Digital Data Sources Research Articles

Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models.

Artificial intelligence (AI) and machine learning (ML) are anticipated to transform the practice of medicine. As one of the largest sources of digital data in healthcare, laboratory results can strongly influence AI and ML algorithms that require large sets of healthcare data for training. Embedded bias introduced into AI and ML models not only has disastrous consequences for quality of care but also may perpetuate and exacerbate health disparities. The lack of test harmonization, which is defined as the ability to produce comparable results and the same interpretation irrespective of the method or instrument platform used to produce the result, may introduce aggregation bias into algorithms with potential adverse outcomes for patients. Limited interoperability of laboratory results at the technical, syntactic, semantic, and organizational levels is a source of embedded bias that limits the accuracy and generalizability of algorithmic models. Population-specific issues, such as inadequate representation in clinical trials and inaccurate race attribution, not only affect the interpretation of laboratory results but also may perpetuate erroneous conclusions based on AI and ML models in the healthcare literature.

On the up and up: the job mobility of skilled return migrants

Abstract This paper examines the labor market incorporation of skilled Indian migrants returning home after working in the United States. I analyze a novel dataset of 7,177 time-varying transnational employment histories from LinkedIn using logistic regression and survival analysis. I find that skilled Indian migrants experience occupational upgrades when re-entering their home labor market, buoyed by foreign human capital and compensating for US legal status restrictions. While immigrants often face initial occupational downgrades and job mobility constraints in foreign labor markets, return migrants experience stability, promotions and accelerated rates of job mobility at home. The paper contributes to the literature on return migration, a significant but understudied dimension of international migration due to data constraints. I offer LinkedIn employment histories as digital data source to address these limitations and leverage their rich spatio-temporal information. These data help expand prior research on return migrant wages to broader questions about transnational migrant career trajectories. The findings shed light into where high-demand migrants might maximize their career growth, and have implications for understanding the policies shaping the recruitment and retention of skilled workers in the global labor market.

Digital Competencies and Training Approaches to Enhance the Capacity of Practitioners to Support the Digital Transformation of Public Health: Rapid Review of Current Recommendations.

The COVID-19 pandemic highlighted gaps in the public health workforce's capacity to deploy digital technologies while upholding ethical, social justice, and health equity principles. Existing public health competency frameworks have not been updated to reflect the prominent role digital technologies play in contemporary public health, and public health training institutions are seeking to integrate digital technologies in their curricula. As a first step in a multiphase study exploring recommendations for updates to public health competency frameworks within the Canadian public health context, we conducted a rapid review of literature aiming to identify recommendations for digital competencies, training approaches, and inter- or transdisciplinary partnerships that can enhance public health practitioners' capacity to support the digital transformation of public health. Following the World Health Organization's (2017) guidelines for rapid reviews, a systematic search was conducted on Ovid MEDLINE, Ovid Embase, ERIC (Education Resources Information Center), and Web of Science for peer-reviewed articles. We also searched Google Scholar and various public health agency and public health association websites for gray literature using search terms related to public health, digital health, practice competencies, and training approaches. We included articles with explicit practice competencies and training recommendations related to digital technologies among public health practitioners published between January 2010 and December 2022. We excluded articles describing these concepts in passing or from a solely clinical perspective. Our search returned 2023 titles and abstracts, of which only 12 studies met the inclusion criteria. We found recommendations for new competencies to enable public health practitioners to appropriately use digital technologies that cut across all existing categories of the core competencies for public health framework of the Public Health Agency of Canada. We also identified a new competency category related to data, data systems management, and governance. Training approaches identified include adapted degree-awarding programs like combined public health and informatics or data science degree programs and ongoing professional certifications with integration of practice-based learning in multi- and interdisciplinary training. Disciplines suggested as important to facilitate practice competency and training recommendations included public health, public health informatics, data, information and computer sciences, biostatistics, health communication, and business. Despite the growth of digital technologies in public health, recommendations about practice competencies and training approaches necessary to effectively support the digital transformation of public health remain limited in the literature. Where available, evidence suggests the workforce requires new competencies that cut across and extend existing public health competencies, including new competencies related to the use and protection of new digital data sources, alongside facilitating health communication and promotion functions using digital media. Recommendations also emphasize the need for training approaches that focus on interdisciplinarity through adapted degree-awarding public health training programs and ongoing professional development.

Systematic Visual Documentation of the Mah Meri Tribe's Cultural Practices

The purpose of photography is identical to that of language and writing. Photography's operation must be evaluated in light of various explanatory, interpretative, and theoretical linguistic assertions due to visual art's multifaceted and multifunctional nature. In documentary photography, people, situations, objects, and events are depicted straightforwardly and narratively; This paper first gives a brief overview of the history and facts related to the beliefs and culture of the Mah Meri tribe in Peninsular Malaysia in photo documentary reporting. The primary purpose of this research is to conduct a thorough analysis of cultural traditions and environmentalist ideals. Accordingly, Mah Meri requires systematic and organized visual data, and the collection of qualitative data is essential. In a phased study, ethnomethodological techniques were used to collect periodic primary data. Throughout this systematic documentation procedure, more time was needed to collect in-depth information about personal lifestyles and religious practices. In conclusion, this research contributes to the modern accessibility of digital data sources by achieving an understanding of the Mah Meri tribe through the discovery and deconstruction of traditional beliefs unknown to the public.

Spatiotemporal patterns of public attention to invasive species across an invasion front: a case study of lionfish (Pterois miles) from the Mediterranean Sea

Understanding societal interest in invasive species is crucial as greater public attention can support the success of conservation efforts. One of the main challenges in monitoring societal interest to support conservation is the absence of near-real-time indicators to track large-scale spatiotemporal dynamics of public attention. However, the digital revolution has opened up new opportunities to develop such indicators in support of invasive species research and management. Here, we aim to evaluate public awareness and interest in the invasive lionfish (Pterois miles) in the Mediterranean Sea and investigate spatiotemporal patterns of public interest in the species along its invasion front by using Google search volumes as a proxy for public attention. We implemented topic searches for the lionfish to download Google search volumes from 2013 to 2022 and used these data to test two hypotheses: (1) countries experiencing lionfish invasion exhibit higher public attention than those without lionfish, and (2) in invaded countries, public attention peaks around the arrival year, followed by a decline to baseline levels. Our study confirmed the first hypothesis, showing higher public attention in invaded countries. However, temporal patterns of attention did not fully align with the year of lionfish arrival, suggesting that the socio-political complexity of the region (e.g., cultural trends or internet accessibility), may be a key aspect to consider in future studies. Overall our study shows that digital data sources provide a unique opportunity to monitor social aspects of biological invasions with low associated costs and quick access to a great amount of data generated by internet users.

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

BackgroundHealthcare associated infections (HAI) pose a major threat to healthcare systems resulting in an increased burden of disease. Surveillance plays a key role in rapidly identifying these infections and preventing further transmissions. Alas, in German hospitals, the majority of surveillance efforts have been heavily relying on labour intensive processes like manual chart review. In order to be able to identify further starting points for future digital tools and interventions to aid the surveillance of HAI we aimed to gain an understanding of the current state of digitalisation in the context of the general surveillance organisation in German clinics across all care-levels. The end user perspective of infection prevention and control (IPC) professionals was chosen to identify digital interventions that have the biggest impact on the daily surveillance work routines of IPC professionals. Perceived impediments in the advancement of surveillance digitalisation should be explored.MethodsFollowing the development of an interview guideline, eight IPC professionals from seven German hospitals of different care levels were questioned in semi- structured interviews between December 2022 and January 2023. These included questions about general surveillance organisation, access to digital data sources, software to aid the surveillance process as well as current issues in the surveillance process and implementation of software systems. Subsequently, after full transcription, the interview sections were categorized in code categories (first deductive then inductive coding) and analysed qualitatively.ResultsResults were characterised by high heterogeneity in terms of general surveillance organisation and access to digital data sources. Software configuration of hospital and laboratory information systems (HIS/LIS) as well as patient data management systems (PDMS) varied not only between hospitals of different care levels but also between hospitals of the same care level. Outside research projects, neither fully automatic software nor solutions utilising artificial intelligence have currently been implemented in clinical routine in any of the hospitals.ConclusionsAccess to digital data sources and software is increasingly available to aid surveillance of HAI. Nevertheless, surveillance processes in hospitals analysed in this study still heavily rely on manual processes. In the analysed hospitals, there is an implementation and funding gap of (semi-) automatic surveillance solutions in clinical practice, especially in healthcare facilities of lower care levels.

Digital educational content compilation sources

The development of digitalization of higher education leads to the emergence of new methods and means of learning. At present, information technologies are increasingly used, including distance learning, digital sources are used to create educational content, which opens new prospects for increasing motivation to study disciplines, activation of students’ activity in the learning process and, in general, for the professional success of university graduates. The article analyzes digital sources of spatial educational content compilation in mastering ecological and environmental educational disciplines and materials (by the example of bachelor’s training in the direction 21.03.02 Land management and cadastres). It is important for future specialists in the field of land management and cadastre to be able to use a variety of digital sources of data on the current state of land resources in their daily activities. The information sources in the form of spatial positioning public services, interactive topographic, geological and hydrological map, the map of dangerous exogenous geological processes manifestation, the map of territory functional zoning, the website of the Russian Federation hydrometeorological service management are shown and verified. It is important to use up-to-date geo-ecological and land-cadastral information when studying natural science disciplines, it is inadmissible to study outdated literary materials, inaccurate and unreliable data from electronic sources.

Challenges in access, representativeness, and bias in smart financial data relating to income volatility and economic insecurity.

Introduction & BackgroundFinancial transaction data are highly valuable sources of digital footprints data for behavioural and economic research, but to properly create impact we must closely consider their limitations. Financial institutions hold a wealth of consumer data with untapped potential for community intelligence. These datasets combine excellent coverage with extremely granular information on consumer finances, income and spending, yet these institutions face great challenges in leveraging this data for social good. Smart Data Foundry is a university-owned, non-profit organisation that facilitates safe access to these datasets for researchers and provides insights to enable government bodies to tackle today's major challenges including the cost-of-living crisis and climate change. Objectives & ApproachWe will explore the opportunities afforded by these datasets for social and economic research. For example, using pseudonymised individual consumer banking data from NatWest Group, we have developed metrics for understanding income volatility and economic insecurity in collaboration with the Joseph Rowntree Foundation. We can also use these data to study consumer spending patterns and responses to economic changes such as interest rate rises and the net zero transition. We will assess the limitations of the data including issues of representativeness, bias, and missing data, and describe methods and mitigations to account for these challenges. We also discuss the barriers to accessing this type of data, in both relationship development with data partners, and privacy and governance concerns. Relevance to Digital FootprintsIndividual level customer transaction data provides a rich and novel form of digital footprint for behavioural and economic analyses. Every point of income or expenditure is recorded in a uniquely valuable digital footprint by financial institutions. These can provide a variety of insights, such as responses to macroeconomic shocks across demographic sets, emerging areas of financial distress, and help us better understand the drivers and risks of financial vulnerability. In both its aggregated and individual form, the data can provide an additional layer of understanding for trends we may see in other data, such as health or administrative data. Conclusions & ImplicationsHaving addressed the challenges of data access and data quality, we demonstrate that consumer banking data is an incredibly valuable form of digital footprints data, capturing key information on consumer behaviour. We conclude with a call for further research to develop use cases of this data for social good.

Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data

Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can support the teaching of data science and computational thinking. Research is needed about the design of tasks that support high school statistics teachers to learn new statistical and computational approaches such as digital image analysis and classification models. Using our design framework, the construction of a task is described that introduces classification modeling using grayscale digital images. The task was implemented within a teaching experiment involving six high school statistics teachers. Our findings from this exploratory study indicated that the task design seemed to support statistical and computational thinking practices related to classification modeling and digital image data.

Mapping Blue Economy Potential Using Spatial Statistical Downscaling Model: Analysis of the Impact of Climate Change on Freshwater Fish Resources

Blue Economy is a sustainable economic concept that focuses on utilizing economic resources in marine, coastal and land ecosystems. Sustainable use of freshwater resources in the blue economy for inland waters supports economic growth with environmental balance. The Statistical Downscaling method is used to understand the impact of climate change on freshwater fish resources. To carry out mapping of the potential of the blue economy, it is carried out by statistical downscaling modeling with satellite variables with Integrated Nested Laplace Approximation parameter estimates. The response variable is satellite variables in the form of average rainfall. The modeling results show that Kalipuro District has the highest blue economy potential, while Kalibaru has the lowest. From the research results, that satellite data on average rainfall is a strong basis for printing statistical downscaling, increasing efficiency with open source digital data sources. Satellite data integration, maximizing analysis and comprehensive blue economy potential efficiency.

Seeding innovation: the role of internal and external digital data in agri-food product innovation

PurposeSince previous literature provides fragmented and conflicting results about the use of digital data for product innovation, the article aims to comprehensively explore and shed light on how agri-food firms utilise external and internal digital data sources when dealing with different product innovations, such as incremental, architecture and radical innovation.Design/methodology/approachThis paper adopts an exploratory multiple-case study and a theory-building process, focussing on the agri-food industry. We collected primary and secondary data from eight manufacturing companies.FindingsThe findings of this research show an empirical framework of six agri-food firms’ digital data utilisation behaviours: the supervisor, the passive supervisor, the developer, the passive developer, the pathfinder and the conjunction behaviour. These digital data utilisation behaviours vary according to a combination of data sources, such as internal data related to inside phenomenon measures (e.g. data generated by sensors installed in the production plan) or external data (e.g., market trends, overall sector sales), and innovation purposes.Practical implicationsThis article offers guiding principles that assist agri-food companies when utilising internal and external digital data sources for specific product innovation outcomes such as incremental, architectural and radical innovation.Originality/valueThe significance of external and internal data sources in stimulating product innovation has garnered substantial attention within academic discussions, highlighting the critical importance of analysing digital data for driving such innovation. Nonetheless, the predominant approach is to study a single innovation outcome through the lens of digital technology. In contrast, our study stands out by adopting a fundamental perspective on data sources, enabling a more nuanced explanation of the overall product innovation outcomes within the agri-food sector.

THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT

Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity’s most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.

Exploring the potential for online data sources to enhance species threat mapping through the case study of global bat exploitation.

Expanding digital data sources, including social media and online news, provide a low-cost way to examine human-nature interactions, such as wildlife exploitation. However, the extent to which using such data sources can expand or bias understanding of the distribution and intensity of threats has not been comprehensively assessed. To address this gap, we quantified the geographical and temporal distribution of online sources documenting the hunting and trapping, consumption, or trade of bats (Chiroptera) and compared these with the distribution of studies obtained from a systematic literature search and species listed as threatened by exploitation on the International Union for Conservation of Nature Red List. Online records were collected using automated searches of Facebook, Twitter, Google, and Bing and were filtered using machine classification. This yielded 953 relevant social media posts and web pages, encompassing 1099 unique records of bat exploitation from 84 countries. Although the number of records per country was significantly predicted by the number of academic studies per country, online records provided additional locations and more recent records of bat exploitation, including 22 countries not present in academic literature. This demonstrates the value of online resources in providing more complete geographical representation. However, confounding variables can bias the analysis of spatiotemporal trends. Online bat exploitation records showed peaks in 2020 and 2014, after accounting for increases in internet users through time. The second of these peaks could be attributed to the COVID-19 outbreak, and speculation about the role of bats in its epidemiology, rather than to true changes in exploitation. Overall, our results showed that data from online sources provide additional knowledge on the global extent of wildlife exploitation, which could be used to identify early warnings of emerging threats and pinpoint locations for further research.

Digital Communication Studies during the Pandemic: A Sociological Review Using Topic Modeling Strategy

The health crisis triggered by COVID-19 has exerted a profound influence on both conventional communication methods and the manifestations of interaction within the virtual sphere. Gradually, studies on digital communication have taken on an increasingly prominent role in various social science disciplines that address determinants such as the crisis of misinformation or digital interaction in contemporary societies. This study aims to analyze the key research topics that sociology has addressed in relation to the pandemic, along with the level of innovation in the utilization of digital sources and analytical methodology. The analysis is grounded in the hypothesis that the effects of the pandemic have led the discipline of sociology to reassess and more fully integrate studies on digital communication. On this premise, a systematic review of studies sourced from the Web of Science (WoS) and Scopus databases was executed. Innovative computational methodologies were employed for the categorization of articles and the elucidation of principal research topics. Furthermore, this research scrutinized the principal digital platforms utilized in these investigations and assessed the extent of methodological innovation applied to data analysis. The outcomes unveiled a pronounced ascendancy in the prominence of communication studies during the pandemic. Nevertheless, it is noteworthy that the utilization of digital data sources in research remains surprisingly limited. This observation highlights a potential avenue for further exploration within the domain of sociological research, promising a more profound and contemporaneous comprehension of social phenomena amid times of crisis.

«Цифровой двойник» населения Арктики в демографических исследованиях и управлении развитием территорий

The article considers “digital twins” of the population as a tool for socio-demographic research and territorial management. The experience of creating digital twins of the population and interactive web-sites devoted to demographic issues is systematized. A methodology for creating a digital twin of the Arctic population is proposed, based on three methodological principles: consideration of the hierarchy of territories, spatial representation of data, and combining demographic statistics with new digital data sources. The author has developed the Digital twin of the Arctic population — an interactive website (dashboard) containing detailed data on the Arctic population, including municipal and settlement levels. It includes demographic statistics, census results and data from digital platforms. The Digital twin of the Arctic population covers such issues as the size, dynamics and composition of the population, resettlement, natural and migration movement, labor and employment, transport movements, science and education, and the impact of the pandemic on demographic processes. Tools of ranking, multivariate analysis, clustering and forecasting of indicator values are implemented for researchers. From the viewpoint of state and municipal management, the main interest is the demographic profiles of regions and territories, reflecting up-to-date information about the demographic situation. Using the Digital twin of the Arctic population, the author draws conclusions about the spatial patterns of the demographic development of the Russian Arctic.

Digital formation processes: A high-frequency, large-scale investigation

Large sources of digital trace data (i.e. “Big Data”) have become increasingly important in the study of material culture. However, akin to the offline material culture traditionally studied by archaeologists, digital trace data is rarely a passive reflection of human behavior – it is a complex palimpsest produced through a variety of erasure and accretion formation processes. To better understand how digital trace palimpsests are formed and how digital formation processes influence and inform our ability to interpret the offline material processes they index, we introduce a computational method – high-frequency archaeological survey – which allows us to observe digital formation processes at a high temporal resolution, as well as a large spatial scale. Using this method every hour for one month, we surveyed posts from across the United States in Craigslist's “Free Stuff” category (popularly called “Curb Alert”), a user-generated source of big digital trace data, indexing material things that have been placed on users' curbs for removal by scavengers or trash collectors. For each post, we observed its time-to-erasure and any edits that were made during the study period – finding that the posts that survive represent a biased sample of those that were posted over the course of the month, conditioned by how recently and on what day the post is posted, the material characteristics of things that are posted about, as well as regional variation. Far from only being evidence of biased end-of-month data, however, we show that further analysis of identified digital formation processes can be an important object of study in its own right – in this case, shedding new light on social scientific questions linking the exchange of “free stuff” with the process of social stratification and urban inequality in the United States. Overall, our findings suggest the importance of accounting for and explicitly analyzing digital formation processes in studies that utilize digital trace data.

Quality Assurance in Digital Forensic Investigations: Optimal Strategies and Emerging Innovations

Digital forensic investigations are critical in modern law enforcement, cybersecurity, and legal proceedings. Ensuring digital evidence’s accuracy, integrity, and reliability is paramount in these contexts. This review article explores the challenges and best practices associated with quality control in digital forensic investigations and the emerging technologies that are reshaping the field. The article begins by discussing the foundational concepts of quality control in digital forensics, emphasizing the need for standardized procedures, documentation, and validation techniques. It delves into the potential sources of errors and bias that can arise during the acquisition, preservation, analysis, and presentation of digital evidence. It highlights the importance of continuous monitoring and review to mitigate these risks. The review article further examines the evolving landscape of digital forensic tools and technologies advancing quality control efforts. It covers advancements in data acquisition methods, including live forensics and memory analysis, and discusses the role of artificial intelligence and machine learning in automating quality control processes. The integration of Blockchain and cryptographic techniques for ensuring the integrity of digital evidence is also explored. In addition, the article addresses the challenges and opportunities presented by cloud computing, IoT devices, and the proliferation of digital data sources. It emphasizes adaptability and agility in quality control approaches to accommodate the changing digital landscape. Through a comprehensive analysis of established practices and emerging technologies, this review article offers practitioners, researchers, and policymakers’ insights into enhancing the reliability and trustworthiness of digital forensic investigations. By adopting robust quality control measures and embracing innovative technologies, the digital forensics community can ensure its findings hold up to scrutiny in the courtroom and beyond.

Using big data for generating firm-level innovation indicators - a literature review

Obtaining indicators on the innovation activities of firms has been a challenge in economic research for a long time. The most frequently used indicators - R&D expenditures and patents - provide an incomplete picture as they represent inputs in the innovation process. Output measurement of innovation has strongly relied on survey data such as the Community Innovation Survey (CIS). However, this type of data suffers from several shortcomings typical of surveys, including incomplete coverage of the business sector, subjectivity concerns, low timeliness, and limited comparability across industries and firms. An alternative that has attracted growing interest is to use big data sources to collect innovation data at the firm level. This paper discusses recent attempts to use digital big data sources including websites and social media to generate firm-level innovation indicators. It summarises the main challenges of using big data and proposes practical guidelines for their use, including a research agenda that should be useful to practitioners as well as users of statistics derived from big data.

Field Epidemiology: Fit for the future.

In recent years, field epidemiologists have embraced rapidly evolving digital tools, data sources and technologies, and collaborated with an ever-growing field of scientific specialisms. The COVID-19 pandemic put field epidemiology under unprecedented demand and scrutiny. As the COVID-19 emergency recedes, it is timely to reflect on the core values of our profession and the unique challenges and opportunities that lie ahead. In November 2022, alumni of the European Programme for Intervention Epidemiology Training (EPIET) and the European Public Health Microbiology (EUPHEM) training programme celebrated 25 years of EPIET, and the present and future of field epidemiology was discussed. The output was recorded and qualitatively analysed. This Perspective reflects the authors' interpretation of the discussion. We should reaffirm our commitment to field epidemiology's core strengths: competence and rigour in epidemiology, surveillance, outbreak investigation and applied research, leading to timely and actionable evidence for public health. Our future success will be defined by an ability to adapt, collaborate, harness innovation, communicate and, ultimately, by our tangible impact on protecting and improving health.

Balancing Digital Forensic Investigation with Cybersecurity for Heavy Vehicle Traffic Crashes

AbstractAfter a traffic crash event, traffic crash investigators collect evidence and data to assist in reconstructing the events to determine crash causation. Some of the data collected in a crash investigation is in the form of digital data from event data recorders built into the electronic control units in the vehicles. Occasionally, traffic crashes are severe enough to destroy the typical network‐based communications protocols to extract the digital forensic data. In these cases, more invasive techniques of gathering forensic data through in‐circuit programming ports or direct reading of data bearing memory chips is needed. While a digital forensic investigation satisfies a virtuous need for society in determining the truth of a traffic crash, the same techniques can be applied by nefarious actors interested in stealing intellectual property (IP) from the same data bearing chips. The exposure of the executable binary containing the IP of the manufacturer has prompted auto makers and suppliers to eliminate access to these sources of digital forensic data by disabling the Joint Task Action Group (JTAG) instrumentation and obfuscating or encrypting the binary data. Herein lies the purpose of this paper, which is to take a systems engineering approach to balance the needs and requirements for a manufacturer to provide sufficient forensic artifacts in the case of an investigation while improving their cybersecurity posture and limiting their exposure to the theft of intellectual property or cyberat‐tack.An activity diagram is presented to show a system model for responding to and investigating a crash event. These activities inform the needs of an improved event data recorder technologies that contain information necessary to reconstruct the crash. Some proposed top level system requirements are presented with a discussion of how they satisfy the needs of the manufacturer and the crash investigator. Specific requirements of recorded data give a notion of a minimal set of recorded data to help investigators. These requirements will improve both the availability and adequacy of forensic data needed for crash event reconstruction. In addition, a separate requirement governing the preservation of Original Equipment Manufacturer (OEM) proprietary software is made, such that their intellectual property is protected to encourage the requirement compliance. Finally, a discussion of how the proposed requirements help determine if a crash event was a result of a cyber‐attack demonstrates the important nature of addressing these needs in future systems.