RESUMO
Precision livestock farming involves the use of new technologies to improve the performance of farms with low profit margins. Since extensive livestock farming is demanding work requiring continuous supervision, it has not improved as drastically as agriculture. Furthermore, nowadays the world is more aware of the importance of respecting biodiversity and reducing the carbon footprint, for which sustainable animal production is recommended. This is the case of small livestock farms, generally located in unpopulated areas and with difficult generational replacement, due to the tasks involved. The use of robots and other devices equipped with intelligent systems can be useful to the farmer in his daily work. In this way, livestock, specifically flocks of sheep, can be monitored and the presence of potential predators such as the wolf identified. Encountering said predator can be avoided by moving the herd to other, safer pasture areas. This work presents a dataset that contains images and videos that allow detecting, classifying and analyzing flocks of sheep and one of their usual predators, wolves. The dataset includes videos of flocks in different locations, with different lighting conditions and different types of sheep. In addition, it contains images of wolves in natural spaces, which are not usually included in the most common datasets used in computer vision. This dataset can be very useful for the work being carried out in extensive precision livestock farming, to develop intelligent systems, such as a robot, that allow autonomous monitoring and control of a herd. Furthermore, it can be used to analyze animal behavior in the presence of a robot, since some of the images have been acquired with the cameras of a quadruped robot. This dataset has been split into three different Zenodo repositories due to its size. Images of sheep can be downloaded from https://zenodo.org/records/11313800 The images of classes 'Person', 'Wolf' and the depth maps for simulation are publicly available at https://zenodo.org/records/11313966 YOLO annotations are at https://zenodo.org/records/11313165.
RESUMO
Machine learning algorithms are becoming more and more useful in many fields of science, including many areas where computational methods are rarely used. High-performance Computing (HPC) is the most powerful solution to get the best results using these algorithms. HPC requires various skills to use. Acquiring this knowledge might be intimidating and take a long time for a researcher with small or no background in information and communications technologies (ICTs), even if the benefits of such knowledge is evident for the researcher. In this work, we aim to assess how a specific method of introducing HPC to such researchers enables them to start using HPC. We gave talks to two groups of non-ICT researchers that introduced basic concepts focusing on the necessary practical steps needed to use HPC on a specific cluster. We also offered hands-on trainings for one of the groups which aimed to guide participants through the first steps of using HPC. Participants filled out questionnaires partly based on Kirkpatrick's training evaluation model before and after the talk, and after the hands-on training. We found that the talk increased participants' self-reported likelihood of using HPC in their future research, but this was not significant for the group where participation was voluntary. On the contrary, very few researchers participated in the hands-on training, and for these participants neither the talk, nor the hands-on training changed their self-reported likelihood of using HPC in their future research. We argue that our findings show that academia and researchers would benefit from an environment that not only expects researchers to train themselves, but provides structural support for acquiring new skills.