Traditional Survey Methods Research Articles

Automated method for extracting and analysing the rock discontinuities from point clouds based on digital surface model of rock mass

Often when rock discontinuities with complex distributions occur in steep terrain, it is difficult to rapidly survey and accurately measure their spatial distribution by traditional surveying and mapping methods. This paper presents a methodology for automated extraction of rock discontinuities from a point cloud and the resulting 3D digital surface model of the rock mass. First, feature planes of rock discontinuities are identified and classified using both their orientation and position in a double-nested Mean-shift Clustering Algorithm. Second, the points corresponding to feature planes are extracted using a Region Growth Algorithm and seed points. Finally, geological information is acquired by analysing the geometric features of the extracted sub-set of points from the point cloud. This approach can directly extract planar features associated with joints and it eliminates spurious points in a point cloud associated with objected such as vegetation. A case study of a rock slope along a highway is presented using the proposed method. A sensitivity analysis of relevant clustering parameters in the Mean-shift Clustering Algorithm is conducted to acquire their optimal values and to assess their robustness. The proposed method produces results that agree with the traditional survey methods and greatly improves the survey efficiency.

Using social media to understand drivers of urban park visitation in the Twin Cities, MN

Green space and parks in urban environments provide a range of ecosystem services and public benefits. However, planners and park managers can lack tools and resources to gather local information on how parks are used and what makes them desirable places for recreation and a wide variety of uses. Traditional survey methods to monitor park use and user preferences can be costly, time consuming, and challenging to apply at scale. Here, we overcome this limitation by using geotagged social media data to assess patterns of visitation to urban and peri-urban green space across park systems in the metropolitan area of the Twin Cities, Minnesota, USA. We find that parks with nearby water features, more amenities, greater accessibility from the presence of trails, and that are located within neighborhoods with higher population density, are associated with higher rates of visitation. As cities grow and shifts in demographics occur, more responsive management of public green space will become increasingly important to ensure urban parks provide ecosystem services and meet users’ needs. Using social media data to rapidly assess park use at a lower cost than traditional surveys has the potential to inform public green space management with targeted information on user behavior and values of urban residents.

Effects of perception on public bike-and-ride: A survey under complex, multifactor mode-choice scenarios

Bicycles are an environmentally-friendly, energy-saving mode of transportation. Public bike-and-ride is a term used to describe the use of public bicycles combined with transit as a means of transportation. A new stated preference (SP) design scheme based on the factors respondents felt were most important was proposed to augment the traditional SP design under complex multi-factor and multi-level situations. iPads were used to automatically administer the survey under each design scheme and collect the choice behavior data for public bike-and-ride. The concept of perceived psychological distance was further proposed and used to estimate Logit models. Comparative analysis of these models shows for the traditional SP design scheme, some factors, including perceived psychological distance, have reduced statistically significant effects on public bike-and-ride choice relative to the new SP design scheme. This indicates that in scenarios with complex decisions, the decision-makers would reduce their consideration of some factors and simplify their decision process making the new SP survey design scheme more suitable to analyze their choice behavior in those cases. Model analysis showed that perceived psychological distance for each traveler is the most critical factor during the decision process for travel behavior analysis. Finally, sensitivity analysis was conducted to determine which factors in the new survey design were important to increase the share of public bike-and-ride users. Practical findings for promoting public bike-and-ride use are presented in addition to theoretical references for improving the traditional survey methods.

Non-invasive genetic censusing and monitoring of primate populations.

Knowing the density or abundance of primate populations is essential for their conservation management and contextualizing socio-demographic and behavioral observations. When direct counts of animals are not possible, genetic analysis of non-invasive samples collected from wildlife populations allows estimates of population size with higher accuracy and precision than is possible using indirect signs. Furthermore, in contrast to traditional indirect survey methods, prolonged or periodic genetic sampling across months or years enables inference of group membership, movement, dynamics, and some kin relationships. Data may also be used to estimate sex ratios, sex differences in dispersal distances, and detect gene flow among locations. Recent advances in capture-recapture models have further improved the precision of population estimates derived from non-invasive samples. Simulations using these methods have shown that the confidence interval of point estimates includes the true population size when assumptions of the models are met, and therefore this range of population size minima and maxima should be emphasized in population monitoring studies. Innovations such as the use of sniffer dogs or anti-poaching patrols for sample collection are important to ensure adequate sampling, and the expected development of efficient and cost-effective genotyping by sequencing methods for DNAs derived from non-invasive samples will automate and speed analyses.

Perceptual mapping based on web search queries and consumer forum comments

Consumers’ online activities such as keyword searching and writing reviews can provide valuable information that reflects their perception of the market. This study proposes ways to analyze market structure and draw a perceptual map from the following two types of online data: keyword search and online consumer forum data. We apply our methodology to the imported car brands in South Korea automobile market. The multidimensional scaling (MDS) results provide different consumer insights depending on the nature of data. The inter-brand similarity values derived from the proposed two metrics are shown to be correlated. Especially, using consumer forum data, we apply our metric to analyzing the market structure of two sub-markets: midsize sedan and compact crossover sport utility vehicle (SUV). Furthermore, utilizing the proposed measures, we calculate the prototypicality of a brand and demonstrate its positive effect on sales. Marketing managers can apply our technique to understand the market structure and perform longitudinal studies to monitor consumers’ perceptual changes without conducting a time-consuming, traditional survey method.

Mining Trending Hash Tags for Arabic Sentiment Analysis

People text millions of posts everyday on microblogging social networking especially Twitter which make microblogs a rich source for public opinions, customer’s comments and reviews. Companies and public sectors are looking for a way to measure the public response and feedback on particular service or product. Sentiment analysis is an encouraging technique capable to sense the public opinion in a fast and less cost tactic than traditional survey methods like questionnaires and interviews. Various sentiment methods were developed in many languages, such as English and Arabic with much more studies in the first one. Sometime, hash tags are misleading or may have a title that does not really reflects the subject. Tweets in trend hash tags may contain keyword or topics titles better represent the subject of the hash tag. This research aims at proposing an approach to explore Twitter Hash tag trends to retrieve tweets, group retrieved tweets to learn topics’ profiles, do sentiment analysis to test the subjectivity of tweets then develop a prediction model using deep learning to classify a new tweet to the appropriate topic profile. Arabic hash tags trends have been used to evaluate the proposed approach. The performance of the proposed approach (clustering topics within hashtag trend to learn topics profiles then do sentiment analysis) shows better accuracy than sentiment analysis without clustering the topics.

Monitoring post-release survival of the northern corroboree frog, Pseudophryne pengilleyi, using environmental DNA

Context Translocations are becoming an increasingly important conservation tool to combat rising levels of species extinction. Unfortunately, many translocation efforts fail; yet, the timing and cause of failure often remain unknown. Monitoring individuals in the days and weeks following release can provide valuable information on their capacity to survive this initial hurdle. In Australia, breeding programs have been established for the endangered northern corroboree frog, Pseudophryne pengilleyi, to enable reintroduction to the wild via captive-reared individuals, typically, early life stages such as eggs or juvenile frogs that cannot be monitored via traditional survey methods that target adult frogs (e.g. shout–response). Environmental DNA (eDNA) detects trace amounts of DNA that organisms release into their environment and could provide a means to infer population persistence for wildlife releases and translocations. Aims In the present study, we aim to develop an eDNA assay capable of detecting both sexes of P. pengilleyi across multiple life stages, and use it to monitor their survival. Methods An eDNA assay was developed to target the two corroboree frog species (P. pengilleyi and P. corroboree, the southern corroboree frog) and was tested for its sensitivity and specificity in silico and in vitro. Pseudophryne pengilleyi eggs were released into three naturally occurring ponds and water samples were, subsequently, collected from each pond on several occasions over a period of 78 days. Quantitative polymerase chain reaction was used to detect P. pengilleyi eDNA from water samples. Key Results The developed assay was shown to be sensitive and specific to corroboree frogs. eDNA monitoring of reintroduced P. pengilleyi detected the species’ DNA at three of three release ponds and DNA remained detectable until at least 78 days post-release at two of three ponds. Conclusions We show how the development of a corroboree frog-specific assay allowed us to monitor the post-release survival of P. pengilleyi in naturally occurring pools. Implications eDNA surveys may provide a useful tool to monitor post-release survival of translocated populations in a non-invasive manner, with the potential to identify the timing and causes of failure. Such knowledge can be used to inform the management of translocated populations and future release strategies.

Linking Ground Forest Inventory and NDVI in Mapping above Ground Carbon Stock in Kasane Forest Reserve, Botswana

Quantification of the above ground carbon stock (AGC) is important in sustainable forest management and policy advice on climate change mitigation. Traditional ground vegetation survey methods have been used to provide data for estimation of AGC stock but constrained by inadequate time and often too costly. Remote sensing when combined with few ground collected data has the potential of improving forest resource assessment even though this opportunity has not well been utilised. In this study, we mapped AGC through combination of ground survey data collected from 51 permanent sapling plots with Normalized Difference Vegetation Index (NDVI) derived from Landsat 5 Thematic Mapper image. Linkage of the two data sources was made during a training stage of supervised classification. The overall classification accuracy was 98%, suggesting that reliable estimate of AGC for a large area can be made through combination of medium resolution satellite imagery and few samples from the ground.

Prevalence of food allergy in Vietnam: comparison of web-based with traditional paper-based survey

Background Web-based surveys (WBS) are increasingly applied in epidemiological studies as an appealing alternative to traditional survey methods. Rapid data collection, reduced expenditure and ease of access to large populations are some of the clear advantages of online surveys. However, WBS are still subject to limitations in terms of sample size, response rate and other additional biases compared to traditional survey methods. In the present study, we seek to validate data on food allergy (FA) in two independent sample populations collected from a WBS, and compare it to a paper-based survey (PBS).Methods Data collected from two survey modes were compared by hypothesis testing for independent sample population. The WBS included 1185 respondents, while the PBS included 9039 respondents.Results Overall, the data from the WBS were comparable to the PBS conducted over the same period of time in Vietnamese adults. There were no effects of different survey modes on the lifetime prevalence of doctor-diagnosed FA (5.7%; P = 0.7795, β = 0.05) and IgE-mmcted FA (5.8%; P = 0.9590, β = 0.05). Both surveys showed the dominance of seafood allergy in this population (up to 2.6%), followed by beef allergy. Close correlation was seen in the patterns of FAs and different clinical symptoms. The contribution of family history of allergic diseases and place of residence to FA were confirmed in both surveys.Conclusions The consistency of the WBS results with the PBS indicates a promising application of online surveys as an economic and validated model for future epidemiological studies, specifically in developing countries.

Optimal survey designs for environmental DNA sampling

Abstract Environmental DNA (eDNA) sampling is a promising tool for monitoring cryptic species. Numerous studies have demonstrated that eDNA sampling can achieve higher detection rates than traditional monitoring techniques, such as trapping; however, the consequences of that sensitivity for survey design requirements and resulting survey costs have not been investigated. We demonstrate how site occupancy detection models and optimal survey design methods can be used to evaluate the cost‐efficiency of eDNA sampling vs. traditional survey methods. We apply these approaches to two datasets—one in which eDNA sampling and trapping were conducted simultaneously (paired dataset), and another in which sampling methods were independently deployed (unpaired dataset)—to assess the cost‐efficiency of eDNA sampling for detecting a freshwater mammal: the platypus Ornithorhynchus anatinus. Conditional probabilities of platypus eDNA being captured in a single water sample (paired dataset: 0.838, unpaired: 0.879), and detected in a single water sample by qPCR (paired: 0.892, unpaired: 0.858), were higher than the conditional probability of detecting a platypus with a single trapping visit (paired: 0.470, unpaired: 0.219). eDNA sampling was more cost‐efficient than trapping, regardless of whether the management objective was to (1) minimize the survey budget needed to achieve a particular asymptotic variance of the occupancy estimator, or (2) minimize the survey budget needed to detect a change in occupancy over time. Site occupancy detection models coupled with optimal survey design methods provide a powerful means with which to compare the sensitivity and cost‐efficiency of eDNA sampling vs. traditional sampling methods.

Using of sociological methods in the study of court work

The article analyzes the possibilities of using the most widespread sociological methods in the process of studying various aspects of court activity. Are offered directions of application of different types of methods in research of work of court depending on their purpose. Thus, it is noted that the use of the method of document analysis is effective in the investigation of the personality of the judge, in the analysis of statistics of various indicators of the court, the study of media materials. The method of observation is effectively used in the study of subjects of court sessions, the interests of participants, peculiarities of their behavior, studying the structure of the meeting, observance of norms for its conduct, etc. With the help of the sociological method of interviewing judges and employees of the court apparatus, one can find out the working conditions of judges and employees of the court apparatus, assess the business, professional and personal qualities of the leadership, study the moral and psychological climate in the team, etc. Particular attention is paid to the method of citizen reporting cards, which is based on the traditional survey method. It indicates its essence and differences from the survey method. It is noted, in particular, on the following differences: the feature of the sample (the questionnaire is subject only to participants in court proceedings), the specifics of tools (a citizen reporting card based on certain criteria-measures), practical orientation (use of the results in the administration of a particular court and the judicial system as a whole). It is concluded that the use of sociological research methods in combination with existing methods of judicial analysis will allow obtaining more complete information on the work of the court, diagnose existing problems, and promote the implementation of democratic principles of transparency and openness.

Metagenomic sequencing of environmental DNA reveals marine faunal assemblages from the West Antarctic Peninsula

The West Antarctic Peninsula (WAP) is the fastest warming region in Antarctica where climate impact on the cold-adapted marine ecosystem is already visible. To monitor faunal changes in remote vast bodies of Antarctic waters, efficient and informative tools are essential. High-throughput sequencing of environmental DNA (eDNA) has emerged as one such tool for monitoring biodiversity and ecosystems, as it increases detection sensitivity of taxa, and sampling is often simpler and less costly than traditional collection methods. We collected water samples from four WAP shallow (≤300m) shelf regions, recovered the eDNA therein, and performed metagenomic shotgun sequencing and analyses to determine the effectiveness of this method to assess marine benthic faunal diversity; this includes the detection of deep-water predatory king crabs whose potential shoreward expansion to warming shelves has sparked much concern. Using a customized bioinformatics pipeline, we identified abundant signatures of common benthic invertebrate fauna, endemic notothenioid fishes, as well as lithodid king crabs. We also uncovered species richness and diversity comparable to biological inventories compiled by the use of traditional survey methods, supporting the efficacy of the eDNA shotgun sequencing approach. As the rate of eDNA degradation affects faunal detection sensitivity, we also quantified mitochondrial ND2 gene copies in eDNA derived from a WAP icefish and found ND2 copies persisted to at least 20days in the cold WAP water, much longer than values reported for temperate environments. We propose that eDNA metagenomic sequencing complements traditional sampling, and combining both will enable more inclusive biodiversity detection and faunal change monitoring in the vast Southern Ocean.

Cryospheric Studies in Indian Himalayan and Polar Region: Current Status, Advances and Future Prospects of Remote Sensing

Cryosphere studies are very critical for understanding the behaviour of the global climate system. The cryosphere components such snow cover, glacier ice, ice sheets, icebergs, etc. need to be studied, from Earth’s energy and water balance point of view. However, their quantification and mapping is tedious on field using tradition survey methods. This review paper summarizes the major research work done in field of remote sensing based cryosphere studies in Indian Himalaya and Polar Regions. The traditional survey methods using terrestrial photogrammetry and aerial photography for snow and glacier studies are presented, initially. Then, remote sensing methods of snow and glacier mapping using visible, infrared and thermal spectral bands are presented. Later, the application of these techniques and method in Indian conditions is discussed. The advanced methods of microwave sensors based inversion models for snow physical properties retrieval are also explained. The separate sub-sections are given to highlight the use of remote sensing in glacier and polar studies. In each section, the current status and advances of the technology are discussed. The future prospects of remote sensing technology for each cryosphere theme are highlighted with emphasis to Indian scenario.

Study on extraction method of Panax notoginseng plots in Wenshan of Yunnan province based on decision tree model

The herbs used as the material for traditional Chinese medicine are always planted in the mountainous area where the natural environment is suitable. As the mountain terrain is complex and the distribution of planting plots is scattered, the traditional survey method is difficult to obtain accurate planting area. It is of great significance to provide decision support for the conservation and utilization of traditional Chinese medicine resources by studying the method of extraction of Chinese herbal medicine planting area based on remote sensing and realizing the dynamic monitoring and reserve estimation of Chinese herbal medicines. In this paper, taking the Panax notoginseng plots in Wenshan prefecture of Yunnan province as an example, the China-made GF-1multispectral remote sensing images with a 16 m×16 m resolution were obtained. Then, the time series that can reflect the difference of spectrum of P. notoginseng shed and the background objects were selected to the maximum extent, and the decision tree model of extraction the of P. notoginseng plots was constructed according to the spectral characteristics of the surface features. The results showed that the remote sensing classification method based on the decision tree model could extract P. notoginseng plots in the study area effectively. The method can provide technical support for extraction of P. notoginseng plots at county level.

Study of extracting Peucedanum praeruptorum planted area in Ningguo of Anhui province based on multi-source and multi-phase image

The herbs used as the material for traditional Chinese medicine are always planted in the mountainous area where the natural environment is suitable. As the mountain terrain is complex and the distribution of planting plots is scattered, the traditional survey method is difficult to obtain accurate planting area. It is of great significance to provide decision support for the conservation and utilization of traditional Chinese medicine resources by studying the method of extraction of Chinese herbal medicine planting area based on remote sensing and realizing the dynamic monitoring and reserve estimation of Chinese herbal medicines. In this paper, taking the Peucedanum praeruptorum planted area in Ningguo prefecture of Anhui province as an example, the multispectral remote sensing images that include Landsat-8 with a 30 m resolution and China-made GF-1 with a 16 m resolution were used as data source. Since the spectral characteristics of P. praeruptorum in the two periods are different from those of other crops, the changes of the images at two stages in the same year could be used to extract the P. praeruptorum planted area intercropped in cultivated land. Then the texture and spectral characteristics of young pecan trees were used to extract the P. praeruptorum planted area intercropped in woodland. The results showed that the extracted area of planted P. praeruptorum with the original imagery of 30 m spatial resolution and 16 m spatial resolution was 25 635.43,24 585.43 mu, respectively.

The potential of small-Unmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index

The loss of unimproved grassland has led to species decline in a wide range of taxonomic groups. Agricultural intensification has resulted in fragmented patches of remnant grassland habitat both across Europe and internationally. The monitoring of remnant patches of this habitat is critically important, however, traditional surveying of large, remote landscapes is a notoriously costly and difficult task. The emergence of small-Unmanned Aircraft Systems (sUAS) equipped with low-cost multi-spectral cameras offer an alternative to traditional grassland survey methods, and have the potential to progress and innovate the monitoring and future conservation of this habitat globally. The aim of this article is to investigate the potential of sUAS for rapid detection of threatened unimproved grassland and to test the use of an Enhanced Normalized Difference Vegetation Index (ENDVI). A sUAS aerial survey is undertaken at a site nationally recognised as an important location for fragmented unimproved mesotrophic grassland, within the south east of England, UK. A multispectral camera is used to capture imagery in the visible and near-infrared spectrums, and the ENDVI calculated and its discrimination performance compared to a range of more traditional vegetation indices. In order to validate the results of analysis, ground quadrat surveys were carried out to determine the grassland communities present. Quadrat surveys identified three community types within the site; unimproved grassland, improved grassland and rush pasture. All six vegetation indices tested were able to distinguish between the broad habitat types of grassland and rush pasture; whilst only three could differentiate vegetation at a community level. The Enhanced Normalized Difference Vegetation Index (ENDVI) was the most effective index when differentiating grasslands at the community level. The mechanisms behind the improved performance of the ENDVI are discussed and recommendations are made for areas of future research and study.

Importance of Laser Scanning Resolution in the Process of Recreating the Architectural Details of Historical Buildings

The TLS method (Terrestrial Laser Scanning) may replace the traditional building survey methods, e.g. those requiring the use measuring tapes or range finders. This technology allows for collecting digital data in the form of a point cloud, which can be used to create a 3D model of a building. In addition, it allows for collecting data with an incredible precision, which translates into the possibility to reproduce all architectural features of a building. This data is applied in reverse engineering to create a 3D model of an object existing in a physical space. This study presents the results of a research carried out using a point cloud to recreate the architectural features of a historical building with the application of reverse engineering. The research was conducted on a two-storey residential building with a basement and an attic. Out of the building’s façade sticks a veranda featuring a complicated, wooden structure. The measurements were taken at the medium and the highest resolution using a ScanStation C10 laser scanner by Leica. The data obtained was processed using specialist software, which allowed for the application of reverse engineering, especially for reproducing the sculpted details of the veranda. Following digitization, all redundant data was removed from the point cloud and the cloud was subjected to modelling. For testing purposes, a selected part of the veranda was modelled by means of two methods: surface matching and Triangulated Irregular Network. Both modelling methods were applied in the case of data collected at medium and the highest resolution. Creating a model based on data obtained at medium resolution, both by means of the surface matching and the TIN method, does not allow for a precise recreation of architectural details. The study presents certain sculpted elements recreated based on the highest resolution data with superimposed TIN juxtaposed against a digital image. The resulting model is very precise. Creating good models requires highly accurate field data. It is important to properly choose the distance between the measuring station and the measured object in order to ensure that the angles of incidence (horizontal and vertical) of the laser beam are as straight as possible. The model created based on medium resolution offers very poor quality of details, i.e. only the bigger, basic elements of each detail are clearly visible, while the smaller ones are blurred. This is why in order to obtain data sufficient to reproduce architectural details laser scanning should be performed at the highest resolution. In addition, modelling by means of the surface matching method should be avoided - a better idea is to use the TIN method. In addition to providing a realistically-looking visualization, the method has one more important advantage - it is 4 times faster than the surface matching method.

Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.

BackgroundUptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques.ObjectiveUsing Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance.MethodsFrom tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs’ tweets using latent Dirichlet allocation (LDA).ResultsOur proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks.ConclusionsThe study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases.

A frontier in the use of camera traps: surveying terrestrial squamate assemblages

AbstractThere is an urgent need to improve methods for surveying snakes and lizards (squamates). Currently, fundamental data gaps exist about squamate distributions and abundance in numerous regions. Traditional squamate survey methods are useful, but they are also resource and labour intensive. In recent decades, camera traps have provided researchers an effective, cost‐efficient and minimally invasive survey tool; albeit primarily for birds, mammals and fish. The use of camera traps for reptiles generally, and squamates specifically, has been limited. Yet, recent developments in camera trapping methodology demonstrate how they could be used to survey a squamate assemblage. Although further research is required, these developments are encouraging. Thus, surveying squamates with camera traps is a primary frontier in camera trapping.

The use of advanced web-based survey design in Delphi research.

A discussion of the application of metadata, paradata and embedded data in web-based survey research, using two completed Delphi surveys as examples. Metadata, paradata and embedded data use in web-based Delphi surveys has not been described in the literature. The rapid evolution and widespread use of online survey methods imply that paper-based Delphi methods will likely become obsolete. Commercially available web-based survey tools offer a convenient and affordable means of conducting Delphi research. Researchers and ethics committees may be unaware of the benefits and risks of using metadata in web-based surveys. Discussion paper. Two web-based, three-round Delphi surveys were conducted sequentially between August 2014 - January 2015 and April - May 2016. Their aims were to validate the Australian nurse practitioner metaspecialties and their respective clinical practice standards. Our discussion paper is supported by researcher experience and data obtained from conducting both web-based Delphi surveys. Researchers and ethics committees should consider the benefits and risks of metadata use in web-based survey methods. Web-based Delphi research using paradata and embedded data may introduce efficiencies that improve individual participant survey experiences and reduce attrition across iterations. Use of embedded data allows the efficient conduct of multiple simultaneous Delphi surveys across a shorter timeframe than traditional survey methods. The use of metadata, paradata and embedded data appears to improve response rates, identify bias and give possible explanation for apparent outlier responses, providing an efficient method of conducting web-based Delphi surveys.