Digital Data Collection Research Articles

Data Journey: Digitalization Projects Deliver Returns for Operators

Data are the backbone of what Klaus Schwab—the founder and executive chairman of the World Economic Forum—proclaimed to be the Fourth Industrial Revolution. This 21st‑century revolution is one of connectivity, advanced analytics, automation, and manufacturing technology. While the road to this high‑tech revolution started long ago with the use of water and steam to power mechanized production, it was electricity’s ability to power mass production that delivered us from the first to the second industrial revolution. Electronics and information systems necessary to automate production ushered in the third revolution. The data and our abilities to decipher and extract value from these seemingly random and disparate patterns of numbers and letters to create actionable intelligence is rapidly transforming the way business is conducted. Data are everywhere and in everything. But on the grand scale of time, the collection, analysis, and application of digital data is relatively young. Billy Bean and the Major League Baseball team Oakland Athletics, for example, demonstrated the importance of data‑driven decision making in the early 2000s by leveraging the power of data analytics to go from last place contender to chasing the championship in what has become known as Moneyball. Some 20 years later, the power of data and digital continues to evolve, with the bright lights of Qatar’s Lusail Stadium, for example, shining down last month on the final football match of the FIFA World Cup; every movement made by Al Rihla was captured, stored, and analyzed. But Al Rihla was not a player; it was the football created by Adidas and used in each of the more than 60 matches held throughout the tournament with an internal sensor for real‑time ball tracking capabilities. High‑speed cameras and antennas surrounding the football pitch provided additional support, feeding data into FIFA’s enhanced football intelligence service. The data collected during the matches were analyzed on a variety of metrics like possession time, line breaks, pressure on the ball, and forced turnovers, and then shared with teams with the goal of improving the game and its players. Winning the “beautiful game” is not as simple as it looks, as there’s more to kicking the ball into the opposing team’s net to score a goal as many times as possible in 90 minutes. These data offer an inside look into the processes of success or failure. Simple, too, is the production of oil and gas when explained on paper: drill a hole into the ground, route what flows to the surface into a pipeline, and then get paid for the effort invested in delivering that resource to the market when the price is right. It is more complicated than that, and data are playing an important role in delivering production increases and more. Data powered the shale gale that swept across the US oil and gas industry. David Millwee, vice president of drilling performance for Patterson‑UTI Drilling, credits data for helping to deliver the yearly increases in the lateral footage drilled.

Contextualizing realism: An analysis of acts of seeing and recording in Digital Twin datafication

Digital Twins are conceptualized as real-time digital representations of real-life physical entities or systems. They are explored for a wide array of societal implementations, and in particular to help address fundamental societal challenges. As accurate digital equivalents of their real-life twin, Digital Twins substitute their physical twin in knowledge production and decision-making processes. They raise high expectations: they are expected to produce new knowledge, expose issues early, predict future behavior, and help to optimize the physical twin. Data play a key role here because they form the building blocks from which the Digital Twin representation is created. However, data are not neutral phenomena but products of human-technology interaction. In this article, we therefore raise the question of how a Digital Twin data collection is created, and what implications does this have for Digital Twins? To answer this question, we explore the data collection process in three cases of Digital Twin development at a university. Connecting to Jasanoff's theoretical framework of regimes of sight, we approach the creation of a data collection as acts of seeing and recording that influence how reality is represented in data, as well as give a certain legitimacy and authority to the data collection. By examining the acts of seeing and recording and their respective roles in producing the data collection, we provide insight into the struggles of representation in Digital Twins and their implications.

"I liked just that it was a communal thing": Feasibility and acceptability of engaging with transgender and gender-diverse persons in a digital photovoice research study on commercial cigarette smoking risk and protective factors.

Commercial cigarette smoking is the leading modifiable risk factor for more than 16 types of cancer. Over one-third (35.5%) of transgender and gender-diverse (TGD) adults smoke cigarettes compared to 14.9% of cisgender adults. The objective of this paper is to describe the feasibility of enrolling and engaging with TGD persons in a digital photovoice study to examine smoking risk and protective factors through real-world experiences (Project SPRING). The study comprised a purposeful sample of 47 TGD adults aged ≥18 years who currently smoke and live in the United States (March 2019-April 2020). They participated in three weeks of digital photovoice data collection using Facebook and Instagram closed groups. A subsample participated in focus groups to explore smoking risks and protective factors in greater depth. We summarized the enrollment strategies and accrual rates, participant engagement (posts, comments, and reactions) during the photovoice data collection to assess study feasibility, and respondent feedback on acceptability and likability during and after the study. Participants were recruited via Facebook/Instagram advertising (n = 33) and via Craigslist/word-of-mouth (n = 14). Costs ranged from $29 via Craigslist/word-of-mouth to $68 per recruited participant via Facebook/Instagram advertising. On average, participants posted 17 pictures of smoking risks/protective factors, commented 15 times on others' posts, and had 30 reactions within their group over 21 days. Participants' rating of the acceptability and likability of the study were positive based on closed- and open-ended feedback. The findings of this report will inform future research to engage with TGD community-engaged research to develop culturally tailored interventions to reduce smoking prevalence among TGD individuals.

Challenges in Use of Practice-based Research Networks for a Medical Device Trial to Detect SARS-CoV-2.

Primary care practice-based research networks (PBRNs) participated in a point of care (POC) device study funded by by the National Institutes of Health and led by the University of Massachusetts Chan Medical School (UMass) to speed the development, validation, and commercialization of POC tests to detect SARS-CoV-2. The purposes of this study were to describe the characteristics of participating PBRNs and their respective collaborators in this device trial and describe complications challenging its execution. Semi-structured interviews were conducted with lead personnel from participating PBRNs and UMass. Four PBRNs and UMass were invited to participate and 3 PBRNs and UMass participated. This device trial recruited 321 subjects in 6 months; 65 subjects from PBRNs. Each PBRN and the academic medical center site enrolled and recruited subjects differently. Main challenges identified were having adequate clinic personnel to enroll and aid in consent and questionnaire completion, frequently changing inclusion/exclusion criteria, use of the digital electronic data collection platform, and having access to a -80°C freezer to store supplies. This trial involved numerous researchers, primary care clinic leaders and staff, and academic center sponsored program staff and attorneys resulting in a resource-intensive endeavor to enroll 65 subjects in the real-world clinical setting of primary care PBRNs with the academic medical center enrolling the rest. Multiple obstacles to standing up the study were encountered by the PBRNS. Primary care PBRNs rely largely on the goodwill established between academic health centers and participating practices. For future investigations involving device studies, collaborating PBRN leaders should assess whether recruitment criteria may change, obtain detailed lists of equipment needed, and/or know if the study is likely to be halted suddenly to appropriately prepare their member practices.

Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data

Although the Internet of Things has been widely applied, the problems of cloud computing in the application of digital smart medical Big Data collection, processing, analysis, and storage remain, especially the low efficiency of medical diagnosis. And with the wide application of the Internet of Things and Big Data in the medical field, medical Big Data is increasing in geometric magnitude resulting in cloud service overload, insufficient storage, communication delay, and network congestion. In order to solve these medical and network problems, a medical big-data-oriented fog computing architecture and BP algorithm application are proposed, and its structural advantages and characteristics are studied. This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate, compare and analyze the fog node through the Internet of Things. The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect. Considering the weak computing of each edge device, the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power, enhance the medical intelligence-aided decision-making, and improve the clinical diagnosis and treatment efficiency. In the application process, combined with the characteristics of medical Big Data technology, through fog architecture design and Big Data technology integration, we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things. The results are promising: The medical platform network is smooth, the data storage space is sufficient, the data processing and analysis speed is fast, the diagnosis effect is remarkable, and it is a good assistant to doctors’ treatment effect. It not only effectively solves the problem of low clinical diagnosis, treatment efficiency and quality, but also reduces the waiting time of patients, effectively solves the contradiction between doctors and patients, and improves the medical service quality and management level.

The Slow Food Movement and the Terra-Madre project: food sovereignty and translocal assemblages

ABSTRACT Since 2004 the SF Movement has developed a global community of food producers and activists called Terra-Madre. Every two years representatives of the community meet in Turin for five days of workshops and conferences. Due to Covid-19 crisis SF has radically changed the nature of the meeting, presenting a six-month calendar of digital and physical events around the world. The paper examines the exchange occurring between communities as a form of translocal activism and considers to what extent SF can be defined as a movement for food sovereignty. It draws on digital data collection on the online activities and research with participants involving semi-structured interviews and observation notes. The paper reflects on the experience of digital fieldwork and how the new format of Terra-Madre provides insights into SF as a translocal movement characterized by conflicts, common perspectives and emergent capabilities.

Деструктивная коммуникация молодёжи в сети Интернет: социально-политический контекст

The paper presents the general results of the study of destructive behaviour of young people in web communities. At the first stages of the study the aim was to identify destructive communication practices among young people on the Internet, including in the context of political discourse. At the final stage the aim was to identify strategies and techniques in the news content that can form and provoke destructive patterns of users’ responses to various socio-political phenomena (events, decisions and persons associated with these events).
 The study included a total of 530 respondents — representatives of student youth from va­rious universities in Tomsk. The empirical material of the study also included regional news reports on socio-political topics, posted in free access by various communities on VK social network (in Tomsk and Novosibirsk). We analyzed 3,786 posts from Novosibirsk and 887 from Tomsk. For data analysis the authors used qualitative method (content analysis, topic analysis, psycholinguistic analysis, psychological analysis, machine learning methods) and quantitative method (API-based data mining algorithm, digital footprint data collection using data platform (https://lk.data.tsu.ru/), natural language processing methods for text analysis and information extraction, machine learning methods, PolyAnalyst text analysis platform). Within the scope of the project the authors studied the specifics of perception and consumption of political content on social media and identified destructive behaviour among young people in web communities.

WEB-BASED APPLICATION FOR ENHANCING ON-SITE AND REMOTE DATA COLLECTION PROCESS DURING PANDEMIC: AN EXPLORATORY STUDY

The COVID-19 pandemic has changed the way data collection for research takes place around the world. Such adaptation has forced a turn of research methodologies in conducting research. This study focuses on on-site and remote digital data collection methods that can be adopted during the pandemic. The method of research and data collection often requires a group of researchers to travel to a specific site to meet communities for data collection, which is not permissible during the pandemic. This paper explores the use of web-based application for documentation of the existing natural and built features, and land management system for identification of the rural community’s land information. In this paper, the use of a web-based application, namely i-LULACAST, is highlighted. The application was designed and used for data entry and management of the rural community with fewer human resources on-site while still maximizing the number of datasets needed for analysis. The system was built using Codelgniter Application 4.0.4 to develop libraries to link databases and perform operations such as data entry, location, and uploading pictures for particular data. This system has also shown prospects for other purposes, such as census, landscape data entry, and contact tracing for medical purposes.

The Application of Terrestrial Light Detection and Ranging to Forest Resource Inventories for Timber Yield and Carbon Sink Estimation

New technologies are utilized to improve forest management, but they require the collection and analysis of digital data. This study assessed the applicability of using light detection and ranging (LiDAR) devices for the examination of forest resource inventories to obtain digital forest resource information. Two terrestrial LiDARs, a backpack laser scanner (BPLS) and a terrestrial laser scanner (TLS) were used and compared with the traditional method to identify which was optimal. The findings showed that the TLS single-scan method was least accurate due to occlusion. The TLS multi-scan method and the BPLS showed high levels of accuracy for the height and diameter at breast height (DBH) estimates in most sample plots. However, the BPLS underestimated height to a greater extent than the other methods in a sloped plot (24°). Nevertheless, the efficiency of the BPLS was 2.8 times higher than that of the TLS when considering the amount of time spent on completing all indoor and outdoor tasks. Thus, these results prove that the utilization of LiDAR devices increases the efficiency of data collection and overcomes the limitations of existing methods. Moreover, they provide accurate information that will be a basis for forest management plans, the estimation of biomass, and the transaction of forest products.

Development and validation of a computerized web-based quantitative food frequency questionnaire.

Epidemiological research is progressing towards digital data collection. This study aimed to evaluate the validity and reproducibility of our new computerized, and easy-to-use Food Frequency Questionnaire (FFQ). Participants' dietary intake was assessed using 24-h Dietary Recalls (DRs) and our FFQ, consisting of 133 food items and beverages. The software allows users to choose between three visualized portion sizes, categorizes the selections into food groups with three degrees of food processing levels, and produces a visualized output of the results. The reproducibility of the FFQ was evaluated based on two user submissions, and its validity was measured by comparing its calculated caloric intake and macro and micro-nutrient consumption to the equivalent mean values from three 24-h DRs. Thirty-nine women of fertility age [18-45] were recruited to the study, of whom twenty-six qualified for reproducibility testing and thirty-one qualified for validity testing. For most nutrient intakes, the FFQs yielded higher scores than the 24-h DRs, resulting in a less satisfactory agreement between them due to FFQs overestimation. The Intra Class Correlation (ICC) coefficient between the two FFQs ranged from moderate for calcium (0.55) to high for magnesium (0.83) (p<0.05), indicating good reproducibility. Evaluation of food groups and processed food reproducibility scores yielded ICC coefficients ranging from moderate (0.53; super-processed foods) to high (0.83; non-processed foods) (p<0.05). Spearman's correlation coefficient showed a moderate (sugar-sweetened beverages) to strong (non-processed foods) correlation (p<0.05). The FFQ we developed and validated in this study showed moderate to high reproducibility and acceptable validity in a group of thirty-nine women of fertility age. Moreover, it is highly adjustable and easy to use, and its digital-based delivery enables large-scale, multilingual nutritional research.

Perceived barriers to the process of COVID-19 control among frontline healthcare workers in South Korea: a qualitative study

ObjectiveThis study aimed to explore barriers to disease control perceived by frontline healthcare workers (HCWs) working in community settings during the COVID-19 pandemic in South Korea.DesignA qualitative study was conducted...

Adaptive Lighting for Curved and Nonuniform Objects in Optomechanical Inspection Systems

Visual inspection is omnipresent and critical in precision manufacturing. However, complex geometries of parts hinder uniform illumination, and high reflectivity challenges accurate focusing for digital visual data collection. This research provides a novel adaptive illuminance distribution for consistent lighting to facilitate quality imaging over complex-shaped, highly reflective surfaces. The central approach entails using arrays of independently controlled light sources to reliably generate different lighting patterns, structures, and colors. Such results consider the geometry, the 3-D pose of parts in the environment, and the surface topography of the workpiece to be inspected, hence amplifying the capabilities of an image capturing system. In this article, we discuss the mathematical problem formulation, analytic solution, optimality of the proposed lighting, and experimental results in imaging curved parts common in aerospace manufacturing. The efficacy of the resulting defect identification is tested using a deep neural network.

A framework for privacy preserving digital trace data collection through data donation

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU’s 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with “data download packages” (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens’ digital life can be obtained and analyzed to answer social-scientific questions – with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a “total error framework” for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.

Use of mHealth tools to register birth outcomes in low-income and middle-income countries: a scoping review

ObjectiveAccurate reporting of birth outcomes in low-income and middle-income countries (LMICs) is essential. Mobile health (mHealth) tools have been proposed as a replacement for conventional paper-based registers. mHealth could provide...

2D image-based crack monitoring: an affordable, sufficient and non-invasive technique for the democratization of preventive conservation of listed buildings

In order to democratize the preventive conservation of most listed buildings, we need to have an affordable tool that allows controlling their main vital signs. Complex and costly control installations, such as large digital data collection campaigns or 3D laser scanning, are not always possible. Methods for analogue data collection, such as comparators and callipers, are much more affordable and discreet but require the use of auxiliary lifting platforms for both installation and reading and control, hence resulting too invasive. In this paper we study techniques for digital indoor monitoring of cracks of listed masonry buildings. 2D image-based crack monitoring technique was found to be sufficient, affordable, traceable, and respectful with the use of the building. To verify its effectiveness, we performed laboratory tests and implement it in a real case study, a church in Zaragoza, Spain, currently undergoing a reparation process. The obtained results show that the proposed 2D image-based inspection technique provides measurements closer to the comparator, used as a reference, than the calliper, with a mean difference of 0.15 mm with respect to the reference, and a standard deviation of 0.17 mm, and its use can be up to 56% cheaper than the comparator. Therefore, the technique is easily generalizable to different heritage buildings.

Mapping forest road properties: From pen-and-paper to digital data collection in a GIS

Mapping forest road properties: From pen-and-paper to digital data collection in a GIS

Traps, apps and maps: to what extent do they provide decision-grade data on biodiversity?

ABSTRACT Ecosystem services arising from the restoration of natural capital are now increasingly recognised as environmental opportunities and monetised, with international climate negotiations focussing on the need for investment into natural capital, and the finance sector pledging to invest. The finance sector has called for decision-grade, asset level data about nature projects in order to facilitate their reporting to investors. This paper offers a case study of novel digital data collection methods used to establish a baseline of faunal biodiversity in a Scottish nature-restoration project on the Bunloit estate which has secured private natural capital investment. Digital camera traps, acoustic sensors with eDNA samples and apps were used to create digital maps to ensure annual survey replication, and citizen scientist engagement. The results were classified by both professional ecologists and citizen scientists. We discuss how the digital data gathered through traps, apps and maps in the case study can be qualified as decision-grade data, according to the Taskforce for Nature-based Financial Disclosure’s specification. We conclude that decision-grade biodiversity data may be produced by practitioners, with limited resources, and make recommendations for data collection and governance methods to ensure nature restoration projects generate decision-grade data for ecosystem services markets.

Factors Affecting the Transition from Paper to Digital Data Collection for Mobile Tuberculosis Active Case Finding in Low Internet Access Settings in Pakistan.

Between September 2020 and March 2021, Mercy Corps piloted hybrid digital (CAPI) and paper-based (PAPI) data collection as part of its tuberculosis (TB) active case finding strategy. Data were collected using CAPI and PAPI at 140 TB chest camps in low Internet access areas of Punjab and Khyber Pakhtunkhwa provinces in Pakistan. PAPI data collection was performed primarily during the camp and entered using a tailor-performed CAPI tool after camps. To assess the feasibility of this hybrid approach, quality of digital records were measured against the paper “gold standard”, and user acceptance was evaluated through focus group discussions. Completeness of digital data varied by indicator, van screening team, and month of implementation: chest camp attendees and pulmonary TB cases showed the highest CAPI/PAPI completeness ratios (1.01 and 0.96 respectively), and among them, all forms of TB diagnosis and treatment initiation were lowest (0.63 and 0.64 respectively). Vans entering CAPI data with high levels of completeness generally did so for all indicators, and significant differences in mean indicator completeness rates between PAPI and CAPI were observed between vans. User feedback suggested that although the CAPI tool required practice to gain proficiency, the technology was appreciated and will be better perceived once double entry in CAPI and PAPI can transition to CAPI only. CAPI data collection enables data to be entered in a more timely fashion in low-Internet-access settings, which will enable more rapid, evidence-based program steering. The current system in which double data entry is conducted to ensure data quality is an added burden for staff with many activities. Transitioning to a fully digital data collection system for TB case finding in low-Internet-access settings requires substantial investments in M&E support, shifts in data reporting accountability, and technology to link records of patients who pass through separate data collection stages during chest camp events.

Reactivity in measuring sensitive online behavior

PurposeDigital trace data provide new opportunities to study how individuals act and interact with others online. One advantage of this type of data is that it measures behavior in a less obtrusive way than surveys, potentially reducing measurement error. However, it is well documented that in observational studies, participants' awareness of being observed can change their behavior, especially when the behavior is considered sensitive. Very little is known regarding this effect in the online realm. Against this background, we studied whether people change their online behavior because digital trace data are being collected.Design/methodology/approachWe analyzed data from a sample of 1,959 members of a German online panel who had consented to the collection of digital trace data about their online browsing and/or mobile app usage. To identify reactivity, we studied change over time in five types of sensitive online behavior.FindingsWe found that the frequency and duration with which individuals engage in sensitive behaviors online gradually increases during the first couple of days after the installation of a tracker, mainly individuals who extensively engage in sensitive behavior show this pattern of increase after installation and this change in behavior is limited to certain types of sensitive online behavior.Originality/valueThere is an increased interest in the use of digital trace data in the social sciences and our study is one of the first methodological contributions measuring reactivity in digital trace data measurement.

Symptom monitoring based on digital data collection during inpatient treatment of schizophrenia spectrum disorders – A feasibility study

Digital acquisition of patients’ self-reports on individual risk factors and symptom severity represents a promising, cost-efficient, and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. Yet, studies investigating digital data collection in patients with a schizophrenia spectrum disorder (PSSDs) are scarce. The objective of this study was to explore the feasibility of digitally acquired self-report assessments of risk and symptom profiles at the time of admission into inpatient treatment in an age-representative sample of hospitalized PSSDs. We investigated the required support, the data entry pace, and the subjective user experience. Findings were compared with those of patients with an affective disorder (PADs). Of 82 PSSDs who were eligible for inclusion, 59.8% (n=49) agreed to participate in the study, of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates, and subjective experience ratings did not differ between PSSDs and PADs. Patients reported high satisfaction with the assessment. PSSDs required more support and time for the data entry than PADs. Our results indicate that digital data collection is a feasible and well-received method in PSSDs. Future clinical and research efforts on digitized assessments in psychiatry should include PSSDs and offer support to reduce digital exclusion.