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363 results for “travel”
Figure 2 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 2. Number of months per year with optimal temperature conditions for CBB growth. The optimal temperature conditions were determined by evaluating the area where daily minimum temperature was above 18°C and daily maximum temperature was below 30°C.
Figure 3 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 3. Mean annual number of air passengers traveling between the Hawaiian Islands. Dispersal pathways are shown for those islands that are confirmed to have coffee berry borer. Thicker blue lines indicate higher numbers of passengers.
Comparing travel behavior and opportunities to increase transportation sustainability in small cities, towns, and rural communities
<p>The vast majority of travel behavior and sustainable transportation research has focused on urban areas. A rural perspective is lacking. In this study, we aim to dive deeper into understanding how people travel and their perceptions and opinions about various components of travel in a majority rural state. By speaking directly with Vermonters through in-person interviews, we obtain uniquely personal points of view and analyze them for commonalities and differences between urban, suburban, and rural Vermonters. We ask questions on day-to-day challenges of traveling, suggestions for reducing greenhouse gas (GHG) emissions, responses to fuel prices, and opinions on electric vehicles. Some of our key findings include that rural areas struggle most with traveling long distances to reach services, urban areas are more concerned with traffic, and opinions on electric vehicle (EV) ownership are consistent across the state, with people being likely to consider owning an EV if costs were to decrease. Our interviews identify additional questions that should be evaluated further to help states develop practical and effective policies aimed at reducing GHG emissions in rural areas. We also recommend further in-depth survey research to provide a more complete picture of the potential to shift travel behavior, particularly in rural areas. This research adds to the body of knowledge in a historically understudied population, enabling the research community to better understand and work more closely with small and rural communities to address climate change and achieve deeper GHG emission reductions.</p>
Data for: Evaluating heterogeneity in household travel response to carbon pricing: a study focusing on small and rural communities
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Comparing travel behavior and opportunities to increase transportation sustainability in small cities, towns, and rural communities
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Data from: Movement-integrated habitat selection reveals wolves balance ease of travel with human avoidance in a risk-reward trade-off
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Data for: Analysis of travel time to HIV treatment in sub-Saharan Africa reveals inequities in access to antiretrovirals
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A holistic analysis of passenger travel energy and greenhouse gas intensities
<p>Dataset supporting the analysis of the journal article: Schäfer, A. & Yeh, S. A holistic analysis on passenger travel energy and GHG-intensities. <em>Nature Sustainability</em>, <strong>2020. </strong></p>
Travelling Annotations: Network Analysis as a Tool to Study Glossing Networks in Carolingian Europe
<p>While traditionally, the transmission of medieval texts is studied by means of stemmatics, certian types of textuality are well-known as being particularly resistent to traditional methods. This is also the case with annotations. While annotations can behave text-like, it is more often the case that each individual gloss must be treated as an autonomous entity. In different manuscripts different combinations of glosses are combined so that two manuscripts may contain a very different assembly of glosses and look dissimilar, while being closely related. In such cases, network analysis proves handy as a mean to reveal connection between manuscripts and trace the patterns of transmission of particular annotations, while opening new ways of using this transmission as a proxy for studying the intellectual networks that participated in such an exchange. In this presentation, I will exemplify this approach on the corpus of early medieval annotations to the Etymologiae of Isidore of Seville, the most important medieval Latin encyclopaedia. More specifically, it can be presupposed that most of the glosses to this text came into being in the context of its use for teaching in Carolingian period (c. 750 – 900). Their transfer, thus, may be related to the circulation of schoolmasters, students, and books through the networks of Carolingian schools.</p>
Extract of Local Notice to Mariners, 2018-2019, for Development of AIS Model of Texas Gulf Intracoastal Waterway Travel Times
<p>These files summarize mentions of restrictions or cautions for navigation on the Texas Gulf Intracoastal Waterway contained in Coast Guard Local Notice to Mariners files.</p>
Weekly travel times by direction and sample count for each link in Development of AIS Model of Texas Gulf Intracoastal Waterway Travel Times
<p>Excel spreadsheet containing all of the travel times and sample counts for each link (by direction).</p>
Data from: Non-invasive biophysical measurement of travelling waves in the insect inner ear
Frequency analysis in the mammalian cochlea depends on the propagation of frequency information in the form of a travelling wave (TW) across tonotopically arranged auditory sensilla. TWs have been directly observed in the basilar papilla of birds and the ears of bush-crickets (Insecta: Orthoptera) and have also been indirectly inferred in the hearing organs of some reptiles and frogs. Existing experimental approaches to measure TW function in tetrapods and bush-crickets are inherently invasive, compromising the fine-scale mechanics of each system. Located in the forelegs, the bush-cricket ear exhibits outer, middle and inner components; the inner ear containing tonotopically arranged auditory sensilla within a fluid-filled cavity, and externally protected by the leg cuticle. Here, we report bush-crickets with transparent ear cuticles as potential model species for direct, non-invasive measuring of TWs and tonotopy. Using laser Doppler vibrometry and spectroscopy, we show that increased transmittance of light through the ear cuticle allows for effective non-invasive measurements of TWs and frequency mapping. More transparent cuticles allow several properties of TWs to be precisely recovered and measured in vivo from intact specimens. Our approach provides an innovative, non-invasive alternative to measure the natural motion of the sensilla-bearing surface embedded in the intact inner ear fluid.
Darwin travelling microscope
3D model of small portable brass microscope owned by Charles Darwin and dating from period when he sailed on HMS Beagle as geologist and naturalist. Manufactured by Cary of London, 1826-30. Source: Objaverse 1.0 / Sketchfab
iTFM Matlab Code for Improved formulation of travelling fires
<p>This is the iTFM code for calculations in Matlab of gas temperature in improved formulation of travelling fires. It is written by Egle Rackauskaite and Guillermo Rein, Imperial College London, UK, and it is based on the journal paper (doi:10.1016/j.istruc.2015.06.001):</p> <p>E Rackauskaite, C Hamel, A Law, G Rein, <em>Improved formulation of travelling fires and application to concrete and steel structures</em>, <strong>Structures</strong>, 2015. http://dx.doi.org/10.1016/j.istruc.2015.06.001</p> <p>Contact authors at g.rein@imperial.ac.uk and reingu@gmail.com<br /> Work funded by Engineering and Physical Sciences Research Council and Arup<br /> File published under a Creative Commons license CC BY 4.0</p>
Gain and output power of Traveling Wave Tube at W-band
<p>Results of the W-band TWT gain and output power simulated by using MAGIC 3D Particle in Cell Simulators. These results were presented in the paper title "W-band TWTs for New Generation High Capacity Wireless Networks" presented at the 17th International Vacuum Electronics Conference.</p>
Experiments with Frequency Fitness Assignment based Algorithms on the Traveling Salesperson Problem
<p><strong>1. Introduction</strong></p><p>In this archive, we provide the implementation and experimental results of eight different algorithms to solve Traveling Salesperson Problem (TSP) instances from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p><p>A TSP is defined by a fully-connected weighted graph of n cities. The goal is to find the overall shortest tour that visits each cities exactly once and returns to its starting point. The TSP is NP-hard. We consider 56 symmetric instances from the well-known <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p><p>Solutions in our work are stored in the path representation, where such a tour is encoded as a permutation x of the numbers 1 to n, each identifying a city. If a city appears at index j in the permutation x, then it will be the jth city to be visited. This means that a tour x will pass the following edges: (x[1], x[2]), (x[2], x[3]), (x[3], x[4]), … (x[n-1], x[n]), (x[n], x[1]).</p><p><strong>2. Directory Structure</strong></p><p>This dataset is split into multiple separate <i>tar.xz</i> archives. These can be unpacked in the same folder and will produce the directory structure described below. Each archive contains this note and the license information, but apart from that, there is no redundancy.</p><p>This archive contains the following directories:</p><ul><li>source contains the Python source codes needed to run the experiment.<ul><li>moptipy-main is a local copy of the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package used for our experiment.</li><li>tsplib contains the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> data. This includes the instances used in our experiments as files in text format with suffix .tsp. If an optimal tour is given by TSPLib, it is stored in a text format file with suffix .opt.tour and name prefix identical to the instance file. In other words, the file eil51.tsp contains the TSP instance eil51 and the file eil51.opt.tour contains the corresponding optimal tour. Both the TSP instances and optimal tours can be downloaded from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/</a>. We also include the documentation of TSPLIB in file <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">tsp95.pdf</a> documenting them. We further include the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/TSPFAQ.html">TSPLIB FAQ</a> both as HTML and PDF file (tsplib_faq.html and tsplib_faq.pdf) and the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html">list of known optimal tour lengths</a> as HTML and PDF file (optimal_tour_lengths_of_symmetric_tsps.html, optimal_tour_lengths_of_symmetric_tsps.pdf). Notice that, while the TSP instances we used are Euclidean, all distances are converted to integers as prescribed by the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">documentation</a>.</li></ul></li><li>results is the directory with the log files. Each log file contains information of one run, i.e., one execution of one algorithm on one problem instance. All improving moves of a run as well as the final solution are stored in the log file. The direct sub-folders results represent the algorithms and contain one folder per TSP instance, which, in turn, contain the log files.</li><li>evaluation is a folder with the extracted evaluation and figures</li><li>evaluation_edited is a folder with evaluation figures slightly edited for better visual appeal (but obviously without changing any result / scientific content)</li><li>evaluator is a folder with a Python script main.py that generates all the files in evaluation from the data it finds in results. It requires the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package being installed for running in the version given in requirements.txt.</li></ul><p><strong>3. Algorithms</strong></p><p>The (1+1) EA is the most basic evolutionary algorithm and also be considered as a randomized local search. It starts with one random solution/permutation xc and computes its length yc=f(xc). In each iteration, it applies a unary search operator op to obtain a new tour xn=op(xc) and computes its length yn=f(xn). If yn<=yc, then it will accept the new tour and set xn=xn and yc=yn. The results of this algorithm are given in folder results/ea_revn.</p><p>FFA is a fitness assignment process that takes place before this last step in the EA. We integrate FFA into the (1+1) EA and obtain the (1+1) FEA. This algorithm uses an additional table H which counts, for any tour length y, how often it has been seen during the search so far. After the new tour xn is created and its objective value yn is computed, the (1+1) FEA sets H[yc] = H[yc] + 1 and H[yn] = H[yn] + 1. It will accept xn if and only if H[yn] <= H[yc] and, only in this case, set xn=xn and yc=yn. The results of this algorithm are given in folder results/fea_revn.</p><p>SA is the classical simulated annealing algorithm. In our experiment, it will accept the new solution xn with probability P. If the new solution is better, the acceptance probability P is 1. For worse solutions, the probability is between 0 and 1, i.e., sometimes, worse solution are also accepted. This algorithm has a temperature cooling schedule. It starts at an initial temperature and over time, the temperature decreases. The probability P of accepting the worse solution depends on the temperature and decreases as well. The results of this algorithm are given in folder results/sa_revn.</p><p>An FFA-based version of SA uses the frequency fitness instead of the objective values in all acceptance decisions. The results of this algorithm are given in folder results/fsa_revn.</p><p>EAFEA(A) is a hybrid which alternates between the EA and the FEA and copies a solution from the FEA to the EA if it has an entirely new objective value, i.e., if H[yn] = 1. The results of this algorithm are given in folder results/eafea2_revn.</p><p>SAFEA(A) is a hybrid which alternates between the SA and the FEA and copies a solution from the FEA to the SA if it has an entirely new objective value, i.e., if H[yn] = 1. The results of this algorithm are given in folder results/safea2_revn.</p><p>EAFEA(B) is a hybrid which alternates between the EA and the FEA and copies a solution from the FEA to the EA part if it has a better objective value. The results of this algorithm are given in folder results/eafea_revn.</p><p>SAFEA(B) is a hybrid which alternates between the SA and the FEA and copies a solution from the FEA to the SA part if it has a better objective value. The results of this algorithm are given in folder results/safea_revn.</p><p>We apply all algorithms with the same unary operator reverse, which reverses a randomly chosen subsequence of the tour. This operator is also often called a "2-opt move". It has the advantage that the new objective value of a new solution can be computed in O(1) if the objective value of the solution from which it is derived is known.</p><p><strong>4. How to Run the Experiment</strong></p><p>First, you need to make sure to have all the dependencies installed that this program requires. You can do this by executing the following command in the terminal:</p><p>pip install matplotlib numba numpy psutil scikit-learn moptipy moptipyapps</p><p>Now enter the source directory, i.e., the directory containing the run.py file, in your terminal. Depending on your system configuration and whether you run Windows or Linux, you can start the program with <i>one</i> of the commands below. (If running the first command returns with an error, just try the next one in the list.)</p><ul><li>python3 -m run</li><li>python -m run</li><li>python run.py</li><li>python3 run.py</li></ul><p>Then the experiment will run. It will automatically create a sub-folder results in source and place all log files that are generated into it. Be careful: The experiment will take a long time. However, if you have multiple CPUs, you can simply start several instances of this program in independent terminals. Each instance will then conduct different runs. This also works if this folder is shared over the network, in which case you can run multiple processes on multiple PCs.</p><p>Side note: This experiment uses the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package for implementing its algorithms, running the experiments, and gathering their results. If you want to install moptipy on your system instead of using the version supplied here, you can install it via pip install moptipy. It also uses moptipyapps to load some data.</p><p><strong>5. Literature</strong></p><ul><li>Frequency Fitness Assignment (FFA):<ol><li>Thomas Weise, Zhize Wu, Xinlu Li, Yan Chen, and Jörg Lässig. Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient. IEEE Transactions on Evolutionary Computation (TEVC). 2022. Early Access. doi:<a href="https:doi.org/10.1109/TEVC.2022.3191698">10.1109/TEVC.2022.3191698</a>.</li><li>Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value. <i>IEEE Transactions on Evolutionary Computation</i> 25(2):307–319. April 2021. Preprint available at <a href="http://arxiv.org/abs/2001.01416">arXiv:2001.01416v5</a> [cs.NE] 15 Oct 2020. doi:<a href="http://dx.doi.org/10.1109/TEVC.2020.3032090">10.1109/TEVC.2020.3032090</a>. Experimental results and source code are available at doi:<a href="http://doi.org/10.5281/zenodo.3899474">10.5281/zenodo.3899474</a>.</li><li>Tianyu Liang, Zhize Wu, Jörg Lässig, Daan van den Berg, and Thomas Weise. Solving the Traveling Salesperson Problem using Frequency Fitness Assignment. In Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, and Keeley A. Crockett, editors, Proceedings of the IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI'22), part of the IEEE Symposium Series on Computational Intelligence (SSCI 2022). December 4–7, 2022, Singapore, pages 360–367. IEEE. doi:<a href="https://doi.org/10.1109/SSCI51031.2022.10022296">10.1109/SSCI51031.2022.10022296</a>.</li><li>Thomas Weise, Mingxu Wan, Ke Tang, Pu Wang, Alexandre Devert, and Xin Yao. Frequency Fitness Assignment. <i>IEEE Transactions on Evolutionary Computation (IEEE-EC)</i> 18(2):226-243, April 2014. doi:<a href="http://dx.doi.org/10.1109/TEVC.2013.2251885">10.1109/TEVC.2013.2251885</a>.</li><li>Thomas Weise, Xinlu Li, Yan Chen, and Zhize Wu. Solving Job Shop Scheduling Problems Without Using a Bias for Good Solutions. In <i>Genetic and Evolutionary Computation Conference Companion (GECCO'21 Companion),</i> July 10-14, 2021, Lille, France. ACM, New York, NY, USA. ISBN 978-1-4503-8351-6. doi:<a href="http://doi.org/10.1145/3449726.3463124">10.1145/3449726.3463124</a>.</li><li>Thomas Weise, Yan Chen, Xinlu Li, and Zhize Wu. Selecting a diverse set of benchmark instances from a tunable model problem for black-box discrete optimization algorithms. <i>Applied Soft Computing Journal (ASOC)</i>, 92:106269, June 2020. doi:<a href="http://dx.doi.org/10.1016/j.asoc.2020.106269">10.1016/j.asoc.2020.106269</a>.</li><li>Thomas Weise, Mingxu Wan, Ke Tang, and Xin Yao. Evolving Exact Integer Algorithms with Genetic Programming. In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC'14), Proceedings of the 2014 World Congress on Computational Intelligence (WCCI'14)</i>, pages 1816-1823, Beijing, China, July 6-11, 2014. Los Alamitos, CA, USA: IEEE Computer Society Press. ISBN: 978-1-4799-1488-3. doi:<a href="http://dx.doi.org/10.1109/CEC.2014.6900292">10.1109/CEC.2014.6900292</a>.</li></ol></li><li>Traveling Salesperson Problem (TSP):<ol><li>Pedro Larrañaga, Cindy M. H. Kuijpers, Roberto H. Murga, I. Inza, and S. Dizdarevic. Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. <i>Artificial Intelligence Review,</i> 13(2):129–170, April 1999. Kluwer Academic Publishers, The Netherlands. doi:<a href="https://doi.org/10.1023/A:1006529012972">10.1023/A:1006529012972</a>.</li><li>Gerhard Reinelt. TSPLIB — A Traveling Salesman Problem Library. <i>ORSA Journal on Computing</i> 3(4):376-384. 1991. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/</a>.</li><li>Gerhard Reinelt. TSPLIB95. 1995. Heidelberg, Germany: Universität Heidelberg, Institut für Angewandte Mathematik. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf</a>.</li><li>Thomas Weise, Raymond Chiong, Ke Tang, Jörg Lässig, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, and Xin Yao. Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. <i>IEEE Computational Intelligence Magazine (CIM)</i> 9(3):40-52, August 2014. doi:<a href="http://dx.doi.org/10.1109/MCI.2014.2326101">10.1109/MCI.2014.2326101</a>.</li><li>Eugene Leighton Lawler, Jan Karel Lenstra, Alexander Hendrik George Rinnooy Kan, and David B. Shmoys. <i>The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization.</i> Wiley Interscience. 1985.</li><li>David Lee Applegate, Robert E. Bixby, Vasek Chvatal, and William John Cook. <i>The Traveling Salesman Problem: A Computational Study.</i> Princeton University Press. 2007.</li><li>Gregory Z. Gutin and Abraham P. Punnen, editors. <i>The Traveling Salesman Problem and its Variations.</i> Volume 12 of Combinatorial Optimization. Kluwer Academic Publishers. 2002. doi:<a href="https://dx.doi.org/10.1007/b101971">10.1007/b101971</a>.</li></ol></li><li>Software:<ol><li>The Metaheuristic Optimization in Python Package <a href="https://thomasweise.github.io/moptipy">moptipy</a></li></ol></li></ul><p><strong>6. License</strong></p><p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> in directory source/tsplib, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file LICENSE.txt in this archive.</p><p><strong>7. Contact</strong></p><p>If you have any questions or suggestions, please contact</p><p>Mr. Tianyu LIANG (梁天宇) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥学院</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:liangty@stu.hfuu.edu.cn">liangty@stu.hfuu.edu.cn</a>.</p>
Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics
<p>IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). </p> <p><em>We thank the institutes who maintain the IMAGE Magnetometer Array (<a href="https://space.fmi.fi/image/">https://space.fmi.fi/image/</a>): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.</em></p>
Time Travelers Tavern Block out
here i have started the idea for my envifroment brief in Uni The idea is some sort of time travellers tavern type thing where all these time travellers can just stop and rewind so im gonna have references to a bunch of different time travel movies so i've got the phone box coming out of the worm hole from bill and ted and i've got a DeLorean and the funky device from men in black and i'm thinking of trying to add the hot tub time machine as well. Source: Objaverse 1.0 / Sketchfab
Traveling in Autumn Mountains, 17th-18th C CE
Traveling in Autumn Mountains, 17th-18th C CE, now in the collection of the Minneapolis Institute of Art. From the jade's description on [artsmia.org](www.artsmia.org): *'In this mountain scene, an official riding a donkey is followed by his attendant who carries an umbrella; they cross a rustic bridge over a waterfall. Above them a similar group in smaller scale, as if in the far distance, crosses another bridge. This type of jade mountain had its beginnings in the late Ming period. The setting, with its figures, trees, bridges and animals, represents a translation of the orthodox landscape painting tradition into jade sculpture. Numerous depictions similar to this one of scholars traveling in or contemplating mountain scenery can be found in paintings of the period. The theme of humankind's harmony with nature was favored by the Taoists and the literati.'* More information [here](https://collections.artsmia.org/art/4326/traveling-in-autumn-mountains-china) Source: Objaverse 1.0 / Sketchfab
Propagational Isotropy of Large Scale Traveling Ionospheric Disturbances Over Australia And New Zealand due to the 2022 Tonga Volcanic Eruption
<p>This data repository contains global TEC processed data from 14 - 16 January 2022. The original data were obtained from the GNSS-TEC database available at https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/ provided by the Institute for Space-Earth Environment Research, Nagoya University. The data is in .mat format (binary Matlab file) with the following data matrices:</p> <ol> <li>Coordinates (geographic coordinates - Latitude, Longitude)</li> <li>dTEC1 (detrended TEC)</li> <li>TimeTEC_combined (time series absolute TEC for each geographic coordinate)</li> </ol> <p>Data has a time resolution of 5 min in each column. </p>
ScienceDex guides
Understand access before you commit
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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.