a suite of software tools to support various aspects of data analysis and curation; - provide guidance on ethical considerations surrounding analysis of social media data; - deliver training workshops for social science researchers; - engage with the public on this important topic through a series of festivals (food, music, science).

Categories of “trajectory” describing the faith of all proposals applied under the MSCA H2020 during 2014–2020 in regard to ethics.

20 transcribed and anonymised interviews with military personnel returning from the military-run Ebola treatment centre , Kerry Town, Sierra Leone between March-July 2015. The sample included three broad groups: doctors (7), nurses and healthcare assistants (6), and others such as personal protective equipment monitors, lab technicians and mortuary attendants (7). Interviews explored the ethical challenges participants felt that they had encounters prior to deployment, during deployment and on return from deployment.

There is a major Ebola epidemic affecting parts of West Africa. Ebola is a highly infectious disease that carries a significant risk of death. New therapies and potential vaccines that can be distributed to the affected populations are being developed. Stricken communities have appealed for help. One response from the UK government has been to deploy UK military healthcare personnel to Sierra Leone (operation Gritrock), initially to provide a small facility for affected healthcare workers and to assist with training of local healthcare workers. It is possible that the scope of this involvement will increase, and prudent planning is in place for further deployments. This is the first major, purely humanitarian military deployment since Rwanda (1994). It is known that civilian humanitarian healthcare workers experience complex ethical tensions when deployed as expatriates. Military healthcare workers face both related and different (uniquely military) challenges when deployed in conflict scenarios but it is not known how they will experience the novel ethical challenges and complexities in a purely humanitarian setting, dealing with a highly infectious disease in conditions of near disaster for the affected communities. This project aims to collect interview data on the ethical challenges experienced by the deployed UK military healthcare personnel. It plans to recruit up to 25 nurses, doctors, and allied health professionals. An initial analysis of the resulting data will enable training materials to be developed quickly to benefit those, including civilians, about to deploy to Ebola-affected regions. These materials will be evaluated by a subset of the participants and used to inform, train and support existing and future (military and civilian) deployments during the Ebola outbreak. The data collected will also be used in the longer term to expand and enrich existing understanding of the ethical experiences of expatriate healthcare workers volunteering for humanitarian work in other contexts, for instance working with non-governmental organisations or as part of governmental responses. It is predicted that the UK medical military will increasingly be expected to contribute to similar humanitarian responses in the future. This work will also contribute to military preparation, training, support and policy in other humanitarian contexts.

This study aims to investigate how ethical leadership can influence work engagement and workaholism through the potential moderating effect of self-efficacy. There have been debates on the similarities, their negative correlation, and differences between these two work outcomes. To show one new aspect of evidence regarding the debate, we chose ethical leadership as the common antecedent of the outcomes and analyzed the relationships while considering a boundary condition, self-efficacy. For this purpose, using an online questionnaire, we collected primary data from 80 graduate students from a university in Indonesia. An experimental research design was applied, and we used t-test and hierarchical regression analysis to confirm the relationship mentioned above. Results indicate that ethical leadership has a positive effect on work engagement, while it has an insignificant effect on workaholism. Moreover, self-efficacy did not moderate the relationships between ethical leadership and work engagement, or ethical leadership and workaholism. One novelty of the present study is the finding of different consequences of the two “similar” work outcomes from ethical leadership. Implications, limitations, and direction for future research are also discussed.

Transcripts of in-depth interviews and group discussions with managers, researchers, ethics committee members, field data collectors and community members on the issues around ethical data sharing in the context of research involving women and children in urban India. We interviewed researchers, managers, and research participants associated with a Mumbai non-governmental organization, as well as researchers from other organizations and members of ethics committees. We conducted 22 individual semi-structured interviews and involved 44 research participants in focus group discussions. We used framework analysis to examine ideas about data and data sharing in general; its potential benefits or harms, barriers, obligations, and governance; and the requirements for consent. Both researchers and participants were generally in favor of data sharing, although limited experience amplified their reservations.

It is increasingly recognized that effective and appropriate data sharing requires the development of models of good data sharing practice capable of taking seriously both the potential benefits to be gained and the importance of ensuring that the rights and interests of participants are respected and that risk of harms is minimized. Calls for the greater sharing of individual level data from biomedical and public health research are receiving support among researchers and research funders. Despite its potential importance, data sharing presents important ethical, social, and institutional challenges in low income settings. This dataset comprises qualitative research conducted in India, exploring the experiences of key research stakeholders and their views about what constitutes good data sharing practice.

The Data Analytics Outsourcing Marketsize was valued at USD 12.12 USD Billion in 2023 and is projected to reach USD 101.12 USD Billion by 2032, exhibiting a CAGR of 35.4 % during the forecast period.Outsourcing data analysis benefits businesses due to its ability to suprisingly improve their decision-making and reduce costs in addition to easy access to advanced technologies like artificial intelligence (AI) and machine learning (ML). Tailored industry-specific solutions are developed to tackle these unique problems, while scalability is a core principle facilitating the processing of various volumes of data. Through this approach, entities will have the chance to adopt the strengths of the outsourcing, thus increasing productivity as well as offering innovations. Data Security and Compliance must be the basis among partner providers that follow stringent standards. The principle of integration of existing technological processes leads to smooth business processes with the aid of tech-driven strategies. A number of trends can be pointed out that are fostering the spread of these technologies: cloud analytics use, predictive and prescriptive analytics, personalized customer experience and data governance/ethics-related issues . Eventually, the data analysis outsourcing is absolutely significant as it helps to capture the hidden knowledge of data, and to promote growth and maintain competitiveness of different sectors. Recent developments include: November 2023 – Accenture and Salesforce work together to help life sciences companies differentiate themselves with data and AI. This will help to accelerate the deployment of data and analytics capabilities and support decision-making and operations., October 2023 – Krungsri announced a five-year partnership with IT infrastructure service provider Kyndryl. Through the implementation of data analytics, cloud solutions, and automation, Kyndryl strengthens banks' ability to adapt to market changes, enhance traditional systems, and improve customer-focused digital banking services., June 2023 – Microsoft and Moody's Corporation announced a new strategic partnership to deliver advanced data, analytics, research, collaboration, and risk solutions to financial services. The partnership is built on Moody's robust data and analytics capabilities and the power and scale of Microsoft's Azure OpenAI service, leveraging Microsoft AI to deliver Moody's proprietary data and analytics., May 2023 – Capgemini and Google Cloud expanded their strategic partnership in data analytics and artificial intelligence (AI). The partnership launched a new platform for generative AI to assist enterprises in realizing the full potential of Exploit AI and created a global Google Cloud (CoE) technology., January 2022 – Fractal acquired Neal Analytics, a Microsoft Gold cloud, data, engineering, and AI consulting partner. Neal Analytics will enhance Fractal's AI engineering capabilities and cloud-first offerings across Microsoft's multi-cloud ecosystem, enabling customers to scale AI and decision-making. It will also strengthen Fractal's presence in the Pacific Northwest, Canada, and India.. Key drivers for this market are: Increased Volume of Digital Data Production to Augment Market Growth. Potential restraints include: Data Storage and Privacy Concerns May Stifle Market Growth . Notable trends are: Businesses Adopting Data Analytics Outsourcing is recognized as a Significant Trend.

This research examines internet collective behavior in mainland China during the COVID-19 pandemic, focusing on the factors and characteristics that drive such behavior. The Chinese government initially implemented a conservative and biased policy to contain the spread of the virus, but the sudden lifting of lockdown measures in late 2022 resulted in a surge in infections and scarcity of medical resources. This policy shift led many Chinese internet users to perceive the government’s actions as hasty and harsh, prompting them to engage in collective online behavior. The study employed a survey-based approach, collecting 1,626 valid questionnaires, which underwent reliability testing, descriptive statistical analysis, and a difference-in-differences test. A structural equation model (SEM) was then constructed and applied to comprehensively analyze the mediating and moderating effects of latent variables. Ethical considerations were prioritized, with informed consent obtained from all participants, who were provided with detailed information about the study and given sufficient time to review and ask questions. The research yielded three primary conclusions: the Chinese public demonstrated a perception of fairness and exhibited obedience, respect, and cooperation with the government during the epidemic; the observed online collective behavior can be characterized as a moderate and rational form of resistance, explained by the elaborated social identity model (ESIM); and the middle class consistently adopted a self-vulnerability strategy, positioning themselves as beneficiaries of protection to maximize their own interests in epidemic prevention and control. This study shows notable insights into internet collective behavior in mainland China during the COVID-19 pandemic, highlighting perceptions, resistance, and strategies adopted by different segments of the population.

The global crowd analytics market is experiencing robust growth, projected to reach $1.55 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 32.14% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of cloud-based solutions for data processing and analysis is significantly reducing infrastructure costs and improving accessibility for businesses of all sizes. Furthermore, the rising demand for real-time insights in sectors like transportation and retail, driven by the need for improved operational efficiency, security, and customer experience, is a major catalyst. Advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the accuracy and capabilities of crowd analytics platforms, leading to more effective crowd management and predictive analytics. The integration of various data sources, including video surveillance, social media, and mobile sensor data, provides a more comprehensive understanding of crowd behavior, furthering market growth. However, challenges remain. Data privacy concerns and the ethical implications of using crowd analytics technologies necessitate robust regulatory frameworks and transparent data handling practices. The high initial investment costs associated with implementing sophisticated crowd analytics systems can be a barrier to entry for smaller businesses. Furthermore, the complexity of integrating and analyzing diverse data streams from various sources presents a technical hurdle for many organizations. Despite these challenges, the long-term prospects for the crowd analytics market remain exceptionally positive, driven by continuous technological innovation and the increasing need for intelligent crowd management solutions across a variety of sectors. The market is segmented by deployment (cloud and on-premises) and end-user (transportation, retail, and others), offering diverse opportunities for vendors specializing in specific applications and technological solutions. North America and Europe are currently the leading regions, but the Asia-Pacific region is expected to witness significant growth in the coming years due to rising urbanization and technological adoption.

IntroductionClinical research with remote monitoring technologies (RMTs) has multiple advantages over standard paper-pencil tests, but also raises several ethical concerns. While several studies have addressed the issue of governance of big data in clinical research from the legal or ethical perspectives, the viewpoint of local research ethics committee (REC) members is underrepresented in the current literature. The aim of this study is therefore to find which specific ethical challenges are raised by RECs in the context of a large European study on remote monitoring in all syndromic stages of Alzheimer’s disease, and what gaps remain.MethodsDocuments describing the REC review process at 10 sites in 9 European countries from the project Remote Assessment of Disease and Relapse–Alzheimer’s Disease (RADAR-AD) were collected and translated. Main themes emerging in the documents were identified using a qualitative analysis approach.ResultsFour main themes emerged after analysis: data management, participant’s wellbeing, methodological issues, and the issue of defining the regulatory category of RMTs. Review processes differed across sites: process duration varied from 71 to 423 days, some RECs did not raise any issues, whereas others raised up to 35 concerns, and the approval of a data protection officer was needed in half of the sites.DiscussionThe differences in the ethics review process of the same study protocol across different local settings suggest that a multi-site study would benefit from a harmonization in research ethics governance processes. More specifically, some best practices could be included in ethical reviews across institutional and national contexts, such as the opinion of an institutional data protection officer, patient advisory board reviews of the protocol and plans for how ethical reflection is embedded within the study.

This dataset is rooted in a study aimed at unveiling the origins and motivations behind the creation of malware repositories on GitHub. Our research embarks on an innovative journey to dissect the profiles and intentions of GitHub users who have been involved in this dubious activity.

Employing a robust methodology, we meticulously identified 14,000 GitHub users linked to malware repositories. By leveraging advanced large language model (LLM) analytics, we classified these individuals into distinct categories based on their perceived intent: 3,339 were deemed Malicious, 3,354 Likely Malicious, and 7,574 Benign, offering a nuanced perspective on the community behind these repositories.

Our analysis penetrates the veil of anonymity and obscurity often associated with these GitHub profiles, revealing stark contrasts in their characteristics. Malicious authors were found to typically possess sparse profiles focused on nefarious activities, while Benign authors presented well-rounded profiles, actively contributing to cybersecurity education and research. Those labeled as Likely Malicious exhibited a spectrum of engagement levels, underlining the complexity and diversity within this digital ecosystem.

We are offering two datasets in this paper. First, a list of malware repositories - we have collected and extended the malware repositories on the GitHub in 2022 following the original papers. Second, a csv file with the github users information with their maliciousness classfication label.

malware_repos.txt

Purpose: This file contains a curated list of GitHub repositories identified as containing malware. These repositories were identified following the methodology outlined in the research paper "SourceFinder: Finding Malware Source-Code from Publicly Available Repositories in GitHub."

Contents: The file is structured as a simple text file, with each line representing a unique repository in the format username/reponame. This format allows for easy identification and access to each repository on GitHub for further analysis or review.

Usage: The list serves as a critical resource for researchers and cybersecurity professionals interested in studying malware, understanding its distribution on platforms like GitHub, or developing defense mechanisms against such malicious content.

obfuscated_github_user_dataset.csv

Purpose: Accompanying the list of malware repositories, this CSV file contains detailed, albeit obfuscated, profile information of the GitHub users who authored these repositories. The obfuscation process has been applied to protect user privacy and comply with ethical standards, especially given the sensitive nature of associating individuals with potentially malicious activities.

Contents: The dataset includes several columns representing different aspects of user profiles, such as obfuscated identifiers (e.g., ID, login, name), contact information (e.g., email, blog), and GitHub-specific metrics (e.g., followers count, number of public repositories). Notably, sensitive information has been masked or replaced with generic placeholders to prevent user identification.

Usage: This dataset can be instrumental for researchers analyzing behaviors, patterns, or characteristics of users involved in creating malware repositories on GitHub. It provides a basis for statistical analysis, trend identification, or the development of predictive models, all while upholding the necessary ethical considerations.

READ_ME_DigitalBrainSwitch_SupportingInformation.txtDigital Brain Switch (DBS; May 2013 – August 2015) was a project funded by the Engineering and Physical Sciences Research Council (EPSRC), which brought together computer science and social science. It was a collaboration between Lancaster University; The Open University; Royal Holloway, University of London; and University of Kent.Data originates from a two year project examining the implications of digital technologies for work-life boundary management. Sample were 15 university students, 15 social entrepreneurs and 15 office workers across a range of institutions, organisations and contexts. Participants were recruited from a targeted invitation and all participated on a voluntary basis. Data consist of 45 week long video diaries consisting of instances of role transitions, textual transcription of simultaneous commentary and textual transcription of debriefing sessions. Additionally 45 hour long interview recordings and textual transcripts of further discussions of work-life issues, specifically work-life boundaries, work-life transitions and the effects of keeping video diaries. Data were collected through hand held video cameras and recorded semi-structured interviewsDataset contains sensitive personal information - restricted to project team onlyThis data set contains the project's supporting information, in the following zip files:1. 'Analysis notes.zip' - Notes on the analysis framework2. 'Briefing participants documents.zip' - Documents to brief the participants on what the project was about and what is asked of them3. 'Consent Forms.zip' - Participant consent forms outlining what they were consenting to and how their data would be used4. 'Data Management.zip' - Data management plan outlining how and where data will be stored5. 'Ethics.zip' - Ethics approval documents6. 'Recruitment Documents.zip' - Documents used to promote the project and recruit participants7. 'READ_ME_DigitalBrainSwitch_SupportingInformation.txt' - a text file containing this description.The project's research data is also archived on figshare at: 10.21954/ou.rd.5531911Pilot study data is also archived on figshare at: 10.21954/ou.rd.5549818Data record in Open Research Online: http://oro.open.ac.uk/46687/Project website: http://www.scc.lancs.ac.uk/research/projects/DBS/

According to Cognitive Market Research, the global Open-Source Intelligence - OSINT Market size will be USD 9124.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 25.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 3649.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 2737.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 2098.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 27.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 456.23 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 182.49 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.9% from 2024 to 2031.
The Social Media Analytics category is the fastest growing segment of the Open-Source Intelligence industry

Market Dynamics of Open-Source Intelligence - OSINT Market

Key Drivers for Open-Source Intelligence - OSINT Market

Growth of Open Data from Social Media, Government, and Online Sources Boosting the OSINT Market

The expanding availability of open data from sources like social media, government publications, and online forums is a significant driver of the Open-Source Intelligence (OSINT) market. As more public data becomes accessible, organizations can leverage it for various intelligence-gathering purposes, such as threat detection, market analysis, and competitive intelligence. This wealth of open data allows businesses and security agencies to enhance decision-making, monitor trends, and predict risks. The ease of access to diverse, real-time data fuels the demand for OSINT tools that can efficiently process and analyze vast amounts of information.

Rising Cybersecurity Threats Drive Increased Demand for OSINT Solutions to Proactively Detect and Mitigate Risks

Increasing cyber threats and attacks globally have heightened the demand for advanced intelligence tools like OSINT to prevent and mitigate potential risks. OSINT solutions help organizations identify vulnerabilities, monitor suspicious activities, and track emerging threats in real-time, especially in sectors such as finance, healthcare, and government. The growing frequency and sophistication of cyberattacks create a strong need for proactive defence mechanisms. OSINT provides essential insights to detect threats early, ensuring robust cybersecurity measures are in place. As a result, the OSINT market continues to grow in response to these escalating cybersecurity concerns.

Restraint Factor for the Open-Source Intelligence - OSINT Market

Data Privacy and Ethical Concerns Impacting the Growth of the Open-Source Intelligence Market

Despite the growing use of OSINT, concerns surrounding data privacy and ethical issues pose a restraint to the market. The collection and analysis of publicly available data may raise questions about the boundaries of personal privacy, especially when social media and other public platforms are involved. Organizations using OSINT tools must navigate legal regulations and ethical considerations regarding data usage, especially in jurisdictions with strict privacy laws. These concerns may limit the adoption of OSINT solutions in certain sectors and regions, creating challenges for market growth as businesses strive to balance intelligence gathering with privacy compliance.

Impact of Covid-19 on the Open-Source Intelligence - OSINT Market

The COVID-19 pandemic significantly impacted the Open-Source Intelligence (OSINT) market by accelerating the demand for real-time intelligence and data analysis. With increased reliance on digital platforms during lockdowns, the volume of publicly available data surged, presenting new opportunities for OSINT solutions. Businesses, governments, and security agencies turned to OSINT for monitoring emerging threats, tracking public sentiment, and analyzing global trends. However, the pandemic also heightened concerns about data privacy and the ethical implications of mass data collection. Des...