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SHEEP SHEARING 2023 IN RGYAB LUNG (JIALONG) COMMUNITY, RTA NAG MA (HEIMAHE) TOWNSHIP TOWN, GSER CHEN (GONGHE) COUNTY, MTSHO LHO (HAINAN) TIBETAN AUTONOMOUS PREFECTURE, MTSHO SNGON (QINGHAI) PROVINCE, PR CHINA by Sgrol ma yag སྒྲོལ་མ་ཡག
<p>SHEEP SHEARING 2023 IN RGYAB LUNG (JIALONG) COMMUNITY, RTA NAG MA (HEIMAHE) TOWNSHIP TOWN, GSER CHEN (GONGHE) COUNTY, MTSHO LHO (HAINAN) TIBETAN AUTONOMOUS PREFECTURE, MTSHO SNGON (QINGHAI) PROVINCE, PR CHINA</p><p>Sgrol ma yag སྒྲོལ་མ་ཡག</p><p>May-June is sheep-shearing time in my home area of Rgyab lung (Jialong) Community, Rta nag ma (Heimahe) Township Town, Gser chen (Gonghe) County,<a href="#_ftn1">[1]</a> Mtsho lho (Hainan) Tibetan Autonomous Prefecture, Mtsho sngon (Qinghai) Province, PR China. My family stopped shearing sheep in about 2007. I filmed the first time I participated in sheep shearing since then, helping five local families shear sheep. There have been many changes in sheep-shearing including what families do with the sheared wool and tools used to shear. Most families have around ten people to help.</p><p>Father (b. 1970) said that if we don't shear at this time, the sheep will be uncomfortable and scratch against walls, rocks, and ditches, and might roll over, be unable to stand, and die. </p><p>At around 5PM, my cousin's family arrived with their sheep, brought from the summer pasture to the spring pasture. After the family drove the sheep into the sheep enclosure, the son slaughtered a sheep, and his parents went to the township town by motorcycle and bought various beverages, fruit, biscuits, and other items. Father helped the son take care of the meat. </p><p>Early the next morning, the family got up at four and began shearing after a simple breakfast. Later, helpers came one after another.</p><p>Before they start shearing, men sharpen the <i>bal he</i> 'shearing scissors,' and two or three women of the family cook, prepare milk tea, and cook meat and noodles. Each man has ten <i>bkyig tigs</i> 'strings', indicating each is to catch ten sheep and shear them. This is <i>bkyigs thabs gcig</i> 'one round.' If it is a shearer's first time to shear, other men help him. </p><p>When I was a child, each man had his own strings, and most were made of wool. They colored their strings to differentiate them from others. Father's strings were wool and colored red on top. Every time he finished shearing, I was responsible for collecting the strings. At that time men twisted their strings around their waists and pulled one when they caught a sheep and tied its legs with it. </p><p>Most men who participated in the sheep shearing this year had few wool strings. Instead, they bought cloth from the market and tore the cloth into strips. </p><p>During shearing, two or three women were responsible for marking the sheared sheep with <i>lcags rtsi</i> 'paint'. Each family had a distinctive paint color and marked the sheep differently. For instance, the first family I helped, used green on the neck and blue on the rump. Other women or children were responsible for collecting <i>bal rdul</i> 'pieces of wool that had broken off during shearing'. When I was a child, we children were responsible for collecting <i>bal rdul</i> and preventing the sheep from escaping as elders were catching them. I did the same during the sheep shearing this year. </p><p>After one round finished, the men collected their strings and took a short break. Some resharpened their scissors, and the family offered beverages and snacks. </p><p>One element of fun in sheep shearing is young men competing to see who is the fastest. They joke with each other. One of my cousins was the funniest person that day. He teased young men that if they didn't shear the wool well, they would not find good wives. The young men also verbally sparred with each other. One of my cousins and his friend had a contest of to see who could shear the fastest. My cousin lost several times, so his friends and cousins teased him that he might not find a good wife. They also teasingly suggested that he needed to join more sheep shearing events to practice his ability. </p><p>Father said when he was young, they were happy to join the sheep-shearing events of local families. Once, one of his cousins and he participated in a sheep shearing. There were many young men there to help the family and they competed to see who was the fastest. Father laughingly said that one young man had shaved the edge of his robe that day. Everybody laughed at him and teased him that day. </p><p>Jokes and laughing release tiredness. </p><p>During the shearing, some families medicate the sheep, because they won't have to catch the sheep again with few family members. For instance, one family brought anthrax vaccine of anthrax provided by the local government. </p><p>One family takes care of the wool traditionally. Women scatter the sheared wool on the ground in a line near the sheep enclosure and spread other wool on the scattered wool. A woman puts a stick on the scattered wool and twists it. Another woman helps her to twist the scattered wool to make the wool easier to twist. They make the line of twisted wool into a round shape after twisting it tightly. </p><p>Two or three women spin several pieces of wool to make a long strip men use to bind the <i>bal dos</i> 'rounded wool' with a <i>yog thag</i> 'spun strip'. This process is locally called <i>bal gcu sdom byed pa</i> and it is the final process. Men put the <i>bal dos</i> in the family's storage room. </p><p>Very few families take care of the wool in traditional ways. The shorn wool is simply placed into plastic bags, which doesn't involve arduous labor, such as what the last family event that I joined. They made long strips, scattered the wool on the ground, and so forth. </p><p>Finally, the family offers water for the men to wash their hands. The housewife and the husband or other family members ask others to sit. They offer tea and then meat, noodles, bread, snacks, and various beverages. Family members repeatedly urge guests and helpers to eat and drink more. If some are unfamiliar with the guests, they don't eat much, pretending that they are full after a small amount of food. </p><p>I did the same in one family where I met some guests for the first time. The family leader is a relative of my father and has no son. He is in his seventies, so Father helps him every year during their sheep-shearing. </p><p>This was my first time to participate in this family's sheep-shearing. The day before the shearing, the family head phoned Father and asked if he could come help. He said that it was fine if he could not because of Father's back pain. Father said he would help and added that he would bring me with him. The man was grateful for Father's generous help. </p><p>Father is famous for his sheep-shearing ability. Locals said Father was the best shearer in our community when he was young. </p><p>Except for the last family, Father didn't join the sheep-shearing of other families that I joined. There were many helpers and they said it was fine that he didn't help because they knew about his back pain. Some helpers said that the work would be easier if Father was there. </p><p>Father said when he was young, sheep-shearing took place in the summer pasture, because there were few families, few livestock, enough grass for the animals, and locals moved to summer pasture earlier. So, they packed the <i>bal dos </i>onyaks to the spring pasture where they could store the <i>bal dos</i> as long as they wanted. </p><p>TIBETAN TERMS</p><p>bal dos བལ་དོས།</p><p>bal gcu sdom byed pa བལ་གཅུ་སྡོམ་བྱེད་པ།</p><p>bal he བལ་ཧེ།</p><p>bal rdul བལ་རྡུལ།</p><p>bkyig thag བཀྱིག་ཐག</p><p>bkyigs thabs gcig བཀྱིགས་ཐབས་གཅིག</p><p>gser chenགསེར་ཆེན།</p><p>lcags rtsi ལྕགས་རྩི།</p><p>mtsho lho མཚོ་ལྷོ།</p><p>mtsho sngon མཚོ་སྔོན།</p><p>rgyab lung རྒྱབ་ལུང་།</p><p>rta nag ma རྟ་ནག་མ།</p><p>sgrol ma yag སྒྲོལ་མ་ཡག</p><p>yog thag ཡོག་ཐག</p><p> </p><p>CHINESE TERMS</p><p> </p><p>Jialong 加隆</p><p>Heimahe 黑马河</p><p>Hainan 海南</p><p>Gonghe 共和</p><p><a href="#_ftnref1">[1]</a> In 2021, Gser chen County included seven towns, four townships, ninety-nine administrative communitys, and a total population of 133,409. The county had twenty-two ethnic minorities (seventy percent of the total population) including Tibetans, Han (34,364), Hui (8,844), and Mongolian (2,140). The county had 18,761,600 <i>mu</i> of usable grassland, 457,600 <i>mu</i> of arable land, and an average altitude of 3,200 <a href="https://bit.ly/3Gw2RfS">https://bit.ly/3Gw2RfS</a> 20 November 2022). Heimahe's (township town) population was 4,000 with Tibetans accounting for ninety percent of the total. The township town is 1,000 square kilometers in area with jurisdiction over Rgyab lung (Jialong), Brag mchog (Zhiheqv), Bum pa (Wenba), and Ra 'khyog (Ranheqv) <a href="https://bit.ly/3GuaDXK">https://bit.ly/3GuaDXK</a> 20 November 2022). </p><p>The 2011 population of Mtsho lho Prefecture was 437,800, including 269,100 Tibetans (sixty-three percent) https://bit.ly/2OAGwll 25 July 2020. </p>
Autonomous, multiproperty-driven molecular discovery: from predictions to measurements and back
<p>This repository contains all the data and models associated with the paper "Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back" by Koscher et al. More information to follow</p>
Dataset about "An autonomous low‑cost studio to record production‑ready instructional videos"
<p>Dataset used in the article "An autonomous low‑cost studio to record production‑ready instructional videos".</p> <p>The dataset contains two excel files. One for the TAM model applied to the autonomous low-cost studio (called SAGA), and one with the student perceptions about the resulting videos.</p> <p>Much more information is available in the publication in the journal.</p>
Automatic Generation of Explanations in Autonomous Systems: Enhancing Human Interaction in Smart Home Environments
<p>The file named “Dataset” is the generation of scenarios and explanations used for the proposal.<br>The file named “Questionnaire answers & data analysis” corresponds to the application of a questionnaire addressed to 118 people.</p>
Fig. 11 in Three new species of the spider genus Meotipa Simon, 1895 from Guangxi Zhuang Autonomous Region, China (Araneae: Theridiidae)
Fig. 11. Collection localities for three new species of Meotipa Simon, 1895 in China.
Dataset - Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution
<h1>Dataset - Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution</h1> <p>Dataset for manuscripts "Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution".</p> <p>This dataset includes the raw data for the images in the manuscript except for the schematic drawings.</p> <p>The source code is released as ROS packages at <a title="https://github.com/ZJU-FAST-Lab/Aerobatic-Planner" href="https://github.com/ZJU-FAST-Lab/Aerobatic-Planner" target="_blank" rel="noopener">GitHub</a>.</p> <h2>Large-scale aerobatic flight</h2> <p>Code: outdoor.mlx</p> <p>Data: outdoor_data/</p> <ul> <li> <p>outdoor_final.bag: This rosbag captures all essential data during outdoor flight in real-world experiments.</p> </li> <li> <p>outdoor_map.pcd: Pointcloud map file of the outdoor flight environment.</p> </li> <li> <p>[ManeuverName]View[id].mp4: Video files showcasing different maneuvers from various perspectives during rosbag playback in Rviz visualization software.</p> </li> </ul> <h2>Successive aerobatic maneuvers in confined spaces</h2> <p>Code: indoor.mlx</p> <p>Data: indoor_data/</p> <ul> <li> <p>nokov[id].bag: The rosbag captures all essential data during indoor flight in real-world experiments. Each bag representing a single aerobatic flight. Data package representing indoor experimental flights, with each package representing a single flight.</p> </li> <li> <p>indoor_map.pcd: Pointcloud map file of the indoor flight environment.</p> </li> </ul> <h2>Combination of aerobatic intentions</h2> <p>Data: intention_simulation/</p> <ul> <li> <p>[ManeuverName]_Intention.png: Input aerobatic intentions.</p> </li> <li> <p>[ManeuverName]_Action.png: Maneuvers generated based on intentions.</p> </li> </ul> <p>These pictures are rendered using Blender.</p> <h2>Ablation analyses</h2> <h3>Yaw compensation</h3> <p>Code:</p> <ul> <li> <p>ablation_yaw_comp.mlx</p> </li> <li> <p>yaw_sensitivity.mlx</p> </li> </ul> <p>Data: ablation_data/YawComp/</p> <ul> <li> <p>[with/without]YCM.mp4: Slow-motion footage of the drone executing flight trajectories in the yaw-sensitive area at 1/50th of the original speed.</p> </li> <li> <p>[with/without]YCM.txt: State computation of the entire trajectory, with a time interval of 0.2 microseconds for each line of state.</p> </li> </ul> <h3>Trajectory optimization</h3> <p>Code:</p> <ul> <li> <p>ablation_traj_opt.mlx</p> </li> <li> <p>attitude_penalty.mlx</p> </li> </ul> <p>Data: ablation_data/TrajOpt/</p> <ul> <li> <p>OptTrajRet.txt: Various evaluation metrics obtained from 90 trajectory generations.</p> </li> <li> <p>[ManeuverName]_[OptimizeCondition].png: Typical optimized trajectories under different optimization conditions for various maneuvers.</p> </li> </ul> <h1>Additional data in the rebuttal</h1> <h2>Matching and surpassing human pilots</h2> <p>Code:</p> <ul> <li> <p>human_single.mlx</p> </li> <li> <p>human_multi.mlx</p> </li> </ul> <p>Data: human_competition/[single/multi]/[auto/manual]/[id].bag: The rosbag captures all essential data about drone flight in competition.</p> <h2>Accurate thrust module fitting</h2> <p>Code: thrust_fitting.mlx</p> <p>Data: thrust_fitting/[lidar/nokov].csv: measured calibration data of two drones.</p> <h2>Optimization of 1000 aerobatic trajectories</h2> <p>Code: repeat_trajopt.mlx</p> <p>Data: repeat_traj_opt/</p> <ul> <li> <p>opt_result.txt: final output results of the trajectory optimization problem.</p> </li> <li> <p>optBoundValue.txt: boundary values of trajectories.</p> </li> <li> <p>trajpos.txt: position, velocity, and orientation of the trajectories.</p> </li> </ul> <h2>Aerobatics vs. Non-Aerobatics</h2> <p>Code: nonaero_compare.mlx</p> <p>Data: nonaero_compare/</p> <ul> <li> <p>aerotraj_[aero/non].txt: position and orientation of the trajectories.</p> </li> <li> <p>opt_result_[aero/non].txt: final output results of the trajectory optimization problem.</p> </li> <li> <p>optBoundValue_[aero/non].txt: boundary values of trajectories.</p> </li> <li> <p>trajpos_[aero/non].txt: position, velocity, and orientation of the trajectories.</p> </li> </ul>
CodePori: Large Scale System for Autonomous Software Development by Using Multi-Agents
<p>This dataset accompanies the paper <strong>"CodePori: A Large-Scale System for Autonomous Software Development Using Multi-Agents."</strong> The dataset is recorded in an MS Excel file, which contains the following sheets, with a brief description of each provided below:</p> <ol> <li> <p><strong>Selected Projects:</strong> Contains the descriptions of the 20 selected projects along with the GitHub URL for each.</p> </li> <li> <p><strong>Modifications:</strong> Contains details of the modifications made to the projects to ensure successful execution.</p> </li> <li> <p><strong>Outputs:</strong> Contains the output for each project.</p> </li> <li> <p><strong>Failed Projects:</strong> Contains data on the projects that failed.</p> </li> </ol>
Autonomous Tester Agent Benchmark
<p>This is a benchmark for Autonomous Tester Agents. This benchmark is composed of a hundred test cases for 3 web applications written by professional testers. Half of the test cases are mutations of the original ones to better evaluate the capacity of agents to identify bugs. The test cases are composed of several steps that need precise assertion.</p> <p>An evaluation of the SeeAct model can also be found in the repository, as well as the source code to replicate the results. This repository is the replication package of a paper accepted to the ISSTA 2025 conference. (https://conf.researchr.org/home/issta-2025)</p>
Impact on energy and air quality of connected and autonomous vehicles in an urban context
<p>Despite numerous studies related to autonomous vehicles and connected vehicles (CAVs) and their impact on the economy or on traffic performance (eg, flow management, accidents), there are not many studies that relate these benefits to the environmental component. In this context, the objective of this work consisted in the integrated assessment of the impacts of CAVs on traffic performance, atmospheric emissions CO<sub>2</sub> and NO<sub>x, </sub>and air quality.</p> <p>To this end, a roundabout in the city of Aveiro was selected as a case study, and different scenarios were created: base scenario, considering the current typology of vehicles (conventional); scenario 2, considering defensive behavior CAVs; scenario 3, considering assertive behavior CAVs; and scenario 1, considering all types of vehicles mentioned above. To ensure a comprehensive analysis, all scenarios were evaluated for a period of 24 hours, corresponding to the period of the experimental campaign carried out, and a cascade of models was applied.</p> <p>First, the PTV VISSIM model was applied which allowed, configuring, calibrating and validating the network under study for an evaluation of the traffic performance. Second, the VSP model was applied to estimate atmospheric emissions, Finally, the CFD VADIS model was applied to air quality assessment.</p> <p>The results obtained allowed us to conclude that the introduction of CAVs, promotes longer travel times, especially during times of higher traffic, and an increase in emissions, mainly by the CAVs with defensive behavior. In terms of air quality, there were large differences in terms of NO<sub>2</sub> concentrations, with the CAVs promoting a degradation of air quality, especially during peak traffic hours.</p>
Data from: Neural pathways of autonomic reflexes in familial autonomic ganglionopathy
<p><b>Objective: </b>We determined the molecular basis of a new monogenetic recessive disorder that results in familial neurogenic orthostatic hypotension (nOH) with impaired sympathetic (SNS) and parasympathetic (PNS) activity.<b> </b></p> <p><b>Methods:</b> Two adult siblings from one family (I-4, I-5) and an another subject from a second family (II-3) presented with severe nOH, poor compensatory increase in heart rate, small non-reactive pupils and constipation. All three affected members had low norepinephrine levels and diffuse autonomic failure.</p> <p><b>Results:</b> Whole exome sequencing of DNAs from subjects (I-4, I-5) showed compound heterozygosity for c.907_908delCT (p.L303Dfs*115) /c.688G>A (p.D230N) pathologic variants in the acetylcholine receptor, neuronal nicotinic, alpha 3 subunit (CHRNA3) gene. The II-3 from the second family was homozygous for the same frameshift(fs) variant (p.L303Dfs*115// p.L303Dfs*115). The CHRNA3 is a subunit of nicotinic acetylcholine receptors (nAChRs) that regulate SNS and PNS activity through fast synaptic transmission in the autonomic ganglia. The fs variant is obviously pathogenic and the p.D230N variant is predicted to be damaging (SIFT)/probably damaging (PolyPhen2). The p.D230N variant lies on the interface between CHRNA3 and other nAChR subunits based on structural modeling and is predicted to destabilize the nAChR pentameric complex.</p> <p><b>Conclusions:</b> We report a novel genetic disease that affected three individuals from two unrelated families who presented with nOH and diffuse autonomic failure. These subjects had rare pathologic variants in the CHRNA3 gene that co-segregate with and are predicted to be the likely cause of their SNS and PNS failure.</p>
Autonomous Vehicle Communication Strategies Modeled in Virtual Reality
<p>We sought to better understand how autonomous vehicle (AV) communication strategies impact human road users’ perceptions and behaviors. More specifically, we explored the impact of different external human-machine interface (eHMI) designs on understanding, task load, comfort, trust, acceptance, and reaction time. To accomplish this, we created virtual reality (VR) scenarios where human participants interacted with AVs. Participants experienced biking, driving, and pedestrian simulators and were brought back after initial testing to explore acclimation and learning effects. In terms of perceptions, the presence of an eHMI was the strongest predictor of understanding, comfort, trust, and acceptance outcomes in the statistical models when controlling for all other variables. There was a clear divide between text-based eHMIs and non-text eHMIs, with text-based eHMIs reporting better perception scores and the LED Windshield reporting the worst perception scores. There were perception acclimation effects detected (most notable for task load and comfort), but they had less of an impact than the presence of an eHMI. Perception outcomes had weaker relationships with participant characteristics than with AV characteristics. While behavioral outcomes should be interpreted with caution because of low participant sample sizes, behavioral results largely mirrored perception results in that significant reductions in reaction time were observed with the presence of an eHMI (3.69 second reduction), yielding (3.16 second reduction), and acclimation (0.134 second reduction per trial). Results suggest that eHMI design, AV behavior, and acclimation are most impactful in terms of both perceptions and reaction time.</p>
Statements related to autonomous weapons systems delivered by the Russian Federation to the UN CCW from 2014 to 2022
<p>This file contains the Russian Federation's statements related to the issue of autonomous weapons systems delivered to the United Nations Convention on Certain Conventional Weapons from November 2014 to March 2022. It is the online appendix for "Great power identity in Russia’s position on autonomous weapons systems".</p>
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
<p>This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.</p>
Combining point counts and autonomous recording units improves avian survey efficacy across elevational gradients on two continents
<p>Accurate biodiversity and population monitoring is a requirement for effective conservation decision-making. Survey method bias is therefore a concern, particularly when research programs face logistical and cost limitations.</p> <p>We employed point counts (PCs) and autonomous recording units (ARUs) to survey avian biodiversity within comparable, high elevation, temperate mountain habitats at opposite ends of the Americas: 9 mountains in British Columbia (BC), Canada and 10 in southern Chile. We compared detected species richness against multi-year species inventories and examined method-specific detection probability by family. By incorporating time costs, we assessed the performance and efficiency of single vs. combined methods.</p> <p>Species accumulation curves indicate ARUs can capture ~93% of species present in BC but only ~58% in Chile, despite Chilean mountain communities being less diverse. The avian community, rather than landscape composition, appears to drive this dramatic difference. Chilean communities contain less-vocal species, which ARUs missed. Further, 6/13 families in BC were better detected by ARUs while 11/11 families in Chile were better detected by PCs. Where survey conditions differentially impacted method performance, PCs mostly varied over the morning and with canopy cover in BC, while ARUs mostly varied seasonally in Chile. Within a single year of monitoring, neither method alone was predicted to capture the full avian community, with the exception of ARUs in the alpine and subalpine of BC. PCs contributed little to detected diversity in BC, but including this method resulted in negligible increases in total time costs. Combining PCs with ARUs in Chile significantly increased species detections, again, for little cost.</p> <p>Combined methods were among the most efficient and accurate approaches to capturing diversity. We recommend conducting point counts while ARUs are being deployed and retrieved in order to capture additional diversity with minimal additional effort and to flag methodological biases using a comparative framework.</p>
Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys
<p><span>Ecologists often study biodiversity by evaluating species occupancy and the relationship between occupancy and other covariates. Occupancy models are now widely used to account for false absences in field surveys and to reduce bias in estimates of covariate relationships. Existing occupancy models take as inputs binary detection/non-detection observations of species at each visit to each site. However, autonomous sensing devices and machine learning models are increasingly used to survey biodiversity, generating a new type of observation record (i.e., continuous-score data) that reflects the model's confidence a species is present in each autonomously sensed file, instead of binary detection/non-detection data. These data are not directly compatible with traditional binary occupancy modeling methods.</span></p> <p><span>Here, we develop a new occupancy model that models continuous scores on a visit level as a Gaussian mixture, combining a distribution of scores for files that do contain the species of interest and a distribution of scores for files that do not. The model takes as input continuous scores for each autonomously sensed and classified file, along with an optional small number of binary, manually verified detection and non-detection annotations.</span></p> <p><span>We present a simulation study that shows that over a range of empirically realistic parameters, our model outperforms traditional occupancy models that are based on binary annotation alone. We also apply this new model to an empirical case study using data generated from five machine learning classifiers applied to autonomous acoustic recordings gathered in the eastern United States.</span></p> <p><span>Because our occupancy model generalizes allowable input data beyond binary observations, it is particularly well-suited to the increasing volume of machine learning classified data in ecology and conservation.</span></p>
autonomous-vehicle-interests-multivariate-modeling
<p>This is a release of data and analysis scripts of the "Private or on-demand autonomous vehicles? Modeling public interest using a multivariate model " research study. It contains the 2019 California Vehicle Survey data as well as the scripts followed to clean and analyze the data. A word document within the folder "Description.docx" presents the steps followed to get the outputs. All scripts are written in R.</p>
Raw high-frequency EK80 echosounder datasets from autonomous sailbuoy campaign in Lofoten/Vesterålen 2018
<p>In 2018, the autonomous surface vehicle sailbuoy <a href="http://sailbuoy.no/13-news/74-introducing-the-sb-echo">Echo</a> surveyed the coastal shelf surrounding the Lofoten and Vesterålen islands (aka. LoVe), Norway. The Echo was equipped with a <a href="https://www.kongsberg.com/maritime/products/ocean-science/ocean-science/es_scientific/transducers-for-scientific-echo-sounders/simrad-wbt-mini">EK80</a> high-frequency echosounder, and over the course of 131 field days, 8791 <code>.raw</code> EK80 echosounder files were captured, with a total size of ~750 GB.</p> <p>In 2022, Akvaplan-niva published the complete, raw data from Echo's LoVe 2018 campaign in their research archive on <a href="https://zenodo.org/communities/akvaplan-niva/search?page=1&size=20&q=sailbuoy%20ek80%202018">Zenodo</a>.</p> <p>EK80 datasets from Sailbuoy Echo in LoVe 2018:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.6839067">2018-03-08/2018-03-12</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839167">2018-03-13/2018-03-17</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839191">2018-03-18/2018-04-01</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839345">2018-04-02/2018-04-05</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839512">2018-04-06/2018-04-26</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839520">2018-04-27/2018-05-03</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839542">2018-05-04/2018-05-30</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839550">2018-05-31/2018-06-02</a></li> <li><a href="https://doi.org/10.5281/zenodo.6834583">2018-06-03/2018-06-06</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839566">2018-06-07/2018-06-09</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839576">2018-06-10/2018-06-12</a></li> <li><a href="https://doi.org/10.5281/zenodo.6787552">2018-06-13/2018-06-17</a></li> <li><a href="https://doi.org/10.5281/zenodo.6786850">2018-06-18/2018-06-30</a></li> <li><a href="https://doi.org/10.5281/zenodo.6822565">2018-07-01/2018-07-11</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839593">2018-07-12/2018-07-17</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839740">2018-07-18/2018-07-28</a></li> <li><a href="https://doi.org/10.5281/zenodo.6839756">2018-07-29/2018-08-22</a></li> </ul> <p>See also: <a href="https://doi.org/10.5281/zenodo.6786918">Real-time oceanography captured by autonomous sailbuoy in Lofoten/Vesterålen 2018</a></p> <p>The sailbuoy Echo 2018 LoVe campaign was part of the research project<br> <a href="https://prosjektbanken.forskningsradet.no/en/project/FORISS/269188">Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach</a></p>
Figs 1–3 in Megachilid bees (Hymenoptera: Megachilidae) of the Nakhchivan Autonomous Republic of Azerbaijan: tribes Lithurgini, Dioxyini, and Megachilini
Figs 1–3. Megachile (Creightonella) albisecta (Klug, 1817), gynandromorph: 1 –
Figs 1–3 in An annotated list of the genus Chlaenius Bonelli, 1810 (Coleoptera: Carabidae) of Xinjiang Uygur Autonomous Region of China
Figs 1–3. Holotype of Chlaenius chloodes Andrewes, 1935. 1 – habitus; 2 – labels; 3 –
Figs 1–4 in To the knowledge of eumenine wasps (Hymenoptera: Vespidae: Eumeninae) of Nakhchivan Autonomous Republic of Azerbaijan
Figs 1–4. Lionotus tegularis Morawitz: 1–3 – ♀, lectotype (1 – habitus, lateral view; 2 –
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Allen Brain Atlas
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OpenNeuro
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