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1.
J Pak Med Assoc ; 71(Suppl 7)(11): S67-S69, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34793432

RESUMO

Introduction: Crowdsourcing pools together dispersed information that is considered public knowledge in an area, to form realistic estimates about the area, or to identify new ideas. The technique can be extremely helpful to develop estimates of public health indicators such as catchment area populations or healthcare providers; however, such uses must be scientifically validated. METHODS: We divided the community into 1040 discrete segments of similar lengths of streets (called spots) and then randomly selected 605 of these spots for crowdsourcing. Local respondents were asked to estimate the maximum and the minimum population residing in those spots. Five informants were interviewed per spot. Median values for the maximum and minimum were averaged to arrive at an estimate for the spot's population. Estimates for all spots were added together to arrive at the population of the community. One hundred spots from the 597 crowdsourced spots were revisited to conduct a household census as a "gold standard". RESULTS: Spots where both crowdsourcing and census estimates were computed had a crowdsource population estimate of 19,255 versus a census estimate of 18,119 - a variation of 5.9% (p: <0.001). However, within spot variation was a mean of 25%. CONCLUSIONS: Crowdsourcing communities for public knowledge information can yield more accurate information about public health indicators such as populations. In turn these estimates can help to better understand public health programme coverage. Other applications to consider may be missed children for immunization or schooling, deaths or births in communities or to identify total formal or informal healthcare providers in a community.


Assuntos
Crowdsourcing , Criança , Humanos , Saúde Pública
2.
Stud Health Technol Inform ; 287: 109-113, 2021 Nov 18.
Artigo em Inglês | MEDLINE | ID: mdl-34795092

RESUMO

Recent studies demonstrated that comparative analysis of stem cell research data sets originating from multiple studies can produce new information and help with hypotheses generation. Effective approaches for incorporating multiple diverse heterogeneous data sets collected from stem cell projects into a harmonized project-based framework have been lacking. Here, we provide an intelligent informatics solution for integrating comprehensive characterizations of stem cells with research subject and project outcome information. Our platform is the first to seamlessly integrate information from iPSCs and cancer stem cell research into a single platform, using a multi-modular common data element framework. Heterogeneous data is validated using predefined ontologies and stored in a relational database, to ensure data quality and ease of access. Testing was performed using 103 published, publicly-available iPSC and cancer stem cell projects conducted in clinical, preclinical and in vitro evaluations. We validated the robustness of the platform, by seamlessly harmonizing diverse data elements, and demonstrated its potential for knowledge generation through the aggregation and harmonization of data. Future aims of this project include increasing the database size using crowdsourcing and natural language processing functionalities. The platform is publicly available at https://remedy.mssm.edu/.


Assuntos
Crowdsourcing , Células-Tronco Pluripotentes Induzidas , Bases de Dados Factuais , Processamento de Linguagem Natural , Pesquisa com Células-Tronco
3.
Sensors (Basel) ; 21(21)2021 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-34770276

RESUMO

Like Smart Home and Smart Devices, Smart Navigation has become necessary to travel through the congestion of the structure of either building or in the wild. The advancement in smartphone technology and incorporation of many different precise sensors have made the smartphone a unique choice for developing practical navigation applications. Many have taken the initiative to address this by developing mobile-based solutions. Here, a cloud-based intelligent traveler assistant is proposed that exploits user-generated position and elevation data collected from ubiquitous smartphone devices equipped with Accelerometer, Gyroscope, Magnetometer, and GPS (Global Positioning System) sensors. The data can be collected by the pedestrians and the drivers, and are then automatically put into topological information. The platform and associated innovative application allow travelers to create a map of a route or an infrastructure with ease and to share the information for others to follow. The cloud-based solution that does not cost travelers anything allows them to create, access, and follow any maps online and offline. The proposed solution consumes little battery power and can be used with lowly configured resources. The ability to create unknown, unreached, or unrecognized rural/urban road maps, building structures, and the wild map with the help of volunteer traveler-generated data and to share these data with the greater community makes the presented solution unique and valuable. The proposed crowdsourcing method of knowing the unknown would be an excellent support for travelers.


Assuntos
Crowdsourcing , Pedestres , Sistemas de Informação Geográfica , Humanos , Smartphone
4.
J Med Internet Res ; 23(10): e19789, 2021 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-34673528

RESUMO

BACKGROUND: Wearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices. OBJECTIVE: This study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data. METHODS: This was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses. RESULTS: A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income. CONCLUSIONS: Most wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).


Assuntos
Crowdsourcing , Dispositivos Eletrônicos Vestíveis , Adolescente , Adulto , Estudos Transversais , Humanos , Internet , Inquéritos e Questionários , Estados Unidos
5.
JAMA Netw Open ; 4(10): e2124946, 2021 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-34633425

RESUMO

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.


Assuntos
Algoritmos , Benchmarking , COVID-19/diagnóstico , Regras de Decisão Clínica , Crowdsourcing , Hospitalização/estatística & dados numéricos , Aprendizado de Máquina , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , COVID-19/epidemiologia , COVID-19/terapia , Teste para COVID-19 , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Prognóstico , Curva ROC , Índice de Gravidade de Doença , Washington/epidemiologia , Adulto Jovem
6.
J Med Internet Res ; 23(10): e26280, 2021 10 29.
Artigo em Inglês | MEDLINE | ID: mdl-34714248

RESUMO

BACKGROUND: College campuses in the United States have begun implementing smoke and tobacco-free policies to discourage the use of tobacco. Smoke and tobacco-free policies, however, are contingent upon effective policy enforcement. OBJECTIVE: This study aimed to develop an empirically derived web-based tracking tool (Tracker) for crowdsourcing campus environmental reports of tobacco use and waste to support smoke and tobacco-free college policies. METHODS: An exploratory sequential mixed methods approach was utilized to inform the development and evaluation of Tracker. In October 2018, three focus groups across 2 California universities were conducted and themes were analyzed, guiding Tracker development. After 1 year of implementation, users were asked in April 2020 to complete a survey about their experience. RESULTS: In the focus groups, two major themes emerged: barriers and facilitators to tool utilization. Further Tracker development was guided by focus group input to address these barriers (eg, information, policing, and logistical concerns) and facilitators (eg, environmental motivators and positive reinforcement). Amongst 1163 Tracker reports, those who completed the user survey (n=316) reported that the top motivations for using the tool had been having a cleaner environment (212/316, 79%) and health concerns (185/316, 69%). CONCLUSIONS: Environmental concerns, a motivator that emerged in focus groups, shaped Tracker's development and was cited by the majority of users surveyed as a top motivator for utilization.


Assuntos
Crowdsourcing , Política Antifumo , Humanos , Internet , Política Pública , Fumaça , Estudantes , Tabaco , Uso de Tabaco , Estados Unidos , Universidades
7.
J Acoust Soc Am ; 150(4): 2952, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34717500

RESUMO

Salience is the quality of a sensory signal that attracts involuntary attention in humans. While it primarily reflects conspicuous physical attributes of a scene, our understanding of processes underlying what makes a certain object or event salient remains limited. In the vision literature, experimental results, theoretical accounts, and large amounts of eye-tracking data using rich stimuli have shed light on some of the underpinnings of visual salience in the brain. In contrast, studies of auditory salience have lagged behind due to limitations in both experimental designs and stimulus datasets used to probe the question of salience in complex everyday soundscapes. In this work, we deploy an online platform to study salience using a dichotic listening paradigm with natural auditory stimuli. The study validates crowd-sourcing as a reliable platform to collect behavioral responses to auditory salience by comparing experimental outcomes to findings acquired in a controlled laboratory setting. A model-based analysis demonstrates the benefits of extending behavioral measures of salience to broader selection of auditory scenes and larger pools of subjects. Overall, this effort extends our current knowledge of auditory salience in everyday soundscapes and highlights the limitations of low-level acoustic attributes in capturing the richness of natural soundscapes.


Assuntos
Percepção Auditiva , Crowdsourcing , Atenção , Encéfalo , Humanos
8.
J Biomed Inform ; 122: 103902, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34481057

RESUMO

The effectiveness of machine learning models to provide accurate and consistent results in drug discovery and clinical decision support is strongly dependent on the quality of the data used. However, substantive amounts of open data that drive drug discovery suffer from a number of issues including inconsistent representation, inaccurate reporting, and incomplete context. For example, databases of FDA-approved drug indications used in computational drug repositioning studies do not distinguish between treatments that simply offer symptomatic relief from those that target the underlying pathology. Moreover, drug indication sources often lack proper provenance and have little overlap. Consequently, new predictions can be of poor quality as they offer little in the way of new insights. Hence, work remains to be done to establish higher quality databases of drug indications that are suitable for use in drug discovery and repositioning studies. Here, we report on the combination of weak supervision (i.e., programmatic labeling and crowdsourcing) and deep learning methods for relation extraction from DailyMed text to create a higher quality drug-disease relation dataset. The generated drug-disease relation data shows a high overlap with DrugCentral, a manually curated dataset. Using this dataset, we constructed a machine learning model to classify relations between drugs and diseases from text into four categories; treatment, symptomatic relief, contradiction, and effect, exhibiting an improvement of 15.5% with Bi-LSTM (F1 score of 71.8%) over the best performing discrete method. Access to high quality data is crucial to building accurate and reliable drug repurposing prediction models. Our work suggests how the combination of crowds, experts, and machine learning methods can go hand-in-hand to improve datasets and predictive models.


Assuntos
Crowdsourcing , Aprendizado de Máquina , Reposicionamento de Medicamentos
9.
J Acoust Soc Am ; 150(2): 1390, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34470275

RESUMO

Although the use of nontraditional settings for speech perception experiments is growing, there have been few controlled comparisons of online and laboratory modalities in the context of speech intelligibility. The current study compares outcomes from three web-based replications of recent laboratory studies involving distorted, masked, filtered, and enhanced speech, amounting to 40 separate conditions. Rather than relying on unrestricted crowdsourcing, this study made use of participants from the population that would normally volunteer to take part physically in laboratory experiments. In sentence transcription tasks, the web cohort produced intelligibility scores 3-6 percentage points lower than their laboratory counterparts, and test modality interacted with experimental condition. These disparities and interactions largely disappeared after the exclusion of those web listeners who self-reported the use of low quality headphones, and the remaining listener cohort was also able to replicate key outcomes of each of the three laboratory studies. The laboratory and web modalities produced similar measures of experimental efficiency based on listener variability, response errors, and outlier counts. These findings suggest that the combination of known listener cohorts and moderate headphone quality provides a feasible alternative to traditional laboratory intelligibility studies.


Assuntos
Crowdsourcing , Percepção da Fala , Cognição , Humanos , Idioma , Inteligibilidade da Fala
10.
BMC Infect Dis ; 21(1): 914, 2021 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-34488673

RESUMO

OBJECTIVES: Antimicrobial resistance (AMR) is a significant threat to global public health. Many medical curricula have limited clinical cases and materials focused on AMR, yet enhanced AMR education and training are needed to support antimicrobial stewardship programmes. We used crowdsourcing methods to develop open-access, learner-centred AMR resources. Crowdsourcing is the process of having a large group, including experts and non-experts, solve a problem and then share solutions with the public. METHODS: We organised a global crowdsourcing contest soliciting AMR-related multiple-choice questions, infographics, and images. First, we convened a diverse steering committee group to finalise a call for entries. Second, we launched the contest and disseminated the call for entries using social media, blog posts, email, and an in-person event. Partner institutions included two digital healthcare platforms: Figure 1® and Ding Xiang Yuan. Both organizations serve as online communities for healthcare specialists and professionals to report and comment on clinical information. At the end of the call, solicited entries were screened for eligibility and judged on merit and relevance to AMR learning and education. Exceptional entries were recognised, awarded prizes, and further reviewed for sharing with the public via open-access platforms. RESULTS: We received 59 entries from nine countries. These included 54 multiple-choice questions, four infographics, and one image. Eligible entries (n = 56) were reviewed and assigned a score on a 1-10 scale. Eight entries received mean scores greater than 6.0 and were selected as finalists. The eight finalist entries consisted of three infographics and five multiple-choice questions. They were disseminated through open-access publications and online medical communities. Although we launched a global call, we relied heavily on medical student groups and the entries received were not entirely globally representative. CONCLUSIONS: We demonstrate that crowdsourcing challenge contests can be used to identify infectious disease teaching materials. Medical educators and curriculum developers can adapt this method to solicit additional teaching content for medical students.


Assuntos
Crowdsourcing , Estudantes de Medicina , Antibacterianos , Farmacorresistência Bacteriana , Humanos
11.
Sensors (Basel) ; 21(17)2021 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-34502771

RESUMO

Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.


Assuntos
Crowdsourcing , Simulação por Computador , Humanos
12.
PLoS One ; 16(9): e0257823, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34587206

RESUMO

Fungal hyphal growth and branching are essential traits that allow fungi to spread and proliferate in many environments. This sustained growth is essential for a myriad of applications in health, agriculture, and industry. However, comparisons between different fungi are difficult in the absence of standardized metrics. Here, we used a microfluidic device featuring four different maze patterns to compare the growth velocity and branching frequency of fourteen filamentous fungi. These measurements result from the collective work of several labs in the form of a competition named the "Fungus Olympics." The competing fungi included five ascomycete species (ten strains total), two basidiomycete species, and two zygomycete species. We found that growth velocity within a straight channel varied from 1 to 4 µm/min. We also found that the time to complete mazes when fungal hyphae branched or turned at various angles did not correlate with linear growth velocity. We discovered that fungi in our study used one of two distinct strategies to traverse mazes: high-frequency branching in which all possible paths were explored, and low-frequency branching in which only one or two paths were explored. While the high-frequency branching helped fungi escape mazes with sharp turns faster, the low-frequency turning had a significant advantage in mazes with shallower turns. Future work will more systematically examine these trends.


Assuntos
Crowdsourcing/métodos , Fungos/crescimento & desenvolvimento , Técnicas Analíticas Microfluídicas/instrumentação , Ascomicetos/crescimento & desenvolvimento , Basidiomycota/crescimento & desenvolvimento , Fenômenos Biológicos , Fungos/classificação , Hifas/classificação , Hifas/crescimento & desenvolvimento , Especificidade da Espécie
13.
Artigo em Inglês | MEDLINE | ID: mdl-34360073

RESUMO

Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.


Assuntos
Crowdsourcing , Smartphone , Calibragem , Humanos , Ruído/efeitos adversos , Som
14.
Sensors (Basel) ; 21(15)2021 Jul 23.
Artigo em Inglês | MEDLINE | ID: mdl-34372243

RESUMO

Crowdsourcing is a new mode of value creation in which organizations leverage numerous Internet users to accomplish tasks. However, because these workers have different backgrounds and intentions, crowdsourcing suffers from quality concerns. In the literature, tracing the behavior of workers is preferred over other methodologies such as consensus methods and gold standard approaches. This paper proposes two novel models based on workers' behavior for task classification. These models newly benefit from time-series features and characteristics. The first model uses multiple time-series features with a machine learning classifier. The second model converts time series into images using the recurrent characteristic and applies a convolutional neural network classifier. The proposed models surpass the current state of-the-art baselines in terms of performance. In terms of accuracy, our feature-based model achieved 83.8%, whereas our convolutional neural network model achieved 76.6%.


Assuntos
Crowdsourcing , Redes Neurais de Computação , Humanos , Aprendizado de Máquina
15.
Sensors (Basel) ; 21(15)2021 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-34372384

RESUMO

Due to the increasing relevance of spatial information in different aspects of location-based services, various methods are used to collect this information. The use of crowdsourcing due to plurality and distribution is a remarkable strategy for collecting information, especially spatial information. Crowdsourcing can have a substantial effect on increasing the accuracy of data. However, many centralized crowdsourcing systems lack security and transparency due to a trusted party's existence. With the emergence of blockchain technology, there has been an increase in security, transparency, and traceability in spatial crowdsourcing systems. In this paper, we propose a blockchain-based spatial crowdsourcing system in which workers confirm or reject the accuracy of tasks. Tasks are reports submitted by requesters to the system; a report comprises type and location. To our best knowledge, the proposed system is the first system that all participants receive rewards. This system considers spatial and non-spatial reward factors to encourage users' participation in collecting accurate spatial information. Privacy preservation and security of spatial information are considered in the system. We also evaluated the system efficiency. According to the experiment results, using the proposed system, information accuracy increased by 40%, and the minimum time for reviewing reports by facilities reduced by 30%. Moreover, we compared the proposed system with the current centralized and distributed crowdsourcing systems. This comparison shows that, although our proposed system omits the user's history to preserve privacy, it considers a consensus-based approach to guarantee submitted reports' accuracy. The proposed system also has a reward mechanism to encourage more participation.


Assuntos
Blockchain , Crowdsourcing , Humanos , Privacidade , Recompensa , Tecnologia
16.
PLoS Comput Biol ; 17(8): e1009274, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34370726

RESUMO

Recent advancements in in situ methods, such as multiplexed in situ RNA hybridization and in situ RNA sequencing, have deepened our understanding of the way biological processes are spatially organized in tissues. Automated image processing and spot-calling algorithms for analyzing in situ transcriptomics images have many parameters which need to be tuned for optimal detection. Having ground truth datasets (images where there is very high confidence on the accuracy of the detected spots) is essential for evaluating these algorithms and tuning their parameters. We present a first-in-kind open-source toolkit and framework for in situ transcriptomics image analysis that incorporates crowdsourced annotations, alongside expert annotations, as a source of ground truth for the analysis of in situ transcriptomics images. The kit includes tools for preparing images for crowdsourcing annotation to optimize crowdsourced workers' ability to annotate these images reliably, performing quality control (QC) on worker annotations, extracting candidate parameters for spot-calling algorithms from sample images, tuning parameters for spot-calling algorithms, and evaluating spot-calling algorithms and worker performance. These tools are wrapped in a modular pipeline with a flexible structure that allows users to take advantage of crowdsourced annotations from any source of their choice. We tested the pipeline using real and synthetic in situ transcriptomics images and annotations from the Amazon Mechanical Turk system obtained via Quanti.us. Using real images from in situ experiments and simulated images produced by one of the tools in the kit, we studied worker sensitivity to spot characteristics and established rules for annotation QC. We explored and demonstrated the use of ground truth generated in this way for validating spot-calling algorithms and tuning their parameters, and confirmed that consensus crowdsourced annotations are a viable substitute for expert-generated ground truth for these purposes.


Assuntos
Crowdsourcing/métodos , Processamento de Imagem Assistida por Computador/métodos , Transcriptoma , Automação , Hibridização In Situ , RNA/química , Análise de Sequência de RNA/métodos , Fluxo de Trabalho
18.
Am J Health Behav ; 45(4): 695-700, 2021 07 26.
Artigo em Inglês | MEDLINE | ID: mdl-34340736

RESUMO

Objectives: Amazon's Mechanical Turk (MTurk) has become a popular data collection tool in the addiction sciences. We sought to examine the psychometric properties of the AUDIT-C in an MTurk sample. Methods: Data collection was facilitated via MTurk (N=309; 52.8% female), where an online survey assessed demographic data, alcohol use behaviors (AUDIT-C), and alcohol-related consequences (CAPS-r). Responses to the AUDIT-C were subjected to a principal component analysis to evaluate the structure of the 3-item measure. Alcohol-related consequences were used as a measure of convergent validity. Results: Results provided evidence for a single-factor structure. Pearson's product-moment correlation coefficients between AUDIT-C scores and CAPS-r scores produced statistically significant results (r = 0.51, p < .001). Using biological sex-based suggested cut-off scores for the AUDIT-C, hazardous drinkers (M = 19.15, SD = 8.27) demonstrated statistically significantly higher levels of alcohol-related consequences than non-hazardous drinkers (M = 12.56, SD = 5.35; t(295) = -8.34, p < .001). Reliability and stability statistics demonstrated strong internal consistency. Conclusions: Results demonstrate the sound psychometric properties of the AUDIT-C for an MTurk sample and provide evidence supporting the use of AUDIT-C as a screening tool to be employed with digitally accessed populations to identify and reach hazardous drinkers.


Assuntos
Consumo de Bebidas Alcoólicas , Comportamento Aditivo , Crowdsourcing , Psicometria , Consumo de Bebidas Alcoólicas/efeitos adversos , Feminino , Humanos , Masculino , Reprodutibilidade dos Testes , Inquéritos e Questionários
19.
J Commun Disord ; 93: 106135, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34214758

RESUMO

PURPOSE: Independent laypersons are essential in the assessment of intelligibility in persons with dysarthria (PWD), as they reflect intelligibility limitations in the most ecologically valid way, without being influenced by familiarity with the speaker. The present work investigated online crowdsourcing as a convenient method to involve lay people as listeners, with the objective of exploring how to constrain the expected variability of crowd-based judgements to make them applicable in clinical diagnostics. METHOD: Intelligibility was assessed using a word transcription task administered via crowdsourcing. In study 1, speech samples of 23 PWD were transcribed by 18 crowdworkers each. Four methods of aggregating the intelligibility scores of randomly sampled panels of 4 to 14 listeners were compared for accuracy, i.e. the stability of the resulting intelligibility estimates across different panels, and their validity, i.e. the degree to which they matched data obtained under controlled laboratory conditions ("gold standard"). In addition, we determined an economically acceptable number of crowdworkers per speaker which is needed to obtain accurate and valid intelligibility estimates. Study 2 examined the robustness of the chosen aggregation method against downward outliers due to spamming in a larger sample of 100 PWD. RESULTS: In study 1, an interworker aggregation method based on negative exponential weightings of the scores as a function of their distance from the "best" listener's score (exponentially weighted mean) outperformed three other methods (median value, arithmetic mean, maximum). Under cost-benefit considerations, an optimum panel size of 9 crowd listeners per examination was determined. Study 2 demonstrated the robustness of this aggregation method against spamming crowd listeners. CONCLUSION: Though intelligibility data collected through online crowdsourcing are noisy, accurate and valid intelligibility estimates can be obtained by appropriate aggregation of the raw data. This makes crowdsourcing a suitable method for incorporating real-world perspectives into clinical dysarthria assessment.


Assuntos
Crowdsourcing , Percepção da Fala , Disartria/diagnóstico , Humanos , Inteligibilidade da Fala , Medida da Produção da Fala
20.
PLoS One ; 16(7): e0253371, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34197498

RESUMO

BACKGROUND: The Covid-19 pandemic has had unprecedented effects on individual lives and livelihoods as well as on social, health, economic and political systems and structures across the world. This article derives from a unique collaboration between researchers and museums using rapid response crowdsourcing to document contemporary life among the general public during the pandemic crisis in Sweden. METHODS AND FINDINGS: We use qualitative analysis to explore the narrative crowdsourced submissions of the same 88 individuals at two timepoints, during the 1st and 2nd pandemic waves, about what they most fear in relation to the Covid-19 pandemic, and how their descriptions changed over time. In this self-selected group, we found that aspects they most feared generally concerned responses to the pandemic on a societal level, rather than to the Covid-19 disease itself or other health-related issues. The most salient fears included a broad array of societal issues, including general societal collapse and fears about effects on social and political interactions among people with resulting impact on political order. Notably strong support for the Swedish pandemic response was expressed, despite both national and international criticism. CONCLUSIONS: This analysis fills a notable gap in research literature that lacks subjective and detailed investigation of experiences of the general public, despite recognition of the widespread effects of Covid-19 and its' management strategies. Findings address controversy about the role of experts in formulating and communicating strategy, as well as implications of human responses to existential threats. Based on this analysis, we call for broader focus on societal issues related to this existential threat and the responses to it.


Assuntos
COVID-19 , Crowdsourcing , Medo , COVID-19/epidemiologia , COVID-19/psicologia , Humanos , Estudos Longitudinais , Pandemias , Suécia/epidemiologia , Fatores de Tempo
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