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1,321 results for “Navigation”
The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China
<p>The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.</p>
A neural circuit for wind-guided olfactory navigation
<p>Data for Matheson et al., 2022 </p>
Cognitive experience alters cortical involvement in goal-directed navigation
<p><span>Neural activity in the mammalian cortex has been studied extensively during decision tasks, and recent work aims to identify under what conditions cortex is actually necessary for these tasks. We studied whether cognitive experience, beyond sensory or motor learning, affects cortical involvement in goal-directed navigation. To this aim, we first trained different cohorts of mice on either a simple goal-directed navigation task ("simple task"), or one of two complex tasks involving delay periods ("delay task") or rule reversals ("switching task") in virtual reality. After task learning, we optogenetically inhibited various cortical areas on a subset of trials while mice performed a given task to assess the necessity of each area for task performance. We found overall minor cortical necessity for the simple task, but large necessity of cortical association areas (specifically of posterior parietal cortex and retrosplenial cortex) for each complex task. We then permanently transitioned mice with complex task experience to the simple task, again inhibiting cortical areas on a subset of trials. Crucially, we found that these mice heavily relied on cortical association areas for simple task performance, unlike mice without prior complex task experience. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in goal-directed navigation.</span></p> <p><span>In this dataset, we include both behavioral data for the initial task training period, as well as behavioral data for all subsequent optogenetic inhibition experiments. Details about the data structure are available in the accompanying Readme.txt files. </span></p>
IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO (Extended)
<p>IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO - Extended Version</p> <p>A. De Luca, L. Muratore and N. Tsagarakis. Italian institute of Technology, IIT.</p> <p> </p> <p>Hybrid Primitive Based planner executed on the real CENTAURO robot. The planner searches for a plan to reach the goal assigned with the available primitives. In this experiment, the primitives are: whole robot driving, and single wheel action. The latter can be divided into single-wheel driving and stepping, based on the elevation difference during the trajectory of the wheel. In addition, single-wheel driving can be merged to obtain the Macro "Reshape", speeding up the execution.</p>
IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO
<p>IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO</p> <p>A. De Luca, L. Muratore and N. Tsagarakis. Italian institute of Technology, IIT.</p> <p> </p> <p>Hybrid Primitive Based planner executed on the real CENTAURO robot. The planner searches for a plan to reach the goal assigned with the available primitives. In this experiment, the primitives are: whole robot driving, and single wheel action. The latter can be divided into single-wheel driving and stepping, based on the elevation difference during the trajectory of the wheel. In addition, single-wheel driving can be merged to obtain the Macro "Reshape", speeding up the execution.</p>
Sir Eian O' Duel, Elizabethan Navigator
Sir Francis Drake, Sir John Hawkins, Sir Thomas Cavendish and Sir Walter Raleigh were famous captains and explorers of the 16th century, but none of there ilk could read a map, let alone tackle the mathematics / astronomy of Dead reckoning. behind the scenes stands the often eccentric scholars entrusted with the title of Pilot-Navigator. Source: Objaverse 1.0 / Sketchfab
Lock (water navigation) TM2027
This wooden model was built by Jonas Norberg in 1761. It was created according to Christopher Polhem´s construction of a lock (water navigation) in Stockholm. Norberg worked closely with Polhem and knew the construction in detail. The model is part of the Swedish Royal Model Chamber and was used instead of drawings in case the construction needed to be repaired. The model is now part of the collections of the Swedish National Museum of Science and Technology. TM 2027 (TEKS0021755) [TM2027](https://digitaltmuseum.se/021026304527/stockholms-sluss-modell"2027") Read more about [Polhem](https://en.wikipedia.org/wiki/Christopher_Polhem"polhem") on Wikipedia. Source: Objaverse 1.0 / Sketchfab
Bilateral Human-Robot Control for Semi-Autonomous UAV Navigation
<p><strong>This video demonstrates the work towards a novel control architecture for UAV navigation. In general, UAVs are not easy to operate and skilled pilots are required for a good performance in manual flight. However, currently it is impossible to capture every possible situation an UAV could encounter in the autonomous control. To avoid overly complicated control, a semi-autonomous control approach can be used, so the drone is partly autonomously and partly manually piloted. The novelty of the approach presented here is in the way this semi-autonomy is defined. </strong></p> <p><strong>As the UAV regularly operates autonomously, it is not desirable to switch to manual control in dangerous procedures. Instead, a more supervisory method of control can be applied in which the UAV is always controlled by the onboard computer, but the boundaries of control are controlled by the operator. Whenever a situation requires bigger risks, the operator is informed requested by the drone for help, which he\she can offer by softening certain boundaries of the UAV.</strong></p> <p><strong>This video demonstrates the concept.</strong></p>
Data for: Social-ecological predictors of spotted hyena navigation through a shared landscape
<p>Human-wildlife interactions are increasing in severity due to climate change and proliferating urbanization. Regions where human infrastructure and activity are rapidly densifying or newly appearing constitute novel environments in which wildlife must learn to coexist with people, thereby serving as ideal case studies with which to infer future human-wildlife interactions in shared landscapes.<em> </em>As a widely reviled and behaviorally plastic apex predator, the spotted hyena (<em>Crocuta crocuta</em>) is a model species for understanding how large carnivores navigate these human-caused 'landscapes of fear' in a changing world. Using high-resolution GPS collar data, we applied resource selection functions and step selection functions to assess spotted hyena landscape navigation and fine-scale movement decisions in relation to social-ecological features in a rapidly developing region comprising two protected areas: Lake Nakuru National Park and Soysambu Conservancy, Kenya. We then used camera trap imagery and Barrier Behavior Analysis (BaBA) to further examine hyena interactions with barriers. Our results show that environmental factors, linear infrastructure, human-carnivore conflict hotspots, and human tolerance were all important predictors for landscape-scale resource selection by hyenas, while human experience elements were less important for fine-scale hyena movement decisions. Hyena selection for these characteristics also changed seasonally and across land management types. Camera traps documented an exceptionally high number of individual spotted hyenas (234) approaching the national park fence at 16 sites during the study period, and BaBA results suggested that hyenas perceive protected area boundaries' semi-permeable electric fences as risky but may cross them out of necessity. Our findings highlight that the ability of carnivores to flexibly respond within human-caused landscapes of fear may be expressed differently depending on context, scale, and climatic factors. These results also point to the need to incorporate societal factors into multiscale analyses of wildlife movement to effectively plan for human-wildlife coexistence.</p>
Supplement: quadT: How Profilers Can Help Navigate Type Migration
<ul> <li>uquadT.tar.gz = boundary profile (aka contract profile)</li> <li>17-* = performance data</li> <li>uquadT_profile.tar.xz_* = statistical profile <ul> <li>This data is divided across 4 files thanks to the unix <code>split</code> command. Use <code>cat</code> to combine, then untar.</li> </ul> </li> </ul> <p>Reports on 3^14 = 4,782,969 configurations of quadT, so there should be that many files in the two profile folders and that many lines of running times in the .out file.</p>
YOLO-based Drone Navigation for Pallet Identification
<p>This upload contains the following:</p> <ul> <li>The training data of 40 pallets and the resulting weights for four YOLOv4-tiny models, two trained to detect pallets and two trained to detect pallet blocks</li> <li>The documentation of 280 flights using these four models as a means of navigation for a micro drone, in the form of logs, images and feature vectors</li> <li>The resultings evaluation and visualisation scripts</li> </ul> <p>Further details, documentation and information on the project can be found in the corresponding <a href="https://github.com/FLW-TUDO/ReID_Drone_Scripts">Github Repo</a> and <a href="https://www.logistics-journal.de/archive/proceedings/2023/5807/rutinowski_en_2023.pdf">publication</a>. If you have any questions concerning these datasets, feel free to contact the corresponding author, <a href="https://www.linkedin.com/in/jeromerutinowski/">Jérôme Rutinowski</a>.</p> <p>This work is part of the project "Silicon Economy Logistics Ecosystem" which is funded by the German Federal Ministry of Transport and Digital Infrastructure.</p>
Dataset for Uncovering multiscale structure in the variability of larval zebrafish navigation
<p>Datasets needed for recreating the figures from the paper "Uncovering multiscale structure in the variability of larval zebrafish navigation"</p>
Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga
<p>Early detection of nuisance species is crucial for the conservation and management of threatened ecosystems, reducing the risk of widespread establishment. Environmental DNA (eDNA) data can increase the sensitivity of biomonitoring programs, oftentimes with minimal cost and effort. However, eDNA analyses have inherent errors that can complicate the integration of molecular survey methods into existing management frameworks. Therefore, it is crucial for eDNA studies to consider imperfect detections and estimate error rates accordingly. Detecting nuisance species in low abundance with minimal uncertainty is vital to increase the chance of containment and eradication. We developed a novel eDNA assay to detect a nuisance marine macroalga across its colonization front using surface seawater samples from Papahānaumokuākea Marine National Monument (PMNM), one of the world's largest marine reserves. <em>Chondria tumulosa</em>, a cryptogenic red alga with invasive characteristics, has been documented forming dense mats that overgrow coral reefs and smother native flora and fauna in PMNM. We verified the eDNA assay using site-occupancy detection modeling from quantification polymerase chain reaction (qPCR) data, calibrated with visual estimates of benthic cover of <em>C. tumulosa </em>that ranged from < 1% to 95%. Results were subsequently validated with high-throughput sequencing of amplified eDNA and negative control samples. Overall, the probability of detecting <em>C. tumulosa </em>at occupied sites was at least 92% when multiple qPCR replicates were positive. Modeled false-positive inferences were 3% or less and false-negative errors were 11% or less. The developed assay is suitable for routine monitoring at shallow sites (less than 10 m), even when <em>C. tumulosa </em>abundance was less than 1%. Successful implementation of eDNA tools in conservation decision-making relies on balancing uncertainties in both visual and molecular detection methods. Our results and modeling demonstrated the assay's sensitivity to <em>C. tumulosa</em>, and we outline the necessary steps to infer ecological presence-absence from molecular detection data. By providing a reliable, cost-effective tool for detecting low-abundance species, eDNA analyses have the potential to enhance the surveillance of nuisance species and inform timely management interventions.</p>
Navigating the landscape of fear: Fruit flies exhibit distinct anti-predator and anti-parasite defensive behaviours
<p>Most organisms are at risk of being consumed by a predator or infected by a parasite at some point in their life. Theoretical constructs such as the landscape of fear (perception of risk) and non-consumptive effects (NCEs, costly responses sans predation or infection) have been proposed to describe and quantify anti-predator and anti-parasite responses. How prey/host species identify and respond to these risks determines their survival, reproductive success and, ultimately, fitness. Most studies to date have focused on either predator-prey or parasite-host interactions, yet habitats and ecosystems contain both parasitic and/or predatory species that represent a complex and heterogenous mosaic of risk factors. Here, we experimentally investigated the behavioural responses of a cactophilic fruit fly, <em>Drosophila nigrospiracula</em>, exposed to a range of species that include parasites (ectoparasitic mite), predators (jumping spiders), as well harmless heterospecifics (non-parasitic mites, ants and weevils). We demonstrate that D. nigrospiracula can differentiate between threat and non-threat species, increase erratic movements and decrease velocity in the presence of parasites, but decrease erratic movements and time spent grooming in the presence of predators. Of particular importance, flies could distinguish between parasitic female mites and non-parasitic male mites of the same species, and respond accordingly. We also show that the direction of these non-consumptive effects differ when exposed to parasitic mites (i.e., risk of infection) versus spiders (i.e., risk of predation). Given the opposing effects of predation versus infection risk on fly behaviour, we discuss potential trade-offs between parasite and predator avoidance behaviours. Our findings illustrate the complexity of risk assessment in a landscape of fear and the fine-tuned non-consumptive effects that arise in response. Moreover, this study is the first to examine these behavioural NCEs in a terrestrial system.</p>
Cumulative culture in artificial navigators
<p>This project is on how cumulative culture can spontaneously emerge in agents who are bound by just four simple rules:</p> <ul> <li><strong>Goal direction</strong>: Having a sense of (roughly) where the goal is.</li> <li><strong>Social proximity</strong>: Aiming to stay close to other agents by moving in the direction they are expected to be next.</li> <li><strong>Route memory</strong>: Agents remember landmarks along the route, and aim to follow along these landmarks. Their memory precision improves over several journeys.</li> <li><strong>Continuity</strong>: To avoid erratic/jerky movements, agents aim to move mostly in the direction that they are currently travelling in.</li> </ul> <p>Despite the lack of explicit social transmission or evaluation of outcomes, pairs of agents with generational turnover show gradual improvements in route efficiency (they converge on the direct line between start and goal). For more information, please read the manuscript on arXiv (linked below).</p> <h1>Current version</h1> <p>Due to uploading restrictions on Zenodo, included here are the reduced format data files. This is easier, as they can be readily used with the scripts from the linked GitHub repository.</p> <h2>Files</h2> <p>Each of the files below contains data for 50 repeats of the following parameter ranges:</p> <ul> <li>p_goal_range: [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35]</li> <li>p_social_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35]</li> <li>p_memory_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5]</li> <li>sd_goal_range: [1.0]</li> <li>sd_memory_max_range: [0.9]</li> <li>simulation_types: ["experimental", "solo", "pair"]</li> <li>n_repetitions: 50</li> <li>n_agents: 2</li> <li>n_generations: 5</li> <li>n_flights_per_generation: 12</li> <li>max_path_length: 200</li> </ul> <p>Note that the maximum path length for the reduced format is limited to 200 steps, whereas the simulation's maxmimum path length is a lot longer. This thus cuts a (very limited) number of true paths short. This only impacts the (x,y) coordinates in the x.dat and y.dat files, but not the efficiency data (which was computed using the full path).</p> <h3>efficiency.dat</h3> <p>This file contains the efficiency computed from full paths from each condition, repeat, and set of parameters. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, and n_flights_per_generation. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12).</p> <p>Values coded as "nan" exist, but they are rare. This occurs if the goal is never reached, and in generations after a generation in which the goal was never reached. (Because these never fly in the first place, as their predecessors never made it to the goal.)</p> <h3>x.dat</h3> <p>This file contains the horizontal (x) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200).</p> <p>Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this.</p> <h3>y.dat</h3> <p>This file contains the vertical (y) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200).</p> <p>Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this.</p> <h1><strong>Past versions</strong></h1> <h2><strong>Version 1<br></strong></h2> <p>There are two data archives in this set: <strong>data_simulation_narrow.zip</strong> (41 GB; 2680 folders containing a total of 80399 subfolders for a total of 5225935 CSV files) and <strong>data_simulation_wide.zip</strong> (7 GB; 557 folders containing a total of 16710 subfolders for a total of 1086150 CSV files).</p> <p>Each archive contains subfolders, each of which represent a unique combination of simulation parameters. These are named with the following naming scheme: "Pgoal-{w}_SDgoal-{sd}_Pcontinuity-{p}_SDcontinuity-{sd}_Psocial-{p}_SDsocial-{sd}_Pmemory-{p}_SDmemoryMax-{sd}_SDmemoryMin-{sd}_SDmemorySteps-5", where {p} is 1000 times the weight parameter, {sd} is 1000 times the equivalent standard deviation for the kappa parameter, and both are rounded to the nearest integer. Example: "Pgoal-100_SDgoal-1000_Pcontinuity-100_SDcontinuity-350_Psocial-100_SDsocial-800_Pmemory-700_SDmemoryMax-2000_SDmemoryMin-400_SDmemorySteps-5"</p> <p>Within each simulation folder, a number of subfolders can be found. This should normally be 30. Each of these represents a single run of a simulation within a condition. The naming convention is "{condition}_run-{run_counter}", where {condition} is the name of the condition ("experimental", "pair", or "solo"), and {run_counter} is a counter that starts at 1 and goes up from there (should be 1-10 in the current set).</p> <p>Within each simulation run subfolder, there are CSV files. These hold the actual data from journeys by artificial navigators. There are two types of data file, one for efficiency, and one for the travelled path. Both types of CSV have a header row with the names of the columns, followed by data rows.</p> <p>Efficiency files are named "efficiency_gen-{gen_nr}.csv", with {gen_nr} indicating the generation number. This starts at 0, ends at 4 (inclusive), and increments by 1. Efficiency files have three columns: "flight" for the flight counter (int, starts at 1), "efficiency_agent1" for the efficiency for the first agent's efficiency (float, between 0 and 1), and "efficiency_agent2" (float, between 0 and 1).</p> <p>Flight path files are named "xy_gen-{gen_nr}_flight-{flight_nr}.csv", with {gen_nr} being the same as above, and {flight_nr} being the journey number. This starts at 0, ends at 11 (inclusive), and increments by 1. Path files have four columns: "x_agent1" for the first agent's horizontal coordinate (float), "y_agent1" for the first agent's vertical coordinate (float), "x_agent2" for the second agent's horizontal coordinate (float), and "y_agent2" for the second agent's vertical coordinate (float).</p> <p>From generation 2 in the experimental condition, the first is the experienced agent, and the second is the naive agent. Float values coded as "nan" reflect there is no data. This occurs for e.g. the second agent in the solo condition and the first-generation experimental condition.</p>
Inductive biases of neural network modularity in spatial navigation
<p>The brain may have evolved a modular architecture for reward-based learning in daily tasks, with circuits featuring functionally specialized modules that match the task structure. We propose that this architecture enables better learning and generalization than architectures with less specialized modules. To test this hypothesis, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the architecture that largely segregates computations of state representation, value, and action into specialized modules enables more efficient learning and better generalization. The behavior of agents with this modular architecture also resembles macaque behaviors more closely. Investigating the latent state computations in these agents, we discovered that the learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to a Kalman filter. These results shed light on the possible rationale for the brain's modular specializations and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.</p>
Replication Package: Navigating the Complexity of Generative AI Adoption in Software Engineering
<p>This paper explores the adoption of Generative Artificial Intelligence (AI) tools and Large Language Models (LLMs) within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a structured interview study with 100 software engineers, drawing upon the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theoretical frameworks. Employing the Gioia Methodology, we derived a preliminary theoretical model of AI adoption in software: the Human-AI Collaboration and Adaptation Framework (HACAF). This model was then validated using Partial Least Squares – Structural Equation Modeling (PLS-SEM) based on data from 183 software professionals. Our research unveils the complex dynamics at play in AI adoption within software engineering. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.</p>
Surface Type Classification for Autonomous Robot Indoor Navigation - Dataset
<p>Surface Type Recognition with Inertial Measurement Unit (IMU).</p> <p>The dataset contains time series samples with 10 features each, related to orientation, velocity and acceleration. Each time series (of lenght 128) includes its corresponding surface type annotation.</p> <p>The data has been also divided in groups for easier cross-validation (80 groups present)</p> <p>A total of 9 different surface types are present in the dataset.</p> <p>"X_data.npy" contains the time series samples of dimension 7626x10x128<br> "label.npy" contains the label information for each sample (dimension 7626x1)<br> "groups.npy" contains the group information for each sample (dimension 7626x1)<br> "details.csv" contains for each sample the group information and the corresponding label</p> <p> </p>
Dataset for: Navigating Ecosystem Services Trade-offs: A Global Comprehensive Review
<p><strong>Methods</strong></p> <p>The dataset is the output of a comprehensive literature-based search that aims to collate all the evidence on where ES relationships have been mentioned and addressed. We applied systematic mapping which is based on the “Guidelines for Systematic Review in Environmental Management” developed by the Centre for Evidence-Based Conservation at Bangor University (Pullin and Stewart 2006).</p> <p>The methodological framework followed the standard stages outlined for systematic mapping in environmental sciences (James et al. 2016). Briefly, we defined the scope and objectives:</p> <p>· We comprehensively review and further explore the global evidence of ES trade-offs and synergies focusing on all systems including terrestrial, freshwater, and marine.</p> <p>· We compiled the evidence on trade-offs and synergies among multiple ES interacting across various ecosystems.</p> <p>· We performed a geographical and temporal trend analysis exploring the distribution of studies across the world examining how the focus on various ecosystem types and ES categories has evolved to highlight gaps and biases.</p> <p>Then we set the criteria for study inclusion (Table 1), searched the evidence, coded, and produced the database. Extracted article information including the specific criteria is detailed in Table 1.</p> <p>The first step was to search the ISI Web of Knowledge core collection (http://apps.webofknowledge.com) database, targeting the search on the ecosystem services literature and studies dealing with trade-offs/synergies, win-win outcomes or bundles when managing different ecosystem services in the landscape/seascape. All peer-reviewed journal articles written in English and Spanish have been considered for review.</p> <p>The <em>peer-reviewed </em>literature from 2005 to 2021 was reviewed identifying relevant studies according to specific search terms. The relevant search terms and descriptive words derived from (Howe et al. 2014) adding “bundles” and “co-benefits”. Boolean nomenclatures ‘*’ = all letters were allowed after the *, were used on the root of words where several different endings applied (Figure 1). Search terms used were:</p> <p>(“*ecosystem service*” OR “environment* service*” OR “ecosystem* approach*” OR “ecosystem good*” OR “environment* good*”)</p> <p>AND</p> <p>(“*trade-off*” OR “tradeoff*” OR “synerg*” OR “win-win*” OR “bundle*” OR “cost*and benefit*” OR “co-benefit*”) n=5194</p> <p>Papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (<a href="http://www.bibliometrix.org">http://www.bibliometrix.org</a>).</p> <p>In the second step (Figure 1) papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (http://www.bibliometrix.org). Papers were classified according to three systems: terrestrial, marine, and freshwater (Table 1). Papers with multiple systems, transitional habitats or those that could not be classified were classified as “other” (Mazor et al. 2018). Articles were classified based on the occurrence of the most frequent system words in their title, keywords, and abstract (Mazor et al. 2018). The set of system-specific words was determined by extracting the 250 most frequently used keywords from all considered articles and assigning each word to either system (articles could fall into just one of the four categories). Using this technique, we managed to classify 100% of the papers. To further enrich the dataset and make it a useful repository for science and policy, an additional sub-classification was performed, categorizing papers into the following categories: Coastal, Urban, Wetlands, Forest, Mountain, Freshwater, Agroecosystems, and Others that mainly represented multiple ecosystems (Table S1). This comprehensive classification approach enhances the dataset’s utility for various scientific and policy-making applications.</p> <p>In the third step (Figure 1), applying the same technique, we classified the papers into four ES categories: habitat (supporting biodiversity related), provisioning, regulating, and cultural services (De Groot et al. 2010; MEA 2005; Sukhdev 2010; Wallace 2007). For the classification into ES categories, articles could fall into one or more of the four categories (see Table 1 for example the keywords used to classify ecosystems, ES categories, and countries). Applying this technique, we excluded 2149 papers that weren’t classified in any of the ecosystem services types categories resulting in 3629 papers (see Figure 1).</p> <p>In the fourth step (Figure 1), an initial screening was conducted to identify papers that did not align with the review objectives of assessing ecosystem services trade-offs and synergies to inform policy and management decisions. We manually reviewed the titles of each paper in the dataset, excluding those that were from other fields or did not align with the review objectives. In this initial assessment, we excluded 347 papers, leaving a total of 3,286 papers for further review. A descriptive analysis of this 3286 article dataset was performed to examine the distribution of ES categories within each ecosystem type over the specified period. This analysis allowed us to conclude the prevalence of each ecosystem service category in different ecosystem types, identifying temporal trends and patterns. The number of occurrences was calculated for each ES category within each ecosystem type, expressed as counts. This allowed for the comparison of ecosystem service distributions across the selected ecosystem types.</p> <p>In the fifth step (Figure 1), we employed an approach to visually represent the geographical distribution and focus of ES studies across the world. With the classification of studies in ES categories and the types of ecosystems, the papers were coded according to the country where the study was performed. It was possible to assign a specific country to 2636 studies, removing 650 studies that did not specify the country of study. From these 2636 papers classified, a proportion were global studies that consider several countries under study (499 global studies).</p> <p>We developed global maps (Figure 1), each offering a unique perspective on the ES research landscape. The first map presents the total number of ES trade-off studies conducted worldwide, illustrating the geographical spread and concentration of research efforts to provide a clear overview of regions that have been extensively studied and those that may require more attention in future research. Additionally, we calculated two key metrics to assess research productivity more comprehensively: the number of research papers per capita and the number of research papers relative to Gross Domestic Product (GDP). For population and GDP, we used the most recent available data from the World Bank (https://data.worldbank.org). These alternative metrics normalize the data based on economic output and population size, providing a more balanced view of research activity across different countries (Figures S3).</p> <p>Detailed maps were created featuring pie charts that highlight the different categories of ES and ecosystem types addressed for each country. These charts offer an understanding of how various ES categories and ecosystems are represented in different parts of the world. Finally, we assessed ES trade-off studies to world regions (Africa, Antarctica, Asia, Australasia, Europe, Latin America, and North America) looking at the relationships between the categories of ES. We considered papers that evaluated more than one category of ES and the papers that considered only one category of ES. This country-level analysis offers insights into regional research trends and priorities, contributing to a more localized understanding of ES studies.</p> <p>In the sixth step (Figure 1), each publication in this review was critically appraised to evaluate the quality of the papers included in the review. The foundation for our critical appraisal stems from the comprehensive and multidimensional approach of Belcher et al. (2016) that is framed to evaluate research quality, which aligns well with the interdisciplinary nature of our study. Belcher et al. (2016) developed a robust framework that incorporates essential principles and criteria for assessing the quality of transdisciplinary research. This is particularly relevant for ecosystem services science and our review that contributes to advancing current knowledge by systematically synthesizing evidence on relationships among various ES across these diverse systems.</p> <p>The Belcher et al. (2016) framework emphasizes four main principles: relevance, credibility (which we have adapted as methodological transparency), legitimacy (generalizability in our context), and effectiveness (significance). A continuous scoring system (ranging from 0 to 1) was applied for the four main criteria to maintain simplicity and consistency across the large number of studies. In this system, a value closer to 0 indicates that the criteria are not met, while a value closer to 1 indicates that the criteria are more closely met. This scoring method was a useful indicator of the overall quality of the paper and how well the article met the review's goals overall.</p> <p>Methodological Transparency was assessed based on the clarity and completeness of methodological descriptions, including data availability, the rigor of statistical analyses, methodological detail, and reproducibility of the findings. This criterion assesses the transparency and rigor of the study's methodology, including data collection, analysis, and reporting (Belcher et al. 2016). Relevance was evaluated by the study's alignment with the review's objectives, its importance to the field, and its practical applicability. This includes the extent to which the study addresses pertinent research questions of the study (Belcher et al. 2016). Significance was determined by the novelty of the study, its theoretical contributions, and practical implications. This includes evaluating whether the study presents new ideas, concepts, or frameworks that advance the field of ES relationships (Belcher et al. 2016). Generalizability was judged based on the contextual applicability and transferability of the study's findings to other settings. This includes assessing whether the study's results can be applied broadly and whether the context is sufficiently described to understand its applicability (Belcher et al. 2016).</p> <p>In the final seventh step (Figure 1) a detailed assessment was performed incorporating sub-categories within each main critical appraisal criteria to allow for a more comprehensive analysis (Table S2). To this end, a random sample of 20% of the studies was selected (574 papers) and a thorough revision by subcategories for each main criteria was performed (see Table S2). For the random selection of 20% of the studies from the dataset, we loaded the dataset into R and set a random seed to ensure reproducibility. We then calculated the sample size as 20% of the total dataset and used the sample function to select this subset of studies randomly. This method ensured an unbiased and representative sample. The selected subset was assessed for a further detailed critical appraisal adding sub-categories to each criterion (Table S2). A qualitative assessment sub-category was also inclded to explain the rationale behind each assigned score in the dataset.</p> <p>We finally created a heatmap to visualize average scores by continent across the four criteria. Utilizing R programming (R Core Team 2023) for data aggregation with the <em>dplyr </em>package, the study computed mean scores for each criterion by continent. The aggregated data was visualized using a heatmap created with the ggplot2 package, where the color intensity of each cell represented the mean scores, facilitating a visual comparison across continents. This analysis offers a quantitative foundation for identifying areas of strength, evaluating performance by continent and potential for improvement, and further elucidating the different quality and focus of research within the ES trade-offs research.</p> <p><strong>References:</strong></p> <p>Belcher, B. M., Rasmussen, K. E., Kemshaw, M. R., & Zornes, D. A. (2016). Defining and assessing research quality in a transdisciplinary context. Research Evaluation, 25(1), 1-17.</p> <p>De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management, and decision-making. Ecological Complexity, 7, 260-272.</p> <p>Howe, C., Suich, H., Vira, B., Mace, G.M., 2014. Creating win-wins from trade-offs? Ecosystem services for human well-being: A meta-analysis of ecosystem service trade-offs and synergies in the real world. Global Environmental Change 28, 263-275.</p> <p>Mazor, T.A.-O., Doropoulos, C.A.-O., Schwarzmueller, F.A.-O., Gladish, D.A.-O.X., Kumaran, N.A.-O., Merkel, K.A.-O.X., Di Marco, M., Gagic, V.A.-O. (2018). Global mismatch of policy and research on drivers of biodiversity loss. Nature Ecology & Evolution 2, 1071-1074.</p> <p>Millennium Ecosystem Assessment (MEA).(2005). Ecosystems and Human Well-Being: Synthesis. Island Press, Washington DC.</p> <p>Pullin, A. S., & Stewart, G. B. (2006). Guidelines for systematic review in conservation and environmental management. Conservation biology, 20(6), 1647-1656.</p> <p>Sukhdev, P. (2010). The economics of ecosystems & biodiversity: mainstreaming the economics of nature: a synthesis of the approach, conclusions, and recommendations of TEEB. UNEP.</p> <p>R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/</p> <p>Wallace K.J. (2007). Classification of ecosystem services: Problems and solutions. Biological Conservation 139, 235-246.</p> <p><strong>Dataset</strong></p> <p>Name of the file: MMartinez-Harms-Dataset-ESTrade-offs_Zenodofinal.xlsx</p> <p>Description: Complete database of the 3286 studies reviewed in this synthesis coded by:</p> <table> <tbody> <tr> <td><strong>N</strong></td> <td>Number of papers</td> </tr> <tr> <td><strong>AU</strong></td> <td>Authors</td> </tr> <tr> <td><strong>TI</strong></td> <td>Title</td> </tr> <tr> <td><strong>SO</strong></td> <td>Publication name</td> </tr> <tr> <td><strong>Included</strong></td> <td>identify papers that did alignd (yes) or did not (No) align with the review objectives.</td> </tr> <tr> <td><strong>JI</strong></td> <td>Scientific Journal</td> </tr> <tr> <td><strong>AB</strong></td> <td>Abstract</td> </tr> <tr> <td><strong>DE</strong></td> <td>Authors’ Keywords</td> </tr> <tr> <td><strong>ID</strong></td> <td>Keywords associated by ISI database</td> </tr> <tr> <td><strong>LA</strong></td> <td>Language</td> </tr> <tr> <td><strong>DT</strong></td> <td>Document Type (article, review, editorial, book chapter, letter, meeting abstract)</td> </tr> <tr> <td><strong>TC</strong></td> <td>Times Cited</td> </tr> <tr> <td><strong>PY</strong></td> <td>Publication Year</td> </tr> <tr> <td><strong>SC</strong></td> <td> Subject Categories</td> </tr> <tr> <td><strong>WC</strong></td> <td>Web of Science categories</td> </tr> <tr> <td><strong>System</strong></td> <td>Freshwater, terrestrial, marine, and other</td> </tr> <tr> <td><strong>Habitat</strong></td> <td>Habitat ES category</td> </tr> <tr> <td><strong>Regulating</strong></td> <td>Regulating ES category</td> </tr> <tr> <td><strong>Cultural</strong></td> <td>Cultural ES category</td> </tr> <tr> <td><strong>Provisionning</strong></td> <td>Provisionning ES category</td> </tr> <tr> <td><strong>Country_Classification</strong></td> <td>Country of study</td> </tr> <tr> <td><strong>Continent</strong></td> <td>Continent of study</td> </tr> <tr> <td><strong>Synergies_Trade-offs</strong></td> <td>The term trade-off involves losing one quality or aspect of something in return for gaining another quality or aspect. Synergy is a situation where the use of one ES directly increases the benefits supplied by another ES or a win-win situation</td> </tr> <tr> <td><strong>Stakeholder_Participation</strong></td> <td>The study interacts with stakeholders at any moment of the study or policy decisions. </td> </tr> <tr> <td><strong>Critical_Appraisal</strong></td> <td>Articles are appraised to ensure that they are adequate for answering the research question</td> </tr> <tr> <td><strong>Relevance</strong></td> <td>Relevance to the review question (0 no relevance, 1 relevance)</td> </tr> <tr> <td><strong>Methodological_Transparency</strong></td> <td>Assessed based on the clarity and completeness of methodological descriptions</td> </tr> <tr> <td><strong>Significance</strong></td> <td>Significance of the contribution (are new ideas offered?)</td> </tr> <tr> <td><strong>Generalizability</strong></td> <td> Is the context specified, and do the ideas apply in other contexts?</td> </tr> <tr> <td><strong>Total_Score</strong></td> <td>The sum total score across all critical appraisal criteria </td> </tr> <tr> <td><strong>Category</strong></td> <td>Coastal, Urban, Wetlands, Forest, Mountain, Freshwater, Agroecosystems, and Others mainly represented multiple ecosystems</td> </tr> <tr> <td><strong>Uses_INVEST</strong></td> <td>This paper uses the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tool from the Natural Capital Project to assess ecosystem services (Yes) or Not (No)</td> </tr> </tbody> </table>
From Redirected Navigation to Forced Attention: Uncovering Manipulative and Deceptive Designs in Augmented Reality through Retail Shopping
<p>In the dataset, there are two types of files: one (1) Excel file and ten (10) PDF files.</p> <p>The Excel file contains data analysis and coding.</p> <p>The PDF files are organized by session. <br>Each PDF file contains screenshots of various scenarios and their corresponding discussions. <br>Each PDF has 12 pages, except for Session 8, which was not completed due to technical problems. <br>The files are organized as follows:</p> <p>Page 01 - Scenario 1: Navigation & Attention phase, Grocery Shopper<br>Page 02 - Scenario 2: Navigation & Attention phase, AR User<br>Page 03 - Scenario 3: Interest & Desire phase, Grocery Shopper<br>Page 04 - Scenario 4: Interest & Desire phase, AR User<br>Page 05 - Scenario 5: Action phase, Grocery Shopper<br>Page 06 - Scenario 6: Action phase, AR User<br>Page 07 - Discussion of Scenario 1<br>Page 08 - Discussion of Scenario 2<br>Page 09 - Discussion of Scenario 3<br>Page 10 - Discussion of Scenario 4<br>Page 11 - Discussion of Scenario 5<br>Page 12 - Discussion of Scenario 6</p> <p>The MIRO Board can be found at the following link:<br>https://miro.com/app/board/uXjVM23rRP8=/?share_link_id=82923580188</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.