Digitalising behavioural data collection through cloud-based technology in veterinary science and beyond

Animal welfare science is a relatively new scientific discipline, evolving mostly from within veterinary medicine over the latter half of the twentieth century into an independent specialty in its own right. Originally, the field of study was heavily focused on animal behavior (ethology), but it has emerged into a truly multi- and inter-disciplinary science, encompassing such sciences as behavior, physiology, pathology, health, immunology, endocrinology, and neuroscience, and influenced by personal and societal ethics. The first academic organization devoted to the scientific study of animal welfare was established in 1966 as the society for veterinary ethology (SVE), demonstrating its veterinary roots by being then affiliated with the British Veterinary Association. The world’s first Professor of Animal Welfare was appointed 20 years later at the University of Cambridge’s Department of Clinical Veterinary Medicine, and in 1991, the SVE became the International Society for Applied Ethology, in recognition of its geographical spread and its evolution from veterinary medicine. Over the last quarter of a century, there has been further expansion of the field and now animal welfare science is represented in many universities’ veterinary medicine and animal science departments across the world. Animal welfare science has become part of the core curriculum for many veterinary degrees, is a recognized specialty qualification within the veterinary professions of Europe, USA, and Australia and courses in animal welfare science as a stand-alone discipline are offered worldwide at Bachelor’s, Master’s, and Doctorate degree levels. Within research, there have been similar patterns of expansion and a spread from a heavy focus on farm animal welfare to the welfare of zoo, laboratory, and companion animals and the impacts of humans on wildlife. There continue to be studies that compare the welfare of populations within systems, but there is also more attention given to gaining in-depth knowledge of the welfare of individual animals, knowing that populations are not homogenous and that individuals within the same system may be experiencing quite different welfare states. We not only continue to use “traditional” welfare indicators but also work to develop novel indicators for use in experimental settings or in the field. As our fundamental knowledge base increases, we look for increasing application and we respond to challenges that arise from our own research questions and findings and from societal needs. In this paper, I will focus on a number of the areas that I see represent Grand Challenges within our discipline.

Effects of exercise on adrenocortical function.

Attention has recently been focused on the use of ISD behavioral data for specifying aircrew training requirements. However, behavioral data are not sufficient for the actual specification of design of equipment, which frequently represents the major dollar investment for training programs. The paper presents a methodology for designing training equipment. This methodology in some ways goes beyond the collection of behavioral data, and in other ways ayoids the weaknesses of the behavioral data approach. The emphasis is on ensuring that device fidelity requirements are specifically correlated with training requirements. Four critical areas are addressed: (1) acquisition of behavioral data; (2) determination of training capabilities; (3) performance measurement; and (4) special requirements for crew coordination training.

Learning motivated by electrical stimulation of the brain.

Field data collection is a foundation of ecological research, but it can be challenging and error‐prone. Researchers planning and undertaking fieldwork for the first time are especially vulnerable to logistical issues that can be avoided with more guidance. We offer guidelines to enhance the accuracy and efficiency of field data collection. Our guidance takes the prospective field ecologist through five steps: (1) identifying which variables should be recorded (focusing on your scientific question, but considering opportunities for additional data collection); (2) designing the data structure (understanding data structure, assigning useful IDs and avoiding confusing characters); (3) preparing a data collection protocol (building in redundancy, choosing a data collection technology, clean data recording and taking additional notes); (4) undertaking a pilot test (including data collection and digitising, communicating with the team); (5) following the protocol (assigning a decision‐maker, noting any changes, following routines and backing up data). This article is motivated by our own trial‐and‐error experiences and those of our colleagues and students, and a desire to help future researchers to avoid them. Our intention is to streamline the planning and field stages of data collection to avoid common pitfalls, thereby saving money, time and resources and supporting the whole data life cycle. Field data collection occurs across disciplines looking to understand the natural world, and each field excursion poses its own unique challenges. However, these guidelines can be useful as a starter for researchers to support open, reproducible, efficient and effective field data collection.

Computational methods such as those used in computational advertising research are often considered at odds with experimental methods. This divergent view of these two paths has frequently led to researchers examining these methodologies in isolation. In this article, we take the view that each path has value and that the strengths of one can inform the other. To this end, we provide a guide to conducting research that focuses on four strengths inherent to experimental research—the principle of control, random assignment of treatments, the exploration of underlying mechanisms, and transparency—that can provide lessons to those using computational advertising data. Similarly, we focus on three strengths inherent to computational advertising research—the collection of behavioral data, the realistic nature in which the data are obtained, and the ability to study dynamic changes over time—that can provide lessons on how to enhance experimental research. In doing so, we assist researchers in strengthening their study designs regardless of their preferred methodology.

Wireless body area networks (WBANs) connected with wearable internet of things (WIoT) offer useful features including sensory information collection, analysis, and transmission for continuous behavior monitoring of autism spectrum disorder (ASD) patients. Due to users’ mobility and time-driven sensed data, data collection becomes very difficult. The current approach employs cluster-based multi-objective path-optimized data collection mechanisms that have experienced hotspot issues leading to loss of energy and coverage problems near the base stations. This work presents the high energy and reliable sensory and behavior data collection (HERSBDC) mechanism to address the research difficulties. To ensure network coverage, the HERSBDC initially provides a new uneven clustering mechanism. Next, multi-objective-based cluster head (CH) selection metrics are proposed. The final step is the creation of a multi-objective routing path to gather vital ASD data more reliably and energy-efficiently. Comparing the proposed HERSBDC algorithm to the low energy adaptive cluster-hierarchy (LEACH)-based, and distributed energy-efficient clustering and routing (DECR) methods, the simulation results demonstrate that the HERSBDC mechanism achieves a much better lifetime by 62.28% and 11.89%, the delivery ratio by 15.04% and 9.51%, with minimal delay by 52.65%, and 9.65%, and routing overhead by 32.05%, and 42.65%, respectively.

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices.

Implanted or anchored telemetry devices are valuable tools in cetacean research, especially for species whose habitats and ranging patterns limit the collection of movement and behavioural data by less invasive methods. In this study, 130 Low‐Impact Minimally Percutaneous External Transmitter (LIMPET) tags were deployed on Cuvier’s beaked whales and fin whales. LIMPET tags are small Type A (anchored) tags which are remotely deployed and secured on or near the dorsal fin using medical‐grade titanium darts. They transmitted data via satellite for weeks to months (mean = 34 days, range 1–239 days). Most of these tagged whales (n = 123) were photo‐identified at deployment. Sixty‐one were photographed after tag loss, with the proportion of the resighted tagged whales meeting or exceeding the inter‐annual resighting rates of their populations. Thirty‐seven resighted whales (16 fin and 21 beaked) had adequate quality photos of the attachment site for visual assessment of healing on at least one day, over periods from 1–4,779 days (13.1 years) after the end of tag transmission. Most tag sites had resolved as one or two small (< 2 cm) marks or shallow depressions within 2–3 years. LIMPET tags placed outside the dorsal fin (n = 34) had significantly shorter transmission durations but were not associated with larger residual marks. Swelling, a notable concern in other tag follow‐up studies, was observed in five whales, three of which were sighted with retained darts that had separated from the external tag package. It took 2–4 years to shed these retained darts. In two cases, the darts appear to have migrated through the fin and exited on the contralateral side. Retention petals from LIMPET tag darts may be left behind; if so, results suggest they are likely encapsulated or ejected without leaving a visible mark. These findings suggest that LIMPET tags are generally a safe and effective option for collecting extended movement and behavioural data from these and similar species.

Ecological Determinants of Social Systems: Perspectives on the Functional Role of Roosting Ecology in the Social Behavior of Tent-Roosting Bats

Animal behavior and welfare science can form the basis of zoo animal management. However, even basic behavioral data are lacking for the majority of amphibian species, and species-specific research is required to inform management. Our goal was to develop the first ethogram for the critically endangered frog Xenopus longipes through observation of a captive population of 24 frogs. The ethogram was applied to produce a diurnal activity budget and to measure the behavioral impact of a routine health check where frogs were restrained. In the activity budget, frogs spent the vast majority of time swimming, resting in small amounts of time devoted to feeding, foraging, breathing, and (in males) amplexus. Using linear mixed models, we found no effect of time of day or sex on baseline behavior, other than for breathing, which had a greater duration in females. Linear mixed models indicated significant effects of the health check on duration of swimming, resting, foraging, feeding, and breathing behaviors for all frogs. This indicates a welfare trade-off associated with veterinary monitoring and highlights the importance of non-invasive monitoring where possible, as well as providing candidates for behavioral monitoring of acute stress. This investigation has provided the first behavioral data for this species which can be applied to future research regarding husbandry and management practices.

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.

Contributions of Behavioral Primatology to Veterinary Science and Comparative Medicine

As pulgas são insetos achatados lateralmente, sem asas, pertencentes ao Filo Arthropoda, à classe Insecta e à ordem Siphonaptera. Na medicina veterinária, as pulgas são habitualmente encontradas em cães e gatos, porém, elas também vivem em uma variedade de outros animais domésticos e pequenos selvagens. A subespécie predominante que parasita cães e gatos é a Ctenocephalides felis felis, conhecida como pulga do gato, causando no animal ação irritativa e inflamatória, além de transmitir patógenos, onde estas causam perturbação e estresse aos animais, provocados pelas suas picadas e movimentos. A pesquisa foi realizada com tutores de animais de estimação, residentes em duas avenidas do Setor Pestana, em Guaraí Tocantins. A coleta de dados foi realizada através de questionários aplicados a todos os tutores de animais e continha seis perguntas fechadas de múltiplas escolhas. Acerca do questionário sobre o método de prevenção e controle de pulgas nos animais, foi constatado que 54% dos responsáveis utilizam medicamentos oriundos de farmácias. Com isso, conclui-se que prevalece o número de donos de animais que tem consciência dos danos a saúde que as pulgas podem causar a seus animais bem como o impacto econômico.

The first 3 rounds of the National Social Life, Health, and Aging Project (NSHAP) were in-person. Preparing for Round Four (R4), NSHAP began developing ways to collect complex questionnaire and biomeasure data remotely. R4 was scheduled to begin in 2020, but due to the coronavirus pandemic, NSHAP delayed R4 data collection and instead conducted a study on respondents' experiences during the pandemic, as well as pretests to strengthen NSHAP's remote data collection capability. This paper describes the methodology, results, and lessons learned from these efforts, which were undertaken as a bridge between NSHAP's all-in-person past and multimode future. The coronavirus disease 2019 (COVID-19) Study was a multimode survey of NSHAP respondents to assess the impact of the pandemic. The multimode approach allowed evaluation of the feasibility of using different modes of data collection with older adults. NSHAP adapted its in-person questionnaire for phone and web administration and conducted pretests of the full phone questionnaire and sections of the web questionnaire. The project developed and tested a "BioBox," a kit containing all the supplies and instructions for respondents to self-collect biomeasures remotely. The BioBox was tested through an in-lab and in-home pilot, followed by 2 larger-scale pretests. The COVID-19 Study and pretests achieved NSHAP respondent participation in remote questionnaire and biomeasure collection, despite being accustomed to fully in-person data collection. Our findings and experiences will inform the collection of NSHAP data in future rounds and could inform other panel studies of older adults considering multimode data collection.