Volunteers are a key part of the archaeological labour force and, with the growth of digital datasets, these citizen scientists represent a vast pool of interpretive potential; yet, concerns remain about the quality and reliability of crowd-sourced data. This article evaluates the classification of prehistoric barrows on lidar images of the central Netherlands by thousands of volunteers on the Heritage Quest project. In analysing inter-user agreement and assessing results against fieldwork at 380 locations, the authors show that the probability of an accurate barrow identification is related to volunteer consensus in image classifications. Even messy data can lead to the discovery of many previously undetected prehistoric burial mounds.

Archaeologists frequently use written guidelines such as site manuals, recording forms, and digital prompts during excavations to create usable data within and across projects. Most written guidelines emphasize creating either standardized datasets or narrative summaries; however, previous research has demonstrated that the resulting datasets are often difficult to (re)use. Our study analyzed observations and interviews conducted with four archaeological excavation teams, as well as interviews with archaeological data reusers, to evaluate how archaeologists use and implement written guidelines. These excavation team and reuser experiences suggest that archaeologists need more specific best practices to create and implement written guidelines that improve the quality and usability of archaeological data. We present recommendations to improve written guidelines that focus on a project's methods, end-of-season documentation, and naming practices. We also present a Written Guidelines Checklist to help project directors improve their written guidelines before, during, and after fieldwork as part of a collaborative process. Ideally, these best practices for written guidelines will make it easier for team members and future reusers to incorporate their own and others’ archaeological data into their research.

Hidden consumption is a potential problem when consumers’ expenditure data from household surveys are used in demand analyses. A solution is to collect and use actual consumption data. This study compares demand estimation using consumption and expenditure data and evaluates meat demand in Nigeria. Data are from a nationally representative panel from Nigeria. The results show the elasticities estimated across both datasets were very similar; thus, if the only objective of data collection is to estimate elasticity using a demand system framework, collection of both types of data (consumption and expenditures) may be unnecessary. The elasticity estimates classify poultry, beef, and processed seafood as luxuries, while other meat and unprocessed seafood are classified as necessities. Own-price elasticities from both datasets indicated that poultry, beef, and processed seafood were price-elastic, and poultry was the most price-elastic.

A stream of research on co-authorship, used as a proxy of scholars’ collaborative behavior, focuses on members of a given scientific community defined at discipline and/or national basis for which co-authorship data have to be retrieved. Recent literature pointed out that international digital libraries provide partial coverage of the entire scholar scientific production as well as under-coverage of the scholars in the community. Bias in retrieving co-authorship data of the community of interest can affect network construction and network measures in several ways, providing a partial picture of the real collaboration in writing papers among scholars. In this contribution, we collected bibliographic records of Italian academic statisticians from an online platform (IRIS) available at most universities. Even if it guarantees a high coverage rate of our population and its scientific production, it is necessary to deal with some data quality issues. Thus, a web scraping procedure based on a semi-automatic tool to retrieve publication metadata, as well as data management tools to detect duplicate records and to reconcile authors, is proposed. As a result of our procedure, it emerged that collaboration is an active and increasing practice for Italian academic statisticians with some differences according to the gender, the academic ranking, and the university location of scholars. The heuristic procedure to accomplish data quality issues in the IRIS platform can represent a working case report to adapt to other bibliographic archives with similar characteristics.

The poor assessment of child malnutrition impacts both national-level trends and prioritisation of regions and vulnerable groups based on malnutrition burden. Namibia has reported a high prevalence of malnutrition among children younger than 5 years of age. The present study's aim was to identify the optimal methods for estimating child stunting and wasting prevalence in Namibia using two datasets with suspected poor data quality: Namibia Demographic and Health Surveys (NDHS) (1992–2013) and Namibia Household Income and Expenditure Survey (NHIES), 2015/16. This comparative secondary data analysis used two prevalence estimation methods: WHO flags and PROBIT. WHO flags is the standard analysis method for most national household surveys, while the PROBIT method is recommended for poor quality anthropometry. In NHIES (n 4960), the prevalence of stunting (n 4780) was 30·3 and 20·9 % for the WHO flags and PROBIT estimates, respectively, and the national wasting prevalence (n 4637) was 11·2 and 4·2 %, respectively. The trends in nutritional status from NDHS and NHIES showed improvement across WHO flags and PROBIT until 2013; however, from 2013 to 2016, PROBIT showed smaller increases in stunting and wasting prevalence (2·5 and 0·6 percentage points) than WHO flags (6·6 and 5·0 percentage points). PROBIT identified the Khoisan ethnic group and Northern geographical regions with the highest stunting and wasting prevalence, while WHO flags identified similar prevalence across most groups and regions. The present study supports the recommendation to use PROBIT when poor data quality is suspected for constructing trends, and for targeting regions and vulnerable groups.

The US government invests substantial sums to control the HIV/AIDS epidemic. To monitor progress toward epidemic control, PEPFAR, or the President’s Emergency Plan for AIDS Relief, oversees a data reporting system that includes standard indicators, reporting formats, information systems, and data warehouses. These data, reported quarterly, inform understanding of the global epidemic, resource allocation, and identification of trouble spots. PEPFAR has developed tools to assess the quality of data reported. These tools made important contributions but are limited in the methods used to identify anomalous data points. The most advanced consider univariate probability distributions, whereas correlations between indicators suggest a multivariate approach is better suited. For temporal analysis, the same tool compares values to the averages of preceding periods, though does not consider underlying trends and seasonal factors. To that end, we apply two methods to identify anomalous data points among routinely collected facility-level HIV/AIDS data. One approach is Recommender Systems, an unsupervised machine learning method that captures relationships between users and items. We apply the approach in a novel way by predicting reported values, comparing predicted to reported values, and identifying the greatest deviations. For a temporal perspective, we apply time series models that are flexible to include trend and seasonality. Results of these methods were validated against manual review (95% agreement on non-anomalies, 56% agreement on anomalies for recommender systems; 96% agreement on non-anomalies, 91% agreement on anomalies for time series). This tool will apply greater methodological sophistication to monitoring data quality in an accelerated and standardized manner.

Discrete choice experiments are used to collect data that facilitates measurement and understanding of consumer preferences. A sample of 750 respondents was employed to evaluate a new method of best-worst scaling data collection. This new method decreased the number of attributes and questions while discerning preferences for a larger set of attributes through self-stated preference “filter” questions. The new best-worst method resulted in overall equivalent rates of transitivity violations and lower incidences of attribute non-attendance than standard best-worst scaling designs. The new method of best-worst scaling data collection can be successfully employed to efficiently evaluate more attributes while improving data quality.

Due to the higher costs and selection bias of directly measuring weight, the majority of body weight data are based on survey responses. However, these statements are subject to systematic biases of social desirability; therefore, it is important to evaluate the magnitude of bias through indirect indicators such as rounding of weights. Data from seven rounds of the Spanish National Health Survey from 1995 to 2017 were included in the study, with 113,284 subjects. A general rounding index of weights terminating in 0 and 5, and a partial rounding index that estimated the bias direction, were used to estimate the bias distribution in the self-reporting of body weight. All body weights were systematically rounded, although more strongly in the lower weights and even more so in the higher weights. Lower weights were rounded up, and the higher weights rounded down. Regarding gender, men had higher rounding indices than women. The subjects generally reported a weight closer to the socially desirable weight. Rounding allows estimating the historical evolution of this bias in health and nutrition surveys, having more accurate information by population segments and designing public policies against obesity aimed at the more affected social segments.

When collecting egocentric network data, visual representations of networks can function as a cognitive aid for depicting relationships, helping to maintain an overview of the relationships, and keeping the attention of the interviewees. Additionally, network maps can serve as a narration generator in qualitative and in mixed-methods studies. While varying visual instruments are used for collecting egocentric network data, little is known about differences among visual tools concerning the influence on the resulting network data, the usability for interviewees, and data validity. The article provides an overview of existing visually oriented tools that are used to collect egocentric networks and discusses their functions, advantages, and limitations. Then, we present results of an experimental study where we compare four different visual tools with regard to networks elicited, manageability, and the impact of follow-up questions. In order to assess the manageability of the four tools, we used the thinking aloud method. The results provide evidence that the decision in favor of a specific visual tool (structured vs. unstructured) can affect the size and composition of the elicited networks. Follow-up questions greatly affect the elicited networks and follow-up cues can level out differences among tools. Respondents tend to prefer the concentric circles tool, with some differences in preferences and manageability of tools between participants with low and those with high socioeconomic status. Finally, assets and drawbacks of the four instruments are discussed with regard to data quality and crucial aspects of the data collection process when using visual tools.

Since the introduction of laser-assisted atom probe, analysis of nonconductive materials by atom probe tomography (APT) has become more routine. To obtain high-quality data, a number of acquisition variables needs to be optimized for the material of interest, and for the specific question being addressed. Here, the rutile (TiO2) reference material ‘Windmill Hill Quartzite,’ used for secondary ion mass spectrometry U–Pb dating and laser-ablation inductively coupled plasma mass spectrometry, was analyzed by laser-assisted APT to constrain optimal running conditions. Changes in acquisition parameters such as laser energy and detection rate are evaluated in terms of their effect on background noise, ionization state, hit-multiplicity, and thermal tails. Higher laser energy results in the formation of more complex molecular ions and affects the ionization charge state. At lower energies, background noise and hit-multiplicity increase, but thermal tails shorten. There are also correlations between the acquisition voltage and several of these metrics, which remain to be fully understood. The results observed when varying the acquisition parameters will be discussed in detail in the context of utilizing APT analysis of rutile within geology.

Within the NHS health check (NHSHC) programme, there is evidence of marked inconsistencies and challenges in practice-level self-reporting of uptake. Consequently, we explored the perceptions of those involved in commissioning of NHSHC to better understand the implications for local and national monitoring and evaluation of programme uptake. Semi-structured, one-to-one, telephone interviews (n=15) were conducted with NHSHC commissioners and leads, and were analysed using inductive thematic analysis. NHSHC data were often collected from practices using online extraction systems but many still relied on self-reported data. Performance targets and indicators used to monitor and feedback to general practices varied between localities. Participants reported a number of issues when collecting and reporting data for NHSHC, namely because of opportunistic checks. Owing to the perceived inaccuracies in reporting, there was concern about the credibility and relevance of national uptake figures. The general practice extraction service will be important to fully understand uptake of NHSHC.