Data Collection Practices Research Articles

Practicalities of language data collection and management in and around Indonesia

Researchers use different approaches when collecting and managing primary language materials during fieldwork. Yet it is important that this work is done in a transparent way, so that it can be used by other researchers, who might have other aims, as well as by the speaker community who might want to use or take note of the collected materials. In this article we use our research experience in language data collection in and around Indonesia in fieldwork projects of three kinds: descriptive fieldwork, linguistic surveys, and projects investigating language contact. Our aim is to provide an introductory and practical guide for students and professionals who are embarking on fieldwork in or around Indonesia. Describing practical methods of language data collection, processing, and management, our aim is to provide a guide for any research which involves the collection of language materials, including linguistic research, oral history or literature, and ethnography.

Abstract No. 579 Interventional radiology: best social media practices for data collection, dissemination, and engagement

Social media has emerged as a powerful tool for networking, marketing, data collection, and sharing educational content. For these reasons, platforms like Twitter, Facebook, and Instagram are extremely popular not only amongst the public but also the medical community. As patients, providers, and students increasingly use social media as a hub for sharing medical information, there is a newfound importance in developing strategies to craft engaging posts and search for relevant content.

The economics of the German investigation of Facebook’s data collection

The importance of digital platforms and related data-driven business models is ever increasing and poses challenges to the workability of competition in the respective markets (tendencies towards dominant platforms, paying-with-data instead of traditional money, privacy concerns, etc.). Due to such challenges, investigations of such markets are of high interest. One of the recent cases is the investigation of Facebook’s data collection practices by German competition authorities. Our article, in contrast to the wide stream of legal studies on this case, aims to analyse whether Facebook’s practices regarding data collection could constitute an abuse of market power from an economic perspective, more specifically against the background of modern data economics. In doing so we summarise the state of the advanced theories, including influences from behavioural economics addressing such markets, and discuss four potential theories of harm.

The Invisible Work of Maintenance in Community Health

Frontline health workers are the first and often the only access point to basic health care services in low-and-middle income countries. However, the work and the issues frontline health workers face are often invisible to the healthcare system, with limited resources to assist them. This study explores the work practices, challenges and roles of frontline health workers in community health with particular focus on pregnancy care in South India. Drawing on the notion of maintenance and articulation work, we describe the maintenance work of frontline health workers maintaining, anticipating, reconciling, and supporting care infrastructures beyond data collection practices. Our findings highlight how socio-cultural practices, perceptions, status, and existing systems influence maintenance work practices. Based on our findings, we suggest moving beyond the focus on training and performance to design CSCW tools to support the maintenance work of frontline health workers as 'system-builders' to make healthcare infrastructures work in community health.

Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery.

BACKGROUND: Sensor technologies and data collection practices are changing and improving quality metrics across various domains. Surgical skill assessment in Robot-Assisted Minimally Invasive Surgery (RAMIS) is essential for training and quality assurance. The mental workload on the surgeon (such as time criticality, task complexity, distractions) and non-technical surgical skills (including situational awareness, decision making, stress resilience, communication, leadership) may directly influence the clinical outcome of the surgery. METHODS: A literature search in PubMed, Scopus and PsycNet databases was conducted for relevant scientific publications. The standard PRISMA method was followed to filter the search results, including non-technical skill assessment and mental/cognitive load and workload estimation in RAMIS. Publications related to traditional manual Minimally Invasive Surgery were excluded, and also the usability studies on the surgical tools were not assessed. RESULTS: 50 relevant publications were identified for non-technical skill assessment and mental load and workload estimation in the domain of RAMIS. The identified assessment techniques ranged from self-rating questionnaires and expert ratings to autonomous techniques, citing their most important benefits and disadvantages. CONCLUSIONS: Despite the systematic research, only a limited number of articles was found, indicating that non-technical skill and mental load assessment in RAMIS is not a well-studied area. Workload assessment and soft skill measurement do not constitute part of the regular clinical training and practice yet. Meanwhile, the importance of the research domain is clear based on the publicly available surgical error statistics. Questionnaires and expert-rating techniques are widely employed in traditional surgical skill assessment; nevertheless, recent technological development in sensors and Internet of Things-type devices show that skill assessment approaches in RAMIS can be much more profound employing automated solutions. Measurements and especially big data type analysis may introduce more objectivity and transparency to this critical domain as well. SIGNIFICANCE: Non-technical skill assessment and mental load evaluation in Robot-Assisted Minimally Invasive Surgery is not a well-studied area yet; while the importance of this domain from the clinical outcome’s point of view is clearly indicated by the available surgical error statistics.

Just a spoonful of ice cream helps the methods go down: A hands-on approach to experimental design, null hypothesis testing, and inferential statistics

Courses Research methods for undergraduates or graduates. Objectives The aims of this activity are: (1) to clarify the basics of experimental design, (2) to illustrate the concept of levels of measurement, (3) to demonstrate in-person/hands-on data collection procedures, (4) to understand and practice the steps in null hypothesis testing, and (5) to engage with different kinds of inferential statistics.

Notified But Unaware: Third-Party Tracking Online

Drawing from conceptual studies on transparency, particularly with regard to market complexity, user literacy and resignation, this article studies and analyzes the practices of third-party data collection online. Empirically, we map third-party trackers on a sample of Swedish websites in five sectors (media, retail, banking/insurance, public sector, and health), and the trackers are compared to lists of known trackers to determine their main purpose. These results are then used in digital focus groups divided into high-trust and low-trust individuals, to better understand how everyday consumers perceive and cope with the tracking infrastructure. The main results indicate that third-party tracking is omnipresent online, particularly for media and retail sites, showing that data-driven markets depend on the collection, sharing and trade of consumers’ personal data. Furthermore, despite regulatory provisions promoting clearer notifications and designs within the EU, many users are still highly unaware of the data collection practices and their underlying purposes. The results indicate a weak consent base for data collection and a market structure and data collecting practices that are highly non-transparent. We recommend more active supervisory authorities, and more stringent requirements primarily in relation to the obscure ad tech infrastructures to improve transparency and promote consumer awareness.

Assisted-Fog-Based Framework for IoT-Based Healthcare Data Preservation

Healthcare has witnessed a technological advancement in improving the quality of care and speeding the process of diagnosing patients due to its intervention with the internet of medical things. IoT in healthcare (H-IoT) plays a significant role in facilitating the process of diagnosing and detecting diseases. Different IoT-based medical sensors are used to measure biometrics and send them to the cloud for more analysis. However, the sensed data are massive and vary in their criticality level in which some sensed data are more critical (health-related data) than others. Moreover, computing such critical data in the cloud encounters some delay which is not preferable in real-time monitoring applications. Thus, this work proposes an IoT-fog-based framework to classify the streamed data according to their criticality level and compute the critical data in the fog to detect abnormalities with low latency and high response time. Before designing the proposed work, an analysis was conducted to explore the real data collected by IoT-based medical apps. The exploration of the data involved downloading and manually analyzing up-to-date privacy policies of eight IoT-based medical apps that provide details about data collection practices. The study showed that the streamed data in H-IoT include medical sensors data, apps registration data (personal information), device information, and other information related to cookies. The proposed work introduced the design of fog-based data classification and the algorithm for such classification. The implementation and evaluation of the proposed framework is future work.

Conhecimento, atitude e prática de adolescentes escolares em relação ao uso do preservativo masculino

OBJETIVO: Avaliar conhecimentos, atitudes e práticas de adolescentes escolares em relação ao preservativo masculino, como método contraceptivo de primeira escolha para os adolescentes. MÉTODO: Estudo com abordagem quantitativa, realizado com 143 adolescentes de uma escola estadual de ensino médio, no perído de outubro a novembro de 2019. Para a coleta de dados, aplicou-se o inquérito Conhecimento, Atitude e Prática (CAP) sobre o uso do preservativo masculino. RESULTADOS: Evidenciou-se que 133 (93%) dos adolescentes apresentaram conhecimento inadequado, 105 (73,4%) atitude inadequada e 130 (90,9%) prática inadequada a respeito do uso do preservativo masculino. Esses desfechos se mostram preocupantes, uma vez que a atividade sexual na adolescência está cada vez mais precoce, gerando, também, maior exposição a riscos, como IST/HIV/Aids e gravidez indesejada, principalmente quando esta prática é realizada sem uso ou com uso inadequado do preservativo.CONCLUSÃO: As fragilidades nas informações obtidas pelos adolescentes refletem o conhecimento inadequado pela quase totalidade deles, seguidas pela atitude e prática inadequadas quanto ao uso do preservativo masculino. Dessa forma, visualiza-se a necessidade de potencializar estratégias educativas, com intuito de ampliar o conhecimento de adolescentes acerca do uso do preservativo masculino e, consequentemente, desenvolver prática adequada desse método contraceptivo.

Seeing like an infrastructure: avidity and difference in algorithmic recommendation

ABSTRACT As the influence of algorithmic systems has grown, critics have come to appreciate that algorithms are not autonomous technical forces, but rather heterogeneous sociotechnical systems. The people who build and maintain these infrastructures play integral roles in their functioning: in the tight and continuous cycles of contemporary software development, the thinking of developers shapes how data drives ‘data-driven’ organizations. This article contributes to contemporary debates on infrastructural politics by describing how the vernacular social theorizing of one group of developers tangles with their technical work. Drawing on ethnographic fieldwork with developers of music recommender systems in the US, I examine how they understand the variability of music listeners. I find that the dominant frame for making sense of listener variation is avidity: a level of enthusiasm for music, which manifests as a willingness to expend effort in finding listening material. For people working in this industry, avidity displaces other ways of understanding human variety – particularly demography. While the technical communities behind these systems were predominantly white and male, they understood the difference that set them apart from most users to be their enthusiasm for music. Centreing avidity provided a way to claim elite cultural status and to avoid talking about demographic diversity. It also reflects the infrastructures through which recommender system developers know and intervene upon their users: avidity is what users look like when seen through interaction logs. Less avid users leave fewer traces, and the goal of the recommender system is to encourage them to leave more. As a result, the figure of the less avid listener serves to justify increasingly rapacious data collection practices.

A review of data collection practices using electromagnetic articulography

This paper reviews data collection practices in electromagnetic articulography (EMA) studies, with a focus on sensor placement. We first introduce electromagnetic articulography as a method. We then focus on existing data collection practices. Our overview is based on a literature review of 905 publications from a large variety of journals and conferences, identified through a systematic keyword search in Google Scholar. The review shows that experimental designs vary greatly, which in turn may limit researchers’ ability to compare results across studies. Finally, we describe an EMA data collection procedure that includes an articulatory-driven strategy for determining where to position sensors on the tongue without causing discomfort to the participant. We also evaluate three approaches for preparing (NDI Wave) EMA sensors reported in the literature with respect to the duration the sensors remain attached to the tongue: 1) attaching out-of-the-box sensors, 2) attaching sensors coated in latex, and 3) attaching sensors coated in latex with an additional latex flap. Results indicate no clear general effect of sensor preparation type on adhesion duration. A subsequent exploratory analysis reveals that sensors with the additional flap tend to adhere for shorter times than the other two types, but that this pattern is inverted for the most posterior tongue sensor.

Physical-Equivalent Privacy

ABSTRACT This article introduces and applies the concept of “physical-equivalent privacy” to evaluate the appropriateness of data collection about library patrons’ use of library-provided e-resources. It posits that as a matter of service equity, any data collection practice that causes e-resource users to enjoy less information privacy than users of an information-equivalent print resource is to be avoided. Analysis is grounded in real-world e-resource-related phenomena: secure (HTTPS) library websites and catalogs, the Adobe Digital Editions data-leak incident of 2014, and use of web trackers on e-resource websites. Implications of physical-equivalent privacy for the SeamlessAccess single-sign-on proposal will be discussed.

Phase I study of F16IL2 antibody–cytokine fusion with very low‐dose araC in acute myeloid leukaemia relapse after allogeneic stem cell transplantation

Phase I study of F16IL2 antibody–cytokine fusion with very low‐dose araC in acute myeloid leukaemia relapse after allogeneic stem cell transplantation

Green data collection strategy in the science domain for environmental sustainability

There is a recognition by modern communities world-wide, that the steadily increasing level of activities in the science domain and population growth, lead to an increase in environmental pressure on critical areas. There is a need, therefore, for stakeholders to critically appraise principles, policies, and practices that could lead to the best practices required for sustainable science activities and environmental management that is driven by green data collection, green principles, and green practices. The models involved in green data collection in science include finance-environment-community-science domain model (FECS), environment-community-science domain-finance model (ECSF), and the science domain, finance, environment-community model (SFEC). SFEC is a novel framework and is being applied in the management of active science domain, abandoned science domain, thus, providing reasonable economic and environmental benefits to the science players and non-science communities. The green data collection strategy is for enhancing social, economic, and environmental resource performance. This strategy has been tested in the Philippines. The formulation of the green data collection strategic framework involved a desktop review of previous and existing local, regional, and national data collection strategies. Consequently, visits are made to active science domain and discussions held with stakeholders. In this paper, the role of information, education, and communication have been revisited and fits adequately into the novel “green data collection model” being advanced for all players in the science domain. The absence of this framework poses the most challenging aspect of prosperity offered by the quality, quantity, diversity, and the sustainability of the science domain. Therefore, this paper has discussed the content of the green data collection strategic plan, anchored on a sustainable-controlled planning cycle. This approach could drive and sustain the harmony between man, science, and the environment.

Nonviral sexually transmitted infections in pregnancy: current controversies and new challenges.

This review provides an update of nonviral, curable sexually transmitted infections (STIs) in pregnancy and summarizes our understanding of the current issues and controversies surrounding risk factors, screening, and treatment of STIs in pregnancy primarily in high-income countries (using the United States and the United Kingdom as examples). The infections covered in this review are syphilis, gonorrhea, chlamydia, trichomoniasis, and Mycoplasma genitalium infections. Overall, limited modern data is available to update researchers and clinicians on the epidemiology and care of STIs in pregnancy. Though common risk factors can be identified among these STIs, like socioeconomic status and inadequate antenatal care, specific screening and treatment challenges vary by geography and pathogen. Wherever available, surveillance data and research evidence are often limited to nonpregnant patients, leading to imperfect pregnancy-specific risk estimates and obstetric lags in the development and adoption of new guidelines. We have identified three areas of opportunity that may enhance the effectiveness of current approaches and inform new ones: improved data collection and evidence-based screening practices; prompt and comprehensive therapy, including partner services, and evaluations of new treatment modalities; and equitable antenatal and sexual healthcare for all pregnant persons and their partners. These findings highlight the need to revisit standards of screening and management of STIs in pregnancy in high-income countries.

Research in forced displacement: guidance for a feminist and decolonial approach

The COVID-19 pandemic has deepened inequities and undermined health, human rights, and gender equality for forcibly displaced populations The ongoing pandemic has also exposed persisting power hierarchies between researchers and forcibly displaced populations These populations experience power asymmetries in their position as the so-called beneficiaries of humanitarian research and action, and are often excluded from participation in decision making about the research intended to benefit their communities Forcibly displaced women and girls are consistently categorised as vulnerable and needing protection or rescuing, which takes away their agency and power of action, while risking exploitation or abuse by the same humanitarian actors that supposedly aim to protect them We believe that applying a feminist lens is not merely about demolishing problematic structures, but also about collaboratively building up new ones for a more just world (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Systematic Analysis of the Global, Regional and National Burden of Kidney Cancer from 1990 to 2017: Results from the Global Burden of Disease Study 2017

BackgroundData on kidney cancer burden are valuable for health-related policy making. ObjectiveTo report the results of the Global Burden of Disease 2017 study on global kidney cancer burden estimates grouped by gender, age, region, country or territory, and sociodemographic index (SDI) from 1990 to 2017. Design, setting, and participantsThis study is based on the Global Burden of Disease database. Outcome measurements and statistical analysisWe report here detailed estimates and temporal trends of the burden estimates of kidney cancer from 1990 to 2017, stratified by gender and age, in 195 countries and territories. We further evaluated the relationship between these estimates and the SDI, a composite indicator of income per person, years of education, and fertility as a measurement of the socioeconomic level of a country/region. The percentage change and estimated annual percentage change of incidence, mortality, and disability-adjusted life years (DALYs) were calculated to quantify temporal trends. Results and limitationsGlobally, age-standardized incidence rates, age-standardized death rates, and DALYs of kidney cancer in males exhibited an increase of 0.387%/yr, 0.345%/yr, and 0.046%/yr, respectively, from 1990 to 2017. This trend was mainly due to the increase in middle and low-middle SDI quintile countries. However, in females, decreasing trends of –0.324%/yr, –0.330%/yr, and –0.669%/yr, respectively, were observed. These trends were mainly due to the decrease in high, high-middle, and middle SDI quintile countries. Study limitations included differences in data collection practices, coding systems, and quality of data sources. ConclusionsThe burden estimate pattern of kidney cancer trends varies widely between genders and throughout the world. Low-middle and middle SDI quintile countries face the highest burden estimates, especially for males. Efforts to increase health care investment are needed in these countries. Patient summaryThe global burden estimate of kidney cancer trends increased in males; however, it decreased in females.

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

BackgroundThe health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and social data at a granular scale, though the effort required to turn these spatially encoded video frames into maps limits sustainability and scalability. In this paper we explore the use of convolution neural networks (CNN) to solve this problem by automatically identifying disease related environmental risks in a series of SV collected from Haiti. Our objective is to determine the potential of machine learning in health risk mapping for these environments by assessing the challenges faced in adequately training the required classification models.ResultsWe show that SV can be a suitable source for automatically identifying and extracting health risk features using machine learning. While well-defined objects such as drains, buckets, tires and animals can be efficiently classified, more amorphous masses such as trash or standing water are difficult to classify. Our results further show that variations in the number of image frames selected, the image resolution, and combinations of these can be used to improve the overall model performance.ConclusionMachine learning in combination with spatial video can be used to automatically identify environmental risks associated with common health problems in informal settlements, though there are likely to be variations in the type of data needed for training based on location. Success based on the risk type being identified are also likely to vary geographically. However, we are confident in identifying a series of best practices for data collection, model training and performance in these settings. We also discuss the next step of testing these findings in other environments, and how adding in the simultaneously collected geographic data could be used to create an automatic health risk mapping tool.

Constructing a queer population? Asking about sexual orientation in Scotland’s 2022 census

ABSTRACT For the first time, Scotland’s 2022 census will ask a question about sexual orientation. Correspondence between National Records of Scotland, the Scottish Parliament’s Culture, Tourism, Europe and External Affairs Committee and campaign groups present insights into decisions made, the uneasy relationship between queer identities and state data collection practices, and the question of who is counted when we count LGBTQ people. Building on Foucault’s critique of projects that construct population knowledge, the census is framed primarily as a tool to facilitate the state’s capacity to govern. My engagement in the design process enabled me to critically examine decisions made about the exclusion of non-binary identities and the use of predictive text technology. These decisions demonstrate how the design process constructed a queer population that ‘made sense’ to the heteronormative majority and ‘designed-out’ queer lives that the state did not wish to bring into being.

Responsible innovation for digital identity systems

Abstract Digital identity (eID) systems are a crucial piece in the digital services ecosystem. They connect individuals to a variety of socioeconomic opportunities but can also reinforce power asymmetries between organizations and individuals. Data collection practices can negatively impact an individual’s right to privacy, autonomy, and self-determination. Protecting individual rights, however, may be at odds with imperatives of profit maximization or national security. The use of eID technologies is hence highly contested. Current approaches to governing eID systems have been unable to fully address the trade-offs between the opportunities and risks associated with these systems. The responsible innovation (RI) literature provides a set of principles to govern disruptive innovations, such as eID systems, toward societally desirable outcomes. This article uses RI principles to develop a framework to govern eID systems in a more inclusive, responsible, and user-centered manner. The proposed framework seeks to complement existing practices for eID system governance by bringing forth principles of deliberation and democratic engagement to build trust amongst stakeholders of the eID system and deliver shared socioeconomic benefits.