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301 results for “law”
Law_methane dispersal
<p>This repository contains the temperature, salinity, dissolved oxygen, dissolved methane and bottom current measurements presented in the publication ‘Dispersion and fate of methane emissions from cold seeps on Hikurangi Margin, New Zealand” submitted to Frontiers in Marine Science. The data were obtained from CTD casts collected at three methane seeps along the Hikurangi margin as well as a mooring outfitted with an ADCP and a MicroCat depolyed at ~15m above the seafloor at one of the seeps. <br>Additionally, the Regional Ocean Modelling System (ROMS) was used to simulate the advection of passive tracers (a proxy for methane) released at five seeps located along the southern Hikurangi margin over a period of one year. The animations from the ROMS model output shows the lateral transport and dispersal of dissolved methane plumes along the Hikurangi Margin, east of New Zealand. The model domain spans the continental shelf of the eastern North Island of New Zealand at a resolution of 2 km. Passive tracers were released 150-200 m above the seafloor (bottom three grid cells) at each of the five seep sites. </p>
Replication package for "Chain Restaurant Calorie Posting Laws, Obesity, and Consumer Welfare"
<p>This package contains the data, programs, and instructions to replicate the manuscript "Chain Restaurant Calorie Posting Laws, Obesity, and Consumer Welfare" by Charles Courtemanche, David Frisvold, David Jimenez-Gomez, Marietou Ouayogode, and Michael Price, which is forthcoming at JEEA.</p>
whanley/ilcorpus: v1.02 International Law corpus
<p>Now with six journals, including Revue générale de droit international public</p>
Spatial distribution of naming patterns in Russian law firms through text embeddings: data and code
<p>Data and code for a paper about spatial analysis of law firms naming in Russia. See also the attached paper's DOI and URL as well as the link to the GitHub repository.</p>
Maximizing Scholarly Access, Impact, and Visibility at the UW Law Library
<p>A video created by UW Law Librarians for the 2021 AALL Innovation Showcase highlighting how the UW Law Library helps UW Law faculty members increase the access, impact, and visibility of their scholarship.</p>
Data for "Do the Laws of Physics Prohibit Counterfactual Communication"
<p>Data (and code used to analyse the data) used in the weak measurement experiment in the paper "Do the Laws of Physics Prohibit Counterfactual Communication" (arXiv:<a href="https://arxiv.org/abs/1806.01257">1806.01257</a>).</p>
Data from: Compression principle and Zipf's law of brevity in infochemical communication
<p>Compression has been presented as a general principle of animal communication. Zipf's law of brevity is a manifestation of this postulate and can be generalised as the tendency of more frequent communicative elements to be shorter. Previous works supported this claim, showing evidence of Zipf's law of brevity in animal acoustical communication and human language. However, a significant part of the communicative effort in biological systems is carried out in other transmission channels, such as those based on infochemicals. To fill this gap, we seek, for the first time, shreds of evidence of this principle in infochemical communication by analysing the statistical tendency of more frequent infochemicals to be chemically shorter and lighter. We analyse data from the largest and most comprehensive open-access infochemical database known as Pherobase, recovering Zipf's law of brevity in interspecific communication (allelochemicals) but not in intraspecific communication (pheromones). Moreover, these results are robust even when addressing different magnitudes of study or mathematical approaches. Therefore, different dynamics from the compression principle would dominate intraspecific chemical communication, defying the universality of Zipf's law of brevity. To conclude, we discuss the exception found for pheromones in the light of other potential communicative paradigms such as pressures on successful communication or the Handicap principle.</p>
A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies
<p>This repository contains the data, codes and files required to reproduce the results of the manuscript "A modified Beer–Lambert–Bouguer law for nonrandom distributions and its application in gap probability calculations for heterogeneous canopies" submitted to the Journal of Advances in Modeling Earth Systems (JAMES).</p>
Raw Data to 'Experimental verification of the area law of mutual information in quantum field theory', arXiv:2206.10563
<p><strong>Absorption images representing the raw data for arXiv:2206.10563</strong></p> <p>'scan5722.zip' contains the raw data for figures 2 and 3.</p> <p>'scan5831.zip' and 'scan9617.zip' contain the raw data for figure 5, right and left, respectively.</p> <p>All three datasets, 9617, 5722, and 5831, are used to obtain the data points in figure 4, from low to high temperatures.</p> <p> </p> <p><strong>Scans 5722 & 5831</strong></p> <p>The absorption images are numbered consecutively.</p> <p>The first image for scans 5722 and 5831 is taken along the axial (longitudinal) direction with our 'longitudinal' imaging system after 10 ms time of flight (TOF) to measure the atom number balance between the two wells. The measurement is performed before ramping up the DW barrier. Two images are taken in each cycle: One shot with atoms ('1-atomcloud.tif') and a second image to record the intensity of the imaging beam without atoms ('1-withoutatoms.tif'). These two pictures are used to extract the atomic density (see the Matlab script).</p> <p>The second image for scans 5722 and 5831 is taken in the direction of the double-well (DW) separation with our 'transverse' imaging system. The measurement is performed before ramping up the DW barrier. The image is taken after 11.2 ms TOF.</p> <p>The subsequent images record the interference fringes for the different evolution times (again, always pairs '-atomcloud.tif' and '-withoutatoms.tif'). They are taken with our 'vertical' imaging system after 15.6 ms TOF. The imaging direction is perpendicular to the weakly confined direction of the clouds and the DW separation.</p> <p>The recorded evolution times for scan 5722 are -1.9 ms (right before ramping up the DW barrier), 0 ms (right after the DW barrier is ramped up), and then in steps of 2.5 ms until 65 ms. This means that '3-atomcloud.tif' corresponds to -1.9 ms, and '30-atomcloud.tif' corresponds to 65 ms. This completes the 'first repeat'. The next two shots, '31-atomcloud.tif' and '32-atomcloud.tif', belong to the 'second repeat' and are again taken with the 'longitudinal' & 'transverse' imaging systems, respectively. The picture '33-atomcloud.tif' is again taken with the 'vertical' imaging and corresponds to -1.9 ms. And so forth.</p> <p>In the same way, the pictures scan 5831 are ordered. The evolution times (in ms) for these two scans are:</p> <p>scan 5722: -1.9 from 0 to 65 (in steps of 2.5)</p> <p>scan 5831: -1.8, from 0 to 65 (in steps of 2.5)</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up. 0 is always right after the barrier was ramped up.</p> <p> </p> <p><strong>Scan 9617</strong></p> <p>This scan only contains images with the 'vertical' imaging system with 15.6 ms TOF. The evolution times (in ms) are as follows:</p> <p>scan 9617: -2.8, from 0 to 15 (in steps of 1.5), 22, 25, 28</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up. 0 is always right after the barrier was ramped up.</p> <p>This means, that '1-atomcloud.tif', '16-atomcloud.tif', '31-atomcloud.tif' and so on correspond to -2.8 ms, and '15-atomcloud.tif', '30-atomcloud.tif', '45-atomcloud.tif' and so on correspond to 28 ms.</p> <p> </p> <p><strong>Matlab script</strong></p> <p>In addition to the data, a Matlab script (calc_atomic_density.m) illustrates how to extract the two-dimensional atomic density from the absorption images. It contains all relevant parameters of the imaging systems.</p>
Strength-mass scaling law governs mass distribution inside honey bee swarms
<p>To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000–10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the ≈1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.</p>
Statue The Sign Of Law
Defending a [Non Molestation Order Defense](https://www.incourt.co.uk/post/can-you-appeal-a-non-molestation-order) requires the intricate gathering of evidence and may involve taking statements from witnesses as well as gathering other evidence regarding your character and behaviour. It is important that as soon as you are served with a Non-Molestation Order that you obtain expert legal advice quickly. Source: Objaverse 1.0 / Sketchfab
Screenshot case-law-app
<p>This is a screenshot of the Case Law network visualization application (https://nlesc.github.io/case-law-app/), to be used in presentations or articles.</p>
Viscosity Measurements at High Pressures: A Critical Appraisal of Corrections to Stokes' Law
<p>Included are central data and codes for the manuscript "Viscosity Measurements at High Pressures: A Critical Appraisal of Corrections to Stokes' Law", published May 2, 2024.</p> <p>Reference for full article:</p> <p>Ashley, A.W., Mookherjee, M., Xu, M., Yu, T., Manthilake, G., & Wang, Y. (2024). Viscosity measurements at high pressures: A critical appraisal of corrections to Stokes' Law. Journal of Geophysical Research: Solid Earth, 129, e2023JB028489. https://doi.org/10.1029/2023JB028489</p>
20220108-MTOA: Accuracy improvement from undertaking additional overlapping tasks follows a law of diminishing returns.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: MTOA-20220108<br><br>Experiment design: Agents improve average accuracy by tackling several tasks.<br><br>Experiment setting: Agents are trained with respect to different tasks and then coordinate upon acting on them. Each time they disagree, one agent adapts its knowledge with respect to the current task.<br><br>Hypotheses: The higher is the number of carried out tasks, the higher the average accuracy is.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20220108-MTOA">https://sake.re/20220108-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br></p>
Data for coherence measurements of polaritons in thermal equilibrium reveal a power law for two-dimensional condensates
<p>All the raw data sets collected for this project are included in this submission. The code for the numerics is also included. 'Readme.text' files are included with the data sets explaining what the data sets are and how to read them. </p>
Extracted experimental data for research paper titled "Micro-thermomechanical Modeling of Rocks with Temperature-dependent Friction and Damage Laws"
<p>This repository contains the experimental data on stress-strain curves extracted from the following original publications for constitutive model validation in our manuscript.</p> <p>[1] Jinping marble: Zhong, Y. Y. (2017). Research on mechanical properties of marble and the effects on rock burst under thermal-mechanical coupling (in Chinese) (Master’s thesis, Chengdu University of Technology). doi: 10.26986/d.cnki.gcdlc.2017.000109.</p> <p>[2] Beibei sandstone: Long, L. J. (2021). Study on mechanical and seepage properties of sandstone under the coupling of temperature-seepage-stress (in Chinese) (Doctoral dissertation, Chongqing University). doi: 10.27670/d.cnki.gcqdu.2021.001009.</p> <p>[3] Gongjue granite: Zhou, H. Y., Liu, Z. B., Shen, W. Q., Feng, T., & Zhang, G. Z. (2022). Mechanical property and thermal degradation mechanism of granite in thermal-mechanical coupled triaxial compression. International Journal of Rock Mechanics and Mining Sciences, 160, 105270. doi: 10.1016/j.ijrmms.2022.105270.</p>
Arcsine laws of light
<p>Paul Lévy's arcsine laws are an example of emergent statistical structure in nature. They has been observed over the years in a large variety of unrelated physical systems. Yet, they have never been studied in the context of coherently driven resonators. In our experiment, we show that these laws arise asymptotically in the time-integrated transmitted intensity of an optical resonator. Here, we publish the dataset used for our manuscript. </p> <p>DOI: https://doi.org/10.1103/PhysRevLett.132.133801</p> <p>The purpose of this upload is to facilitate the reproduction and verification of the results presented in the above manuscript. Kindly contact the corresponding author for any queries.</p>
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
<p>Datasets provided in this storage are introduced in the paper: <em>Towards Neural Scaling Laws for Foundation Models on Temporal Graphs</em></p> <ul> <li>Each .csv file represents all transactions of the token network that has the same name as the file name (<em><tokenname.csv>)</em></li> <li>Each transaction corresponds to a row in each file.</li> <li>Each transaction has: <ul> <li> blockNumber : is the block ID of Ethereum that includes this transaction</li> <li>timestamp: time that the transaction is made in UNIX timestamp format</li> <li>tokenAddress : the address that specifies a unique ERC20 token</li> <li>from: address of sender</li> <li>to: address of receiver</li> <li>value: the amount the transaction</li> <li>fileBlock: we split the whole number of blocks count to 35 buckets and assigned the bucket ID to the transaction to trace the blocks </li> </ul> </li> <li>To use the same setting as described in the papers, we include edge list and label that contain node interactions and labels for each snapshot in each token network. <ul> <li>Each transaction in the edge list also has "from","to" and "amount" fields, but with an additional "snapshot" field to indicate the index of the snapshot that the transaction below to</li> <li>Each row in label file indicates the ground truth label of the snapshot having an index corresponding to the index of the row (e.g first row indicates the label of the first snapshot)</li> <li>We provided the way to generate edge lists and label files in the following Github repository: https://github.com/benjaminnNgo/ScalingTGNs/blob/main/script/utils/TGS.py</li> </ul> </li> <li>However, we also provide raw <em>.csv </em> to divide into generate <em>edgeslist </em>and <em>label with a different setting.</em></li> </ul> <p><br><br></p>
Investigating the neural basis of the loud-first principle of the Iambic-Trochaic law
Open the record for dataset details and reuse information.
Word frequencies and text data for the study of Statistical Laws in Complex Systems
<p>The files contain the data reported in the manuscript "Statistical Laws in Complex Systems" and in the repository <a href="https://github.com/edugalt/StatisticalLaws">https://github.com/edugalt/StatisticalLaws</a></p> <p>The files contain list of word frequencies and filtered word texts, computed from the data provided in <a href="https://gutenberg.org/">Project Gutenberg</a>, <a href="https://en.wikipedia.org/wiki/Wikipedia:Database_download">Wikipedia</a>, and the <a href="https://books.google.com/ngrams/">Google N-gram database.</a></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.