Data Collection Practices Research Articles

Enhancing multi-site autism research through the development of a collaborative data platform.

Data repositories, particularly those storing data on vulnerable populations, increasingly need to carefully consider not only what data is being collected, but how it will be used. As such, the Autism Intervention Research Network on Physical Health (AIR-P) has created the Infrastructure for Collaborative Research (ICR) to establish standards on data collection practices in Autism repositories. The ICR will strive to encourage inter-site collaboration, amplify autistic voices, and widen accessibility to data. The ICR is staged as a three-tiered framework consisting of (1) a request for proposals system, (2) a REDCap-based data repository, and (3) public data dashboards to display aggregate de-identified data. Coupled with a review process including autistic and non-autistic researchers, this framework aims to propel the implementation of equitable autism research, enhance standardization within and between studies, and boost transparency and dissemination of findings. In addition, the inclusion of a contact registry that study participants can opt into creates the base for a robust participant pool. As such, researchers can leverage the platform to identify, reach, and distribute electronic materials to a greater proportion of potential participants who likely fall within their eligibility criteria. By incorporating practices that promote effective communication between researchers and participants, the ICR can facilitate research that is both considerate of and a benefit to autistic people.

A feminist approach to fintech: exploring ‘buy now, pay later’ technologies and consumer fintech

ABSTRACT ‘Buy now, pay later’ (BNPL) is a financial technology that is reshaping online consumption by allowing users to split payment for goods over 3–4 interest-free digital installments. While the use and value of BNPL has risen dramatically, it, and other consumer-oriented fintech, has received relatively little critical attention. Demographically, the majority of BNPL users are young and women and its negative impacts are disproportionately felt by lower-income groups, making this a specifically gendered financial technology. In this paper we develop a feminist approach to studying fintech, which we use to present a critical analysis of BNPL drawing on data from the US, UK, and Canada. Through this lens, we explore BNPL’s revenue streams, data collection practices, relative lack of regulation, and how these factors function structurally in the digital payments space, to analyze their impact for consumers and retailers in the context of rising consumer indebtedness and the financialization of consumption. We argue that BNPL is a fintech intervention that attempts to shift consumer practices with distinctly gendered implications for social reproduction, household finance, and everyday relations to debt and money.

BEYOND REACTIONARY: SAGE PRACTICES FOR INTENTIONAL REFLEXIVITY IN ONLINE QUALITATIVE DATA COLLECTION

Introduction/Frame: Recent pandemic responsiveness has prompted increased online qualitative data collection. Few researchers have offered detailed discussions reflecting on how and/or why they maintained or adjusted in-person data collection techniques for online shifts. This paper pulled on lived experiences from researchers who reflected upon their online data collection practices, galvanized by evidence from peer-reviewed research. Research Question/Objectives: The following research questions guided this project: (1) How did qualitative data collection change when research rapidly shifted online? (2) What were essential elements to consider from in-person to online qualitative data collection practices? Goals and Methods: The goal was to inform qualitative researchers of key concepts to consider in adjusting to online data collection based on a fusion of the literature alongside professional experiences. This applied methodological paper utilized field notebooks and notes from research discussion groups during the transition to online data collection to examine considerations toward responsively revising data collection procedures aligned to online contexts. Results: Our investigation identified eight essential Successfully Applied, Grounded-in-Experience (SAGE) practices to consider in reconceptualizing data collection approaches for online contexts: (1) (re)examining online data collection differences in proximity and amplification of facial-emphasis communication, (2) strengthening awareness of truncated corporal visuality in online contexts, (3) increasing fluency and agility with digital tools, (4) increasing mindfulness of temporal need for visual data over audio-only data, (5) increasing research-oriented professionalism to counterbalance casual social or classroom-styled video chat practices, (6) enhancing researcher reflexivity regarding transcription and field notes, (7) maintaining procedural flexibility and balance regarding potential hybrid data collection, and (8) increasing temporal accessibility vis-à-vis boundaries. Final Considerations: Building from reactionary experiences and guidance during the emergency transition to online data collection modes and approaches, there is a need to reflexively reconceptualize data collection for online contexts.

Rethinking copyright exceptions in the era of generative AI: Balancing innovation and intellectual property protection

AbstractGenerative artificial intelligence (AI) systems, together with text and data mining (TDM), introduce complex challenges at the junction of data utilization and copyright laws. The inherent reliance of AI on large quantities of data, often encompassing copyrighted materials, results in multifaceted legal quandaries. Issues surface from the unfeasible task of securing permission from each copyright holder for AI training, further muddled by ambiguities in interpreting copyright laws and fair use provisions. Adding to the conundrum, the clandestine practices of data collection in proprietary AI systems obstruct copyright owners from detecting unauthorized use of their materials. The paper explores the exceptions to copyright laws for TDM in the European Union, the United Kingdom, and Japan, recognizing their crucial role in fostering AI development. The EU has a two‐pronged approach under the Directive on Copyright in the Digital Single Market, with one exception catering specifically to research organizations, and another, more generalized one, that can be restricted by rightsholders. The UK allows noncommercial TDM research without infringement but rejected a broader copyright exception due to concerns from the creative sector. Japan has the broadest TDM exception globally, permitting the nonenjoyment use of works without permission, though this can potentially overlook the rights of copyright owners. Notably, the applicability of TDM exceptions to AI‐produced copies remains unclear, creating potential legal challenges. Furthermore, an exploration of the fair use doctrine in the United States provides insight into its potential application in AI development. It focuses on the transformative aspect of usage and its impact on the original work's potential market. This exploration underscores the necessity for clear, practical guidelines. In response to these identified challenges, this paper proposes a hybrid model for TDM exceptions emerges, along with recommended specific mechanisms. The model divides exceptions into noncommercial and commercial uses, providing a nuanced solution to complex copyright issues in AI training. Recommendations incorporate mandatory exceptions for noncommercial uses, an opt‐out clause for commercial uses, enhanced transparency measures, and a searchable portal for copyright owners. In conclusion, striking a delicate equilibrium between technological progress and the incentive for creative expression is of paramount importance. These suggested solutions aim to establish a harmonious foundation that nurtures innovation and creativity while honoring creators' rights, facilitating AI development, promoting transparency, and ensuring fair compensation for creators.

Understanding Farmers' Data Collection Practices on Small-to-Medium Farms for the Design of Future Farm Management Information Systems

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and scale-up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles, lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.

Fair Data is the New Black: Online Shopping, Data Leaks, and Broadening the Understanding of Sustainable Fashion

Fashion is an embodied form of culture. It builds on intimate information about the wearer, concerning e.g. body shape, size, age, weight and style. As fashion is digitalized, intimate body data is shared in online environments, which raises novel concerns about the privacy of fashion consumers. This is especially visible in online shopping, which has revolutionized fashion retail and data collection practices in fashion, particularly as concerns the secure and fair use of consumer data. We argue that the datafication of fashion should be investigated as part of sustainability discourse, and data fairness be established as a responsibility criterion. We analyzed 32 popular Finnish online clothing stores. Data management was studied by analyzing the online stores’ network traffic to third parties, investigating the transparency of their privacy policy documents and analyzing possible dark patterns in the website’s cookie banners. Our findings raise concerns over data responsibility and privacy in various areas. The analyzed websites had a high number of third-party services, averaging 5.2. Only 17 online stores out of the examined 32 (53%) clearly mentioned that they collected identifying information and product information in their privacy policies, and only five of the stores (16%) had no dark patterns in their cookie banners. Our results also suggest that several brands that are otherwise considered ethical may need to pay more attention to their fair data processing and privacy. This indicates that a wider discussion about data privacy and data handling practices is needed. Fair management of data must be part of the process of evaluating the sustainability of clothing companies.

Fashioning DIY digital archives: Unsettling academic research to centre garment workers’ voices

Recent calls for decentring Eurocentric frameworks across fashion studies, alongside growing commitments to worker rights, calls for a circular economy, waste reduction and more sustainable materials draw attention to the complex and intractable social, environmental and political challenges facing the global sector. Here we point out how academic research is also implicated in reproducing inequalities, through practices of data collection, analysis and knowledge dissemination. Specifically, in the case of fashion, how worker representation, and indeed worker control over representations of their lived experiences, including labour activism, is lacking in academic research. In this article, we argue that DIY Academic Archiving can be utilized by academics, including fashion scholars, as a powerful tool for remaking fashion research. We propose unsettling usual practices around data management, as well as redirecting current moves for open research data. Turning instead to inspiration from radical archival theory and practice, we explore the potential for co-creating open-access digital archives of research data – here workers’ own stories – to open up possibilities for workers to be more involved in the creation of public narratives about fashion. While not a panacea for resolving all the ills of the fashion industry, we see research processes where workers have more control over their own stories, and how they are used, as a critical step in reimagining fashion scholarship.

How Does Connecting Online Activities to Advertising Inferences Impact Privacy Perceptions?

Data dashboards are designed to help users manage data collected about them. However, prior work showed that exposure to some dashboards, notably Google’s My Activity dashboard, results in significant decreases in perceived concern and increases in perceived benefit from data collection, contrary to expectations. We theorize that this result is due to the fact that data dashboards currently do not sufficiently “connect the dots” of the data food chain, that is, by connecting data collection with the use of that data. To evaluate this, we designed a study where participants assigned advertising interest labels to their own real activities, effectively acting as a behavioral advertising engine to “connect the dots.” When comparing pre- and post-labeling task responses, we find no significant difference in concern with Google’s data collection practices, which indicates that participants’ priors are maintained after more exposure to the data food chain (differing from prior work), suggesting that data dashboards that offer deeper perspectives of how data collection is used have potential. However, these gains are offset when participants are exposed to their true interest labels inferred by Google. Concern for data collection dropped significantly as participants viewed Google’s labeling as generic compared to their own more specific labeling. This presents a possible new paradox that must be overcome when designing data dashboards, the generic paradox, which occurs when users misalign individual, generic inferences from collected data as benign compared to the totality and specificity of many generic inferences made about them.

Justifying the need for a recovery related surveillance system: Exploratory focused interviews.

No recovery related surveillance system exists but given the evidence of effectiveness and growing supply, a house- and resident- level recovery house (RH) surveillance system could be beneficial for data collection on recovery support service (RSS) engagement, and retention; for improved standardization of RH programs and services; and for identification of outcomes associated with long-term recovery. This study aimed to explore current data collection practices at the resident- and house- level through qualitative focus interviews of RH representatives. The 13 RH interviews were scheduled with 16 RH representative respondents. The most frequently collected resident data was at entry (92%) and departure (85%) and included demographics (n = 5), substance use history (n = 6), treatment and recovery history (n = 5), legal and correctional history (n = 6) and mental health information (n = 7). Recovery support data was collected by 85% of houses. Post-stay data was only collected by four RHs (31%). These results indicate that there is a lack of standardized systematic collection, analysis, and reporting of recovery related data in the RH field. A recovery related surveillance system has the potential to fill this gap and inform and improve standard of resident care to support long-term recovery from substance use disorder.

Dementia-Related Disparities in Adult Day Centers: Results of a Bivariate Analysis.

Adult day services (ADS) are a valuable resource for people living with Alzheimer's disease and Alzheimer's disease and related dementias (AD/ADRD) and serve a large population of late-life immigrants, often with limited English proficiency (LEP). This secondary data analysis examined potential disparities in diagnosis, dementia severity, medical complexity, and dementia-related behavioral problems in persons with AD/ADRD with LEP within the ADS setting. The current study used data from TurboTAR, the electronic health record for ADS in California. Bivariate analyses were conducted to examine differences in clinical management for those with and without LEP. Of 3,053 participants included in the study, 42.3% had LEP. Participants with LEP had higher rates of emergency department use and medication mismanagement. However, due to non-standard data collection, there was a significant amount of missing data on language preference (38.1%) and race/ethnicity (46.5%). Although these findings suggest LEP may play a role in the clinical management of persons with AD/ADRD in ADS, missing data caused by lack of standardized collection compromise the results. It is essential to improve data collection practices in ADS on language, race, and ethnicity to help identify health disparities and promote equitable care for marginalized older adults. [Journal of Gerontological Nursing, 50(4), 42-47.].

Utilizing Machine Learning to Forecast Smoking Behavior

Abstract: The examination of smoking habits, extensively studied over time, has posed challenges in accurately predicting and thoroughly analyzing its determinants. Previous research efforts struggled to precisely predict smoking behavior due to the presence of continuous target variables, which hindered the application of crucial feature selection techniques like mutual information. This study aims to tackle these hurdles through an innovative approach that integrates data preprocessing, feature engineering, and advanced machine learning methods. To address the issue of continuous target variables, our methodology involves categorizing smoking behavior into discrete groups, enabling the utilization of feature selection techniques such as mutual information scores. Logistic regression, Gaussian Naive Bayes, and Random Forest Classifier models are utilized to achieve highly accurate predictions of smoking behavior. The Select KBest method is employed to evaluate the importance of features based on mutual information scores. The investigation delves into various health markers, including BMI, haemoglobin levels, and cholesterol, offering comprehensive insights into their influence on smoking habits. Furthermore, Principal Component Analysis (PCA) is implemented to reduce dimensionality while preserving essential information from the dataset. Through this novel approach and a steadfast commitment to ethical data collection practices, our objective is to advance the comprehension of smoking behavior, surmounting past challenges, and providing valuable insights for public health initiatives and smoking cessation endeavors. The paper assesses outcomes using specified algorithms and parameters, presenting a comparative analysis to enhance the clarity and reliability of our findings.

Quality and sustainability of Ethiopia's national surgical indicators.

In 2015, the Ethiopian Federal Ministry of Health (FMOH) developed the Saving Lives through Safe Surgery (SaLTS) initiative to improve national surgical care. Previous work led to development and implementation of 15 surgical key performance indicators (KPIs) to standardize surgical data practices. The objective of this project is to investigate current practices of KPI data collection and assess quality to improve data management and strengthen surgical systems. The first portion of the study documented the surgical data collection process including methods, instruments, and effectiveness at 10 hospitals across 2 regions in Ethiopia. Secondly, data for KPIs of focus [1. Surgical Volume, 2. Perioperative Mortality Rate (POMR), 3. Adverse Anesthetic Outcome (AAO), 4. Surgical Site Infection (SSI), and 5. Safe Surgery Checklist (SSC) Utilization] were compared between registries, KPI reporting forms, and the DHIS2 (district health information system) electronic database for a 6-month period (January-June 2022). Quality was assessed based on data completeness and consistency. The data collection process involved hospital staff recording data elements in registries, quality officers calculating KPIs, completing monthly KPI reporting forms, and submitting data into DHIS2 for the national and regional health bureaus. Data quality verifications revealed discrepancies in consistency at all hospitals, ranging from 1-3 indicators. For all hospitals, average monthly surgical volume was 57 cases, POMR was 0.38% (13/3399), inpatient SSI rate was 0.79% (27/3399), AAO rate was 0.15% (5/3399), and mean SSC utilization monthly was 93% (100% median). Half of the hospitals had incomplete data within the registries, ranging from 2-5 indicators. AAO, SSC, and SSI were commonly missing data in registries. Non-standardized KPI reporting forms contributed significantly to the findings. Facilitators to quality data collection included continued use of registries from previous interventions and use of a separate logbook to document specific KPIs. Delayed rollout of these indicators in each region contributed to issues in data quality. Barriers involved variable indicator recording from different personnel, data collection tools that generate false positives (i.e. completeness of SSC defined as paper form filled out prior to patient discharge) or missing data because of reporting time period (i.e. monthly SSI may miss infections outside of one month), inadequate data elements in registries, and lack of standardized monthly KPI reporting forms. As the FMOH introduces new indicators and changes, we recommend continuous and consistent quality checks and data capacity building, including the use of routinely generated health information for quality improvement projects at the department level.

Eliminating invisible deaths: the woeful state of global rabies data and its impact on progress towards 2030 sustainable development goals for neglected tropical diseases

Like other neglected diseases, surveillance data for rabies is insufficient and incompatible with the need to accurately describe the burden of disease. Multiple modeling studies central to estimating global human rabies deaths have been conducted in the last two decades, with results ranging from 14,000 to 74,000 deaths annually. Yet, uncertainty in model parameters, inconsistency in modeling approaches, and discrepancies in data quality per country included in global burden studies have led to recent skepticism about the magnitude of rabies mortality. Lack of data not only limits the efficiency and monitoring of rabies elimination strategies but also severely diminishes abilities to advocate for support from international funding agencies. Meanwhile, the most vulnerable communities continue to suffer from deaths that could have been prevented through more robust reporting. The Zero by 30 global strategy to eliminate dog-mediated human rabies by 2030 recommends endemic countries adopt the intersectoral approach, Integrated Bite Case Management (IBCM), as a cost-effective method to enhance surveillance. However, effective implementation of IBCM is impeded by challenges such as limited capacity, resources, knowledge, skills, and attitudes toward compliance. To address this, the World Health Organization and United Against Rabies Forum have developed several open-access tools to guide national control programs in strong data collection practices, and online data repositories to pragmatically streamline reporting and encourage data sharing. Here, we discuss how current and future initiatives can be best employed to improve the implementation of existing surveillance tools and prioritization of effective data reporting/sharing to optimize progress toward 2030 elimination.

Kaleidoscopic Inquiries: Queering Approaches to Organizational Diversity Work

This multi-site qualitative research study utilizes a queer theoretical framework to analyze norms and normativity in organizational diversity work. The findings suggest that diversity work contributes to an ontological bifurcation of the individual and organization that foregrounds the individual and casts the organization to the background as an accessory to personal development. To understand how this ontological bifurcation emerges, the analysis traces three metaphors as focal points of norm inquiry related to diversity work – journey, container, and table – and considers them alongside the practices of training, data collection, and positional leadership. The persistent bifurcation of organization and individual helps to reinstate the very inequities that diversity work seeks to address, suggesting to both scholars and practitioners the need for a more durable disruption of the status quo in diversity work.

Predicting Smoking Status of a Person using Machine Learning

Abstract: The study of smoking behavior, a topic extensively researched over time, has presented difficulties in accurately predicting and thoroughly analyzing its determinants. Previous studies struggled to predict smoking behavior accurately, mainly due to the presence of continuous target variables, hindering the application of vital feature selection methods like mutual information. This research aims to address these challenges through an innovative approach that incorporates data preprocessing, feature engineering, and advanced machine learning techniques. To overcome the issue of continuous target variables, our methodology involves categorizing smoking behavior into discrete groups, allowing the use of feature selection methods such as mutual information scores. Logistic regression, Gaussian Naive Bayes, and Random Forest Classifier models are employed in this study to achieve highly accurate predictions of smoking behavior. The Select KBest method is utilized to assess the significance of features based on mutual information scores. The research explores various health indicators, including BMI, haemoglobin levels, and cholesterol, providing comprehensive insights into their impact on smoking behavior. The principal component analysis, or PCA, is another technique used to lower multiplicity. while retaining essential information from the dataset. Through this innovative approach and a rigorous commitment to ethical data collection practices, our goal is to advance the understanding of smoking behavior, overcoming previous challenges, and offering valuable insights for public health initiatives and smoking cessation efforts. The study evaluates results using specified algorithms and parameters, presenting a comparative analysis to enhance the clarity and robustness of our findings.

Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.

A scoping review on tools and methods for trait prioritization in crop breeding programmes

Trait prioritization studies have guided research, development and investment decisions for public-sector crop breeding programmes since the 1970s, but the research design, methods and tools underpinning these studies are not well understood. We used PRISMA-ScR (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) to evaluate research on trait ranking for major crops over the past 40 years (1980–2023). Data extraction and descriptive analysis on 657 papers show uneven attention to crops, lack of systematic sex disaggregation and regional bias. The lack of standardized trait data taxonomy across studies, and inconsistent research design and data collection practices make cross-comparison of findings impossible. In addition, network mapping of authors and donors shows patterns of concentration and the presence of silos within research areas. This study contributes to the next generation of innovation in trait preference studies to produce more inclusive, demand-driven varietal design that moves beyond trait prioritization focused on productivity and yield.

A Review of Privacy Concerns in Energy-Efficient Smart Buildings: Risks, Rights, and Regulations

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data...

The Integration of HR Analytics and Decision Making

Integrating HR Analytics into organizational decision-making processes is crucial for modern businesses looking to improve human resource management practices and drive better decision-making outcomes. This systematic literature review explores the importance of HR Analytics integration, its key determinants, and its impact on organizational performance and decision-making effectiveness. Through an analysis of existing literature, this review identifies factors that influence HR Analytics integration, such as data collection practices, technology availability, managerial skills, and organizational policies. This review also revealed a research gap regarding the optimal use of HR Analytics for strategic decision making, highlighting the need for further research in this area. Overall, these findings underscore the importance of HR Analytics in driving better HR management practices and better organizational decision-making outcomes.