The Burden for High-Quality Online Data Collection Lies With Researchers, Not Recruitment Platforms

Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop models still requires substantial improvement, which is only possible with high-quality (HQ) experimental data. Despite being demanded frequently, such data are still rarely available, especially for Northern European barley production. This study is one of the first to contribute to closing this existing data gap through the targeted collection of HQ experimental data in pluri-annual, multi-location spring barley field trials in Denmark. With this data, the prediction accuracy of Agricultural Production Systems SIMulator significantly increased in contrast to commonly utilized lower quality datasets. Using this data for model calibration resulted in more accurate predictions of in-season plant development and important state variables (e.g. final grain yield and biomass). The model’s prediction accuracy can ultimately be further improved by examining remaining model weaknesses that were discoverable with the HQ data. Process descriptions regarding, for example, early and late leaf development, soil water dynamics and respective plant response appeared to require further improvement. By illustrating the effect of data quality on model performance we reinforce the need for more model-guided field experiments.

The “Report of cancer incidence and mortality in China, 2010” written by the staff of National Central Cancer Registry (NCCR) of China now is published in Annals of Translational Medicine (ATM) (1). In this report the authors described the development of cancer registration in China, methods of data collection and analysis as well as quality control measures in some detail. The information on cancer incidence and mortality in China for 2010 has been presented not only by sex and age groups, but also by urban and rural areas as well as east, middle and west regions with different extent of socio-economic development in this country. The data involved in this Report are important for health leaders, decision makers of China to understand cancer burden in China as a whole and different regions as well for establishing cancer control strategy and plans, the data are also useful for readers within China and abroad who are interested in cancer status in this country. In 1982 the cancer incidence data of urban Shanghai, China for 1975 have appeared in “Cancer Incidence in Five Continents Vol. IV”. After then till 2000 cancer incidence data of Beijing, Shanghai, and several counties of China were presented in the IARC’s publications. In 2008 the National Cancer Registry Program has been set up by the National Health and Family Planning Commission (NHFPC, former Ministry of Health). Up to now 250 local cancer registries have been established as stated in this Report, covering more than 200 million people in China. A total of 145 registries with relatively high quality of cancer data are involved in this report. The great progress of cancer registration achieved in China within a relatively short period of about 30 years is deeply impressive. A few points should be mentioned. Firstly, for the accuracy of estimation of cancer burden for China as a whole, the representative and reliable estimates of sex- and age-specific cancer rates in east, middle and west regions are crucial. So the distributions of demographic, social and economic characteristics of the population covered by the cancer registries, where sex- and age-specific rates for the region are estimated, should be representative for the whole region. If say more cities and urban areas are involved in estimation of regional cancer rates, then the estimated cancer incidence rates for the region would be biased toward upwards. Secondly, rigorously and consistently supervising of cancer data reported is critical for keeping high quality of the data. Data on clinicopathological characteristics of the cases registered should be checked with the medical records of medical institutions where the cases were diagnosed and treated. In the 4th table of this report the proportion of cases with morphological verification (MV%) for pancreatic cancer was 44.40% (45.22% for males, 42.72% for females), which obviously was over-estimated. In more developed areas of China such as urban Shanghai the proportion of cases of pancreatic cancer surgically operated usually is between 20% and 25%. Due to low proportion of cases surgically operated and lack of autopsy estimated MV% for pancreatic cancer should not exceed 20% (even be lower) for the whole country. Temporal trends of cancer incidence and mortality rates are meaningful not only for evaluating effectiveness of and planning cancer control measures, but also for generating hypotheses on etiology and risk factors of cancers to be tested. With the rapid socio-economic development the cancer patterns in different regions of China now are changing remarkably, which creates favorable conditions for investigating role of environmental risk factors including life style in the change of cancer pattern. In addition to annual reports there is an urgent need to accumulate cancer incidence and mortality data collected from some typical registries which are representative for different regions of China and already have cancer data for a relatively long period. Analysis of cancer data by birth cohorts should be applied especially among people of young and middle ages, since at the beginning phase of changing cancer pattern the change in cancer incidence or mortality rates will firstly appear in people of young and middle ages, while rates in old age groups will be still unchanged or even changed in opposite direction. Congratulate the great success in cancer registration in China and expect further progress in this regard.

Many researchers host surveys on online crowdsourcing platforms, such as Amazon’s Mechanical Turk (MTurk) and Prolific. Online platforms promise a convenient way to meet sample size needs while drawing on diverse pools that might not otherwise participate in science. Yet, the quality of data obtained from these platforms is often questionable, so the collection must be closely monitored and reviewed. This study aimed to independently determine which crowdsourcing pool best serves researchers who plan to recruit for online surveys. To achieve this aim, we analyzed data from a recently completed study that drew participants from both MTurk and Prolific. We screened the collected data for both cost and quality, focusing on measures of attention, duration, and internal consistency. We found that only 9.89% of MTurk participants (N = 354) and 43.34% of Prolific participants (N = 345) produced high-quality data; Prolific also proved to be the more affordable option. Researchers considering these platforms for recruitment may weigh the evidence to make decisions when developing their own recruitment strategies. Finally, we highlight best practices for social scientists conducting online research, including additional survey and screening techniques.

Research examining attitudes toward autistic adults has relied on explicit self-report measures, which may be susceptible to socially desirable responding. Because implicit attitudes predict behavioral rejection, understanding both implicit and explicit attitudes toward autistic adults is important. Furthermore, previous research has almost exclusively examined attitudes toward autistic children and has not investigated attitudes toward autistic adults who may also experience prejudice from their peers. We created an implicit association test (IAT) to examine implicit attitudes toward autistic adults. In Study 1, we examined 94 neurotypical adults' (mean [M]age = 31.37 years) implicit attitudes and explicit attitudes toward autistic adults as well as autistic behaviors. In Study 2 (n = 137; M age = 33.43 years), we assessed the same variables using an IAT with descriptive rather than stereotypical words. Participants from both studies demonstrated negative implicit attitudes but positive explicit attitudes toward autistic adults. In Study 2, analyses examining self-reported traits related to autism revealed that more autistic behaviors were associated with less implicit bias. These findings may help explain why autistic adults report discrimination from their peers. The results suggest that there may be benefits in modifying interventions that reduce implicit bias toward other marginalized groups for use with implicit bias against autistic adults. Why was this study done?: The goal of this study was to understand how neurotypical adults in the United States feel and think about autistic adults. Negative attitudes can lead to discrimination against autistic adults or to harmful interactions between autistic and neurotypical adults. Although research has previously examined the attitudes that neurotypical adults have toward autistic adults, most of this work has directly asked people about their attitudes, assessing their explicit, or conscious, attitudes. Neurotypical adults, however, may not be able or willing to admit that they have negative attitudes toward autistic adults. Therefore, it is important to evaluate implicit attitudes, which are underlying attitudes at the unconscious level of awareness.What was the purpose of this study?: This study investigated the implicit and explicit attitudes that neurotypical adults in the general U.S. population have about autistic adults. Assessing both kinds of attitudes is important because each type of attitude predicts different sorts of behaviors toward and judgments of individuals.What did the researchers do?: We conducted this study online using a crowdsourcing method of data collection (Amazon's Mechanical Turk) that gave us access to adults throughout the United States. We developed and administered a reaction-time task to examine implicit attitudes toward autistic adults. In this task, participants rapidly categorized words associated with autism and words not associated with autism as being "good" or "bad." We assessed explicit attitudes with questions about people's knowledge of autism and their liking for autistic adults. Study 1 measured 94 neurotypical adults' (average age = 31.37 years) implicit and explicit attitudes toward autistic adults; Study 2 measured 137 neurotypical adults' (average age = 33.43 years) implicit and explicit attitudes. Whereas Study 1's implicit task used words associated with stereotypes about autistic adults (e.g., extraverted, independent), Study 2 used nonstereotypical words associated with autism (e.g., autistic, spectrum).What were the results of the studies?: Participants in both studies reported positive explicit attitudes but negative implicit attitudes toward autistic adults. In one study, we also found that neurotypical adults with more autistic traits themselves had more positive implicit attitudes toward autistic adults.What do these findings add to what was already known?: Although previous research examined neurotypical adults' explicit attitudes toward autistic adults, the current study demonstrated that neurotypical adults hold negative implicit attitudes toward autistic adults. These findings may help explain why autistic adults experience discrimination from neurotypical adults. Furthermore, our findings suggest that having more autistic traits can lead to a better understanding of the behaviors associated with autism.What are the potential weaknesses in the study?: Limitations of the study were that we collected the data online rather than in person and we only included neurotypical adults as participants.How will these findings help autistic adults now or in the future?: These results shed light on underlying reasons for the potential negative judgments and discrimination that autistic adults face from neurotypical adults. These findings should encourage policy makers to design and implement training programs to reduce neurotypical adults' negative attitudes toward autistic adults.

The systematic collection and analysis of high-quality advanced breast cancer (ABC) data is necessary to advance understanding, optimize care, and improve patient outcomes. High-quality data enables understanding of treatment effectiveness, thereby facilitating the development of innovative therapies. ABC data may also help to counter stigma, by demonstrating that many living with the disease continue to contribute meaningfully to society. Data is an essential tool in highlighting global inequities and advocating to overcome them, and prevalence data is key to determining the burden of ABC worldwide, informing healthcare policies and investment in ABC care.This manuscript reviews global efforts to improve the collection and analysis of ABC data over the past decade. It highlights the advances and persistent challenges to high-quality data collection and proposes actions for the decade ahead. It summarizes research conducted for the ABC Global Alliance's Global Decade Report 2.0. The main findings are: a) New methodologies yield more accurate estimates of the number of people with ABC; b) Data linkage initiatives reveal ABC prevalence far higher than earlier projections; c) ABC-focused registries have advanced data collection globally over the decade; d) High-quality ABC data has been shown to drive treatment access and policy change; e) Persistent gaps in recurrence data and registries remain.The findings from the ABC Global Alliance's Global Decade Report 2.0 have informed the development of a new ABC Global Charter. The ABC Global Charter 2.0 defines ten new achievable and measurable goals for the decade 2025–2035, aiming at improving the lives of people living with ABC worldwide.

e18711 Background: High-quality data is needed to assess the effectiveness and impact of quality improvement collaboratives (QICs). The first Mexico in Alliance with St. Jude Golden Hour Collaborative (MAS Collaborative) ran from May 2019 to November 2020 in 23 hospitals across Mexico and improved the percentage of febrile pediatric hematology-oncology patients (P-HOP) presenting to the emergency department (ED) who receive the first dose of antibiotics in ≤60min from 39% to 78%. This study aimed to evaluate the quality of the data collected during the first MAS Collaborative and inform changes to second, larger-scale MAS Collaborative. Methods: Data quality was determined by data availability throughout the reporting . A complete sequence of was required for inclusion in the analysis. Results and data quality reports were created retrospectively and reviewed by three expert panels to better understand challenges related to data collection. A focus group with MAS Collaborative participants was conducted to review the data collection workflows and identify opportunities for improvement. Results: 2,103 febrile events in P-HOP were reported; 180 (8.6%) events were excluded, 96 (4.6%) informed the baseline, and 1,827 (86.8%) the implementation period. While data availability was excellent for service outcome and process, data availability for other elements of the reporting cascade was lower: 85% for adherence to the institutional guide, 71% for ICU transfer, 70% for sepsis, infections, and blood cultures, 68% for death, and 66% for critical interventions. Experts recommended narrowing the operational definition of critical interventions to those relevant to managing sepsis prior to transfer to the ICU (to assess access challenges), providing more intensive training to teams on the operational definitions for the clinical outcome measures, and more closely monitoring data completeness and quality. The focus group uncovered the need to reduce the number of documented times, differentiate the data collection process for physical or digital patient records, simplify other required variables and operational definitions, and to use a case form for data collection. Additional changes included explicit separation of service and clinical effectiveness measures, using a different software for data reporting and implementing ongoing data validation practices. Conclusions: This study highlights important challenges with the collection of high-quality clinical effectiveness data in the context of QICs in real-word settings. Distinction between service and clinical measures, robust measurement training, and data collection practices that accommodate varied workflows are provided as suggestions to improve data quality and to allow for a more accurate evaluation of the effectiveness and impact of the second MAS Collaborative.

High-quality food composition data are indispensable for improved decision-making in food security, health policy formulation, food labeling, diet formulation, agricultural policymaking, nutrition research, and many other nutrition-related activities. The optimisation of dietary patterns is a powerful tool to reduce the impact of malnutrition on a population’s health and well-being. Many countries in resource-poor settings lack a framework for developing and managing food composition data appropriate for these purposes. In the article, an overview of available food composition tables in Africa and the origin, use and limitations of theses tables are discussed. It is important that those working on any nutrition-related activity for resource-poor settings understand the limitations of current food composition data. Production of high-quality data requires the harmonization and adoption of international standards and guidelines across Africa. Moreover, continuity in the production, compilation and management of high-quality food composition data is challenged by suboptimal capacity building in terms of organizational, institutional and legal framework development. In this perspective article, the authors deliberate on challenges with a focus on Africa, while discussing new advances in food composition activities. Opportunities (such as the Internet of Things (IoT), wearable devices, natural language processing (NLP) and other machine learning techniques) to improve existing resources must be more actively explored and supported.

Facility managers are required to collect high-quality data to achieve corrective maintenance actions. Current facility management (FM) information systems are complex and provide high-quality data. However, they lack interoperability and visualization capabilities. The goal of this study is to improve the quality of data collected that is required for corrective maintenance by utilizing visualization and interoperability capabilities of building information modeling (BIM). To achieve that, an approach that implements industry foundation classes (IFC) BIM to link and present alarms reported by FM systems, such as building energy management systems (BEMS) and building automation systems (BAS), with related data from computerized maintenance management systems (CMMS) was developed and validated on a typical university building. The results showed an efficiency increase in high-quality maintenance data collection. The proposed approach supplements the existing body of knowledge in the FM domain by pr...

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass‐participation internet‐based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high‐quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state‐of‐the‐art analytical methods, and well‐supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Aim: MET exon 14 (METex14) skipping occurs in 3-4% of non-small-cell lung cancer (NSCLC) cases. Low frequency of this alteration necessitated open-label, single-arm trials to investigate MET inhibitors. Since broad MET biomarker testing was only recently introduced in many countries, there is a lack of historical real-world data from patients with METex14 skipping NSCLC receiving conventional therapies. Given the rarity of this population and limitations of existing real-world data sources, the MOMENT registry aims to prospectively collect uniform, comprehensive, high-quality data from patients with METex14 skipping advanced NSCLC treated in routine clinical practice, which can support clinical and regulatory decision making. Patients & methods: MOMENT is a multinational, non-interventional disease registry collecting data on patients with METex14 skipping advanced NSCLC receiving any systemic anticancer therapy. Newly diagnosed patients and those already receiving treatment are eligible. Patients with previous participation in a clinical trial can be included if they receive at least one subsequent therapy line in a routine clinical setting. Eligible systemic treatment includes all available anticancer therapies (approved, conditionally approved or provided through Early Access). Data collection includes biomarker testing results, demographics, baseline clinical characteristics, treatment details and effectiveness, safety information and imaging. Registry site inclusion is dependent on confirmation that local METex14 skipping detection methods are sufficient to confirm METex14 skipping status. MOMENT is currently active at more than 60 sites across Europe and North America and approximately 700 patients are expected to be enrolled within the next 4years. The first patient was enrolled on 4October 2022. After completion of data collection, MOMENT data can be shared with external parties to conduct non-interventional studies. Discussion/conclusion: The MOMENT registry collects comprehensive, high-quality real-world data from patients with METex14 skipping advanced NSCLC receiving systemic anticancer treatment in a routine clinical setting, to enable future studies informing regulatory decisions and optimal care for this rare population. Clinical Trial Registration: NCT05376891 (ClinicalTrials.gov); EUPAS47602 (EU PAS register no.).

High-quality data is crucial in back-analysis of rockburst data and the development of empirical design methods. Data collection and reporting is currently predominantly a manual process (creating a two-pass system) with limited organisation or standardisation. To facilitate the collection, management and analysis of high-quality rockburst damage data, a damage mapping application was developed. This application is designed for offline use with tablet devices, creating a single-pass system. The data captured by the application includes information on location, rock mass conditions, installed ground support and corrosion, falls of ground, damage and photos. The application was designed with four goals in mind: consistency, speed, comprehensiveness and simplicity. To achieve these goals, a number of custom-made widgets were employed for optimal data input. Once input is complete, the data is synchronised to a server on the mine network and is then available for further analysis with an mXrap app.

Over the past decades, wearable and implantable devices have demonstrated great potential for a wide range of personalized health monitoring and therapeutic applications. This special issue primarily focuses on functional and electronic materials, sensors technologies and capabilities, and the associated energy solutions for wearable and implantable devices toward healthcare applications. We have collected 17 reviews, four research articles, and one perspective, all of which are within the scope of this area and cover the topics in breadth and depth.

Recent literature has highlighted how citizen science approaches can engage volunteers, expand scientific literacy, and accomplish targeted research objectives. However, there is limited information on how specific recruitment, retention, and engagement strategies enhance scientific outcomes. To help fill this important information gap, we detail the use of various approaches to engage citizen scientists in the collection of precipitation phase data (rain, snow, or mixed). In our study region, the Sierra Nevada and Central Basin and Range of California and Nevada near Lake Tahoe, a marked amount of annual precipitation falls near freezing. At these air temperatures, weather forecasts, land surface models, and satellites all have difficulty correctly predicting and observing precipitation phase, making visual observations the most accurate approach. From January to May 2020, citizen scientists submitted timestamped, geotagged observations of precipitation phase through the Citizen Science Tahoe mobile phone application. Our recruitment strategy included messaging to winter, weather, and outdoor enthusiasts combined with amplification through regional groups, which resulted in over 199 citizen scientists making 1,003 ground-based observations of rain, snow, and mixed precipitation. We enhanced engagement and retention by targeting specific storms in the region through text message alerts that also allowed for questions, clarifications, and training opportunities. We saw a high retention rate (88%) and a marked increase in the number of observations following alerts. For quality control of the data, we combined various meteorological datasets and compared to the citizen science observations. We found that 96.5% of submitted data passed our quality control protocol, which enabled us to evaluate rain-snow partitioning patterns. Snow was the dominant form of precipitation at air temperatures below and slightly above freezing, with both ecoregions expressing a 50% rain-snow air temperature threshold of 4.2°C, a warmer value than what would be incorporated into most land surface models. Thus, the use of a lower air temperature threshold in these areas would produce inaccuracies in event-based rain-snow proportions. Overall, our high retention rate, data quality, and rain-snow analysis were supported by the recruitment strategy, text message communication, and simplicity of the survey design. We suggest other citizen science projects may follow the approaches detailed herein to achieve their scientific objectives.

<p>ORFEUS (Observatories and Research Facilities for European Seismology) is a non-profit foundation that promotes seismology in the Euro-Mediterranean area through the collection, archival and distribution of seismic waveform data, metadata and closely related products. The data and services are collected or developed at national level by more than 60 contributing Institutions in Pan-Europe and further developed, integrated, standardized, homogenized and promoted through ORFEUS. Among the goals of ORFEUS are: (a) the development and coordination of waveform data products; (b) the coordination of a European data distribution system, and the support for seismic networks in archiving and exchanging digital seismic waveform data; (c) the encouragement of the adoption of best practices for seismic network operation, data quality control and data management; (d) the promotion of open access to seismic waveform data, products and services for the broader Earth science community.  These goals are achieved through the development and maintenance of services targeted to a broad community of seismological data users, ranging from earth scientists to earthquake engineering practitioners. Two Service Management Committees (SMCs) are consolidated within ORFEUS devoted to managing, operating and developing (with the support of one or more Infrastructure Development Groups): (i) the European Integrated waveform Data Archive (EIDA; https://www.orfeus-eu.org/data/eida/); and (ii) the European Strong-Motion databases (SM; https://www.orfeus-eu.org/data/strong/). A new SMC is being formed to represent the community of European mobile pools. Products and services for computational seismologists are also considered for integration in the ORFEUS domain. ORFEUS services currently provide access to the waveforms acquired by ~ 10,000 stations in Pan-Europe, including dense temporary experiments, with strong emphasis on open, high-quality data. Contributing to ORFEUS data archives means long-term archival, state-of-the-art quality control, improved access and increased  usage. Access to data and products is ensured through state-of-the-art information and communications technologies, with strong emphasis on federated web services that considerably improve seamless user access to data gathered and/or distributed by ORFEUS institutions. The web services also facilitate the automation of downstream products. Particular attention is paid to adopting clear policies and licences, and acknowledging the crucial role played by data providers / owners, who are part of the ORFEUS community. There are significant efforts by ORFEUS participating Institutions to enhance the existing services to tackle the challenges posed by the Big Data Era, with emphasis on data quality, improved user experience, and implementation of strategies for scalability, high-volume data access and archival. ORFEUS data and services are assessed and improved through the technical and scientific feedback of a User Advisory Group (UAG), comprised of European Earth scientists with expertise encompassing a broad range of disciplines. All ORFEUS services are developed in coordination with EPOS and are largely integrated in the EPOS Data Access Portal. ORFEUS is one of the founding Parties and fundamental pillars of EPOS Seismology. This contribution presents the current products and services of ORFEUS and introduces the planned key future activities. We aim at stimulating Community feedback about the current and planned ORFEUS strategies.</p>