A short guide for effective field data collection

An Efficient Workflow for Collecting, Entering, and Proofing Field Data: Harnessing Voice Recording and Dictation Software

Abstract. Georeferenced field data collection has become a popular practice allowing everyone to contribute to mapping objects or reporting events. The spread of mobile devices - capable of recording and sharing location coordinates, media and features while on the go - is primarily accountable for such diffusion. Accordingly, a number of mobile apps and software frameworks have been developed and released to perform field data collection. These frameworks allow to customize and dispatch collection forms as well as to manage contributors and records through web interfaces or database management systems. From the contributors’ perspective, specific mobile client apps need to be installed to access selectively the collection forms and contribute to the data collection on the field using their mobile devices. This operation might inhibit the sporadic contribution of occasional users who may not be willing to install additional software. To overcome this limitation, this work presents the Geo Collector Bot, an alternative software toolkit to empower field data collection projects avoiding the development and/or the installation of a specific mobile app on contributors’ devices. The Geo Collector Bot is a configurable Telegram-based chatbot enabling to dispatch of data collection forms that can be activated and filled in through Telegram chats. The ultimate goal of the presented work is to provide an alternative free and open-source software framework suitable for general-purpose field data collection applications. Development patterns and system architecture are described in detail alongside future improvements and outlooks for the Geo Collector Bot project.

The investigation of multiple sources in household water management is considered overly complicated and time consuming using paper and pen interviewing (PAPI). We assess the advantages of computer-assisted personal interviewing (CAPI) in Pacific Island Countries (PICs). We adapted an existing PAPI survey on multiple water sources and expanded it to incorporate location of water use and the impacts of extreme weather events using SurveyCTO on Android tablets. We then compared the efficiency and accuracy of data collection using the PAPI version (n = 44) with the CAPI version (n = 291), including interview duration, error rate and trends in interview duration with enumerator experience. CAPI surveys facilitated high-quality data collection and were an average of 15.2 min faster than PAPI. CAPI survey duration decreased by 0.55% per survey delivered (p < 0.0001), whilst embedded skip patterns and answer lists lowered data entry error rates, relative to PAPI (p < 0.0001). Large-scale household surveys commonly used in global monitoring and evaluation do not differentiate multiple water sources and uses. CAPI equips water researchers with a quick and reliable tool to address these knowledge gaps and advance our understanding of development research priorities.

Background and Objectives: Teak (Tectona grandis) is one of Thailand's most valuable economic tree species, prized for its exceptional qualities such as durability, aesthetic appeal, and resistance to pests and diseases. Its popularity spans both domestic and international markets, making accurate estimation of merchantable teak volume essential for forest management and the timber industry. Precise volume estimation facilitates accurate economic valuation and minimizes resource losses associated with traditional destructive tree-felling methods. Recent advancements in technology, light detection and ranging or LiDAR, have provided opportunities to enhance the accuracy and efficiency of forest resource data collection. Among these tools, the Terrestrial Laser Scanner (TLS) stands out for its ability to generate high-resolution three-dimensional (3D) models of individual trees and forest stands in a non-destructive method. Unlike traditional methods, TLS enables data collection without damaging trees, offering a sustainable and efficient alternative for forestry applications. This study aimed to develop a methodology for utilizing TLS to construct 3D models of teak trees to estimate merchantable wood volume. The results were compared with the traditional felling method, which is considered the most accurate but destructive. In addition, the study attempts to establish predictive equations and volume tables for estimating merchantable teak volume. These outputs aimed to support sustainable forest management practices, reduce resource wastes, and facilitate the economic valuations of teak for the timber industry. Methods: The study was conducted in the Mae Chang forest plantation, located in Mae Mo district, Lampang province, Thailand. The stratified random sampling was employed to establish 12 sample plots, each sizing 40 m × 40 m. The plots were divided into two age groups based on plantation year: 18 years old (planted in 2006) and 39 years old (planted in 1985), with six plots in each group. Data collection involved using a Faro Focus S150 TLS to scan individual trees in each plot. The TLS data were processed to create detailed 3D tree models for further analysis. Merchantable timber volume was calculated using two methods: the stem curve method and quantitative structure models (QSM), both implemented through the 3D forest software. The TLS-derived volume estimates were validated by felling selected sample trees. The diameters and merchantable lengths of these felled trees were measured, and their volumes were calculated using Huber’s formula. Statistical analysis was performed to compare TLS-derived volumes with traditional felling-based volumes using paired t-tests. The accuracy of TLS-derived estimates was evaluated using root mean square error (RMSE). Additionally, predictive equations for merchantable volume estimation were developed using regression analysis. Two types of equations were created: one using diameter at breast height (DBH) as the sole variable and the other incorporating both DBH and merchantable height (HM). A volume table was constructed based on these equations, enabling non-destructive volume estimation for teak trees of varying DBH and HM ranges. Results: The 3D models generated using TLS provided merchantable volume estimates that closely aligned with volumes derived from felled trees. Statistical analysis revealed no significant differences between TLS-derived volumes and traditional felling-based volumes (p > 0.05). The RMSE values for TLS-derived estimates were 0.02 and 0.03 cubic meters when using the stem curve and QSM methods, respectively, indicating high accuracy. Two predictive equations for merchantable volume estimation were developed. The first equation utilized DBH as a single predictor variable and demonstrated a coefficient of determination (R²) of 0.966. The second equation incorporated both DBH and HM, achieving a higher R² value of 0.988. Validation of these equations using data from felled trees showed mean absolute percentage errors (MAPE) of 17.81% and 11.01%, respectively, confirming their suitability for practical applications. The most accurate equation was subsequently used to construct a merchantable volume table, which estimating based on DBH and HM ranges without requiring tree felling. This table provides a practical tool for forest managers and timber industry stakeholders to assess teak volume efficiently and sustainably. Conclusion: The results of this study demonstrate that TLS is a reliable and effective tool for non-destructive estimation of merchantable teak volume. The 3D models generated using TLS data enable precise volume estimation through methods such as stem curve and QSM, producing results that are statistically comparable to traditional destructive felling methods. The acceptable error margins and high accuracy of TLS-derived estimates highlight its potential as a sustainable alternative to conventional volume estimation techniques. By minimizing the need for destructive sampling, TLS-based methods contribute to sustainable forest management practices, preserving valuable tree resources while maintaining economic productivity. Furthermore, the development of predictive equations and merchantable volume tables offers practical solutions for forest managers and timber industry professionals, streamlining the assessment of merchantable volume and economic valuation. Overall, this study underscores the importance of integrating advanced technologies such as TLS into forestry practices. By improving the accuracy and sustainability of resource assessments, these innovations can support long-term forest conservation and enhance the economic potential of Thailand's teak plantations.

This paper presents the results from a quality and usability analysis of participatory land registration (PaLaR) in Indonesia’s rural areas, focusing on data quality, cost, and time. PaLaR was designed as a systematic community-centered land titling project collecting requisite spatial and legal data. PaLaR was piloted in two communities situated in Tanggamus and Grobogan districts in Indonesia. The research compared spatial data accuracy between two approaches, PaLaR and the normal systematic land registration approach (PTSL) with respect to point accuracy and polygon area. Supplementary observations and interviews were undertaken in order to evaluate the effectiveness of the spatial and legal data collection, as well as logical consistency of the data collected by the community committee, using a mobile application. Although the two pilots showed a lower spatial accuracy than the normal method (PTSL), PaLaR better suited local circumstances and still delivered complete spatial and legal data in a more effective means. The accuracy and efficiency of spatial data collection could be improved through the use of more accurate GNSS antennas and a seamless connection to the national land databases. The PaLaR method is dependent on, amongst other aspects, inclusive and flexible community awareness programs, as well as the committed participation of the community and local offices.

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.KeywordsSensor NetworkSensor NodeWireless Sensor NetworkSteiner TreeSensor ReadingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Field data collection in veterinary and animal behaviour science often faces practical limitations, including time constraints, restricted resources, and difficulties integrating high-quality data capture into real-world clinical workflows. This paper highlights the need for flexible, efficient, and standardised digital solutions that facilitate the collection of multimodal behavioural data in real-world settings. We present a case example using PetsDataLab, a novel cloud-based, “no code” platform designed to enable researchers to create customized apps for efficient and standardised data collection tailored to the behavioural domain, facilitating capture of diverse data types, including video, images, and contextual metadata. We used the platform to develop an app supporting the creation of the Dog Pain Database, a novel comprehensive resource aimed at advancing research on behaviour-based pain indicators in dogs. Using the app, we created a large-scale, structured dataset of dogs with clinically diagnosed conditions expected to be associated with pain and discomfort, including demographic, medical, and pain-related information, alongside high-quality video recordings for future behavioural analyses. To evaluate the app’s usability and its potential for future broader deployment, 14 veterinary professionals tested the app and provided structured feedback via a questionnaire. Results indicated strong usability and clarity, although agreement with using the app in daily clinic life was lower among external testers, pointing to possible barriers to routine integration. This proof-of-concept case study demonstrates the potential of cloud-based platforms like PetsDataLab to bridge research and practice by enabling scalable, standardised, and clinically compatible behavioural data collection. While developed for veterinary pain research, the approach is broadly applicable across behavioural science and supports open science principles through structured, reusable, and interoperable data collection.

Field data collection can be expensive, time consuming, and difficult; insightful research requires statistical analyses supported by sufficient data. Pilot studies and power analysis provide guidance on sampling design but can be challenging to perform, as ecologists increasingly collect multiple types of data over different scales. Despite a growing simulation literature, it remains unclear how to appropriately design data collection for many complex projects. Approaches that seek to achieve realism in decision-making contexts, such as management strategy evaluation and virtual ecologist simulations, can help. For a relatively complex analysis, we develop and demonstrate a flexible simulation approach that informs what data are needed and how long those data will take to collect, under realistic fieldwork constraints. We simulated data collection and analysis under different constraint scenarios that varied in deterministic (field trip length, travel, and measurement times) and stochastic (species detection and occupancy rates and inclement weather) features. In our case study, we fit plant height data to a multispecies, three-parameter, nonlinear growth model. We tested how the simulated data sets, based on the varying constraint scenarios, affected the model fit (parameter bias, uncertainty, and capture rate). Species prevalence in the field exerted a stronger influence on the data sets and downstream model performance than deterministic aspects such as travel times. When species detection and occupancy were not considered, the field time needed to collect an adequate data set was underestimated by 40%. Simulations can assist in refining fieldwork design, estimating field costs, and incorporating uncertainties into project planning. We argue that combining data collection, analysis, and decision-making processes in a flexible virtual setting can help address many of the decisions that field ecologists face when designing field-based research.

Data collection is the core function of underwater acoustic sensor networks (UASNs). Lately, ambulatory data gathering methods are being popularized in real applications. However, due to present mobile underwater data collection investigations that are on the basis of 2-D scenarios, the associated approaches are not suitable for 3-D UASNs. Additionally, mobile-element-assisted data collection usually brings special issues on obstacle avoidance. Accordingly, we propose a probabilistic neighborhood location-point covering set-based data collection algorithm with obstacle avoidance for 3-D UASNs. The proposed algorithm initially generates a space lattice set to establish the probabilistic neighborhood location-point covering set for data collection, so as to optimize the data collection latency. Then, an autonomous underwater vehicle traverses only location points in the constructed covering set with a hierarchical grid-based obstacle avoidance strategy. The simulation experiments are performed to verify the proposed algorithm compared with other existing underwater data collection algorithms. Simulations show that our proposed algorithm achieves better performance in terms of data collection latency, data collection efficiency, and obstacle avoidance.

2019 Julie S Denslow &amp; Peter Ashton Prizes for the Outstanding Articles Published in <i>Biotropica</i>

The monitoring collection system is a system that uses computer, control and other technologies to realize the data storage, collection and monitoring of the field environment. It is widely used in data collection and monitoring in various fields. It often collects data generated by various types of equipment during operation through various sensors and actuators, and these collected data are often the key to analyzing system failures and discovering that equipment is under network attack. In the current environment, attacks occur frequently, and how to protect and utilize these data is very important. Therefore, data security protection system of monitoring and acquisition system Based on blockchain is proposed. Its purpose is to improve data security and better use of data. By combining the blockchain technology, the collected data is stored on the blockchain, and the integrity check mechanism is used to ensure the integrity of the collected data and prevent the data from being tampered with. The association rule base is formed by establishing association rules, and the rule base is used to analyze data, detect data anomalies and feed back the analysis results to the administrator. The data security and system security of the monitoring acquisition system is improved, and the burden of manpower analysis of data is reduced.

During response, oiled shorelines must be surveyed to guide cleanup operations. The Shoreline Cleanup and Assessment Technique (SCAT) is a standard method for conducting these surveys. Multiple field teams often conduct SCAT. SCAT surveys quickly produce a large and complex dataset comprised of SCAT observations, GPS positions, and photographs. In order to guide response decision-making, SCAT field data must be processed and analyzed in a timely manner. Until recently, SCAT and GPS data were collected on standardized paper worksheets, transcribed to electronic form, and then incorporated into maps and other decision-making products. Photographs were not tightly managed alongside SCAT data. Today, with the emergence of robust handheld computing technology, the deficiencies inherent in paper data collection are no longer necessary or acceptable. Paper data collection can be slow, error prone, and lacking quality control and integration with GPS technology. Digital options are available to address these challenges. To improve methods, the Office of Response and Restoration is developing a digital field data collection and management system for SCAT data and photographs composed of: (1) specialized software for efficient SCAT data collection with GPS enabled handheld devices, (2) a relational SCAT database which expedites the synthesis of field data into decision making products, promotes community standards, and supports standard paper worksheet and digital data collection methods, and (3) an image database which allows for the processing, documenting, and sharing of large quantities of digital photographs. For this project, commonly used, readily available, and Open Source computing resources were chosen so that end-users could easily test, adopt, and improve this system.

&lt;p&gt;Taking Jinjiang Wudian City Tourism Cultural and Creative Industrial Park as the research object, we use the case study method to explore the intelligent management system design scheme of the tourism cultural and creative industrial park, and through field interviews, data collection, data coding and result analysis, we deeply analyze the tourism cultural and creative industrial park in Jinjiang Wudian City. Through the methods of field interviews, data collection, coding and result analysis, we analyze the management subjects and components of the management mode of Wudian City Tourism and Creative Industry Park in Jinjiang and identify its existing management mode. On this basis, it embeds technical elements of wisdom tourism, subdivides wisdom management composition modules of tourism cultural and creative industrial park, and finally forms a complete wisdom tourism management system of tourism cultural and creative industrial park.&lt;/p&gt;

Distributed data collection systems are often used in many fields, such as industrial control, power systems, seismic monitoring, and ocean exploration. In a distributed data collection system, the time of data collection and the efficiency of data collection are all related to the accuracy of the data. Subsequent analysis and processing show that the distributed data collection system is a very important performance indicator: this article combines the timing factors of the timing rules in the dataset, and after normalization and expansion, the rules will be edited according to the effective voting rate. Perform a weighted sum calculation on the rules in the edited sub-database to obtain the final timing rules. Finally, the algorithm is implemented by pseudo code. With the rapid development of information technology, we have entered the era of big data. Big data has the characteristics of resource sharing, large storage capacity, and high speed, so it has been widely used in various fields. In the context of the rapid development of the modern economy, the human resources department has provided a relatively complete infrastructure system for many large companies. It can be seen that the work of the personnel department is gradually becoming an important factor in the development of the company. Appropriate implementation of big data in the company’s human resource model can integrate resources into the industry and the company, improving operational efficiency, and laying a solid foundation for the company’s sustainable and long-term development. This article first outlines the hardware system and company management of temporal big data in a distributed environment, discusses in detail the needs of human resource management in companies in the era of big data, and proposes a suitable human resource management model.

Effective data collection is vital for research, especially in Malaysia's electrical and electronics (E&amp;E) manufacturing sector, which significantly impacts the economy. This study investigates challenges faced during field data collection of a pilot study, including accessibility, respondents’ location, and confidentiality concerns. It highlights the importance of diverse engagement strategies, such as employee referrals and HR support, to enhance data reliability. Recommendations include leveraging HR support and broadening respondents’ selection. Addressing these challenges can improve human capital development and capability building in the industry. Future research should focus on refining data collection methodologies in the manufacturing sector.