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301 results for “law”
Laws of concatenated perception: Vision goes for novelty, Decisions for perseverance
<p>Datasets in Excel (long format) of Experiment 1-8.</p> <p>Each dataset contains raw data (i.e., before preprocessing) of single-trial variables for each individual subject (observer).</p> <p>Columns include dependent variables (e.g., reported orientation [reported], reaction times [rt]) and independent variables (e.g., orientation, condition) that are specific to each individual experiment.</p> <p>To reproduce the main figures and results, data have to be pre-processed and analyzed according to the pipeline described in details in the manuscript (Methods section).</p>
Supporting dataset underlying "Human cortical folding across regions within individual brains follows universal scaling law"
<p>This is the dataset underlying "Human cortical folding across regions within individual brains follows universal scaling law", the matlab scripts enable the user to direct reproduce the figures in the paper.</p> <p>2020 update: The matlab code for extraction of lobe-wise measures from Freesurfer subjects is now published on <a href="https://doi.org/10.5281/zenodo.3608675">Zenodo</a> and <a href="https://github.com/cnnp-lab/CorticalFoldingAnalysisTools">Github</a>.</p>
Derivatives Law and Practice in the Middle East
<p>The purpose of the paper, part of a project by Boston University Law School, is to examine derivatives law in some Middle Eastern countries including the UAE.</p>
Claudinha Data Protection Law (LGPD) Corpus
<p>This dataset contains privacy policies paragraphs in Portuguese. Each paragraph was annotated by an expert annotator, using a guideline (DOI: <a href="https://doi.org/10.5281/zenodo.13371432" target="_blank" rel="noopener">10.5281/zenodo.13371432</a>). Two types of notes were made: the category of the Brazilian Data Protection (LGPD) Law that fits the text and the level of compliance with LGPD.</p> <p>The categories are divided into three blocks: Omission of data required by law (block 1), Data processing (block 2), Unclear language and others (block 3). There are 3 levels of compliance, with level 1 being in compliance with the law, level 2 being partial potential non-compliance, and level 3 being potential total non-compliance.</p> <p>There are 6341 distinct paragraphs. The corpus has more records (8341 clauses), as there are duplications, since a paragraph can belong to more than one guideline category. Pontetially non-compliant clauses corresponds to 1413 records (21.9%). Below, statistics regarding the number of paragraphs belonging to each category and frequencies of categories in privacy policies.</p> <table> <tbody> <tr> <td> <h2><strong>Category</strong></h2> </td> <td> <h2><strong>Number of clauses</strong></h2> </td> <td> <h2><strong>Document frequency</strong></h2> </td> </tr> <tr> <td><strong>Block 1: Omission of data required by law</strong></td> </tr> <tr> <td>Access to data</td> <td>283</td> <td>61</td> </tr> <tr> <td>Anonymization, blocking and deletion</td> <td>204</td> <td>46</td> </tr> <tr> <td>Automated decision</td> <td>45</td> <td>19</td> </tr> <tr> <td>Category of processed data</td> <td>1427</td> <td>63</td> </tr> <tr> <td>Controller identification</td> <td>107</td> <td>47</td> </tr> <tr> <td>Data correction</td> <td>154</td> <td>52</td> </tr> <tr> <td>Duration of treatment</td> <td>234</td> <td>52</td> </tr> <tr> <td>Existence of treatment</td> <td>142</td> <td>37</td> </tr> <tr> <td>Express consent</td> <td>176</td> <td>41</td> </tr> <tr> <td>ID and contact DPO</td> <td>150</td> <td>46</td> </tr> <tr> <td>Non-consent</td> <td>91</td> <td>33</td> </tr> <tr> <td>Personal data source</td> <td>471</td> <td>55</td> </tr> <tr> <td>Portability</td> <td>97</td> <td>36</td> </tr> <tr> <td>Purpose of sharing</td> <td>119</td> <td>16</td> </tr> <tr> <td>Purpose of treatment</td> <td>1620</td> <td>71</td> </tr> <tr> <td>Revoke consent</td> <td>154</td> <td>50</td> </tr> <tr> <td>Right of deletion</td> <td>173</td> <td>40</td> </tr> <tr> <td>Third party sharing</td> <td>919</td> <td>69</td> </tr> <tr> <td><strong>Block 2: Data processing</strong></td> </tr> <tr> <td>Advertising</td> <td>215</td> <td>38</td> </tr> <tr> <td>Children data</td> <td>118</td> <td>37</td> </tr> <tr> <td>Cookies</td> <td>432</td> <td>60</td> </tr> <tr> <td>Consent by use</td> <td>339</td> <td>47</td> </tr> <tr> <td>Other consents</td> <td>73</td> <td>30</td> </tr> <tr> <td>Policy changes</td> <td>210</td> <td>61</td> </tr> <tr> <td>"Take it or leave it"</td> <td>50</td> <td>21</td> </tr> <tr> <td><strong>Block 3: Unclear language and others</strong></td> </tr> <tr> <td>Generic expressions</td> <td>244</td> <td>37</td> </tr> <tr> <td>Other unclear clauses</td> <td>94</td> <td>26</td> </tr> </tbody> </table> <p> </p>
Supporting material for "Benford's law as debris flow detector in seismic signals"
<p>Supporting material list,</p> <p>0, Source code for Random_Forest_model;</p> <p>1, Random_Forest_model.pkl, trained machine learning model;</p> <p>2, FiguresSI.zip, 58 debris flow events with Benford's law features, and Exp. fitting curve;</p> <p>3, Table2DebrisFlowEventDetails.xlsx, details of 58 debris flow events and expentional fitting parameters.</p> <p>Note, you can check our GitHub repository<br>https://github.com/Nedasd/Benfords_law_as_mass_movements_detector.git</p> <p> </p>
Non-Newtonian Power-law fluid simulations in rectangular channel
<h1>Velocity distribution in rectangular channel for Power law fluid.</h1> <p>Using viscosity: eta(x)=K*(x/gamma0)^(n-1)/gamma0</p> <p>Solved the PDE equation: https://doc.comsol.com/5.5/doc/com.comsol.help.comsol/comsol_ref_equationbased.23.008.html<br>in rectangular channel in COMSOL with<br>c=eta(sqrt(d(u,x)^2+d(u,y)^2), f=0.013333333 [Pa*s]<br>e_a, d_a, alpha, beta, gamma=0<br>gamma0=1[1/s]<br>Boundary conditions on x=0, y=0, x=witdh, y=height is u=0</p> <p>Output file contains: Coordinates X, Y [mm] on triangular grid and Velocity u [m/s]</p> <p>File specific parameters:<br>"H2O_100x096.txt" - n=1, K=0.013333333 [Pa*s], height=1 [mm], width=0.96 [mm]<br>"10pFBS_37C_100x115.txt" - n=0.5, K=0.00541540444218501 [Pa*s], height=1 [mm], width=1.15 [mm]<br>"10pFBS_097x092.txt" - n=0.32, K=0.00541540444218501 [Pa*s], height=0.97 [mm], width=0.92 [mm]</p>
Bimanual Grasping adheres to Weber's Law
<p>Datasets associated with the following publication:</p> <p><em>Hesse, C., Harrison, R. E., Giesel, M., & Schenk, T. (2021). Bimanual Grasping adheres to Weber’s Law. i-Perception (under revision)</em></p> <p>The folder contains four data files. </p> <p>DATA_MGA_N20.txt: contains the size of the maximum hand opening for grasping and the size of the aperture during manual estimation for each trial and every participant</p> <p>DATA_EndPos_N20.txt: contains the position of each hand (left and right) on the box at the moment the object was lifted in grasping trials.</p> <p>SummaryDATA_SD_N20.txt and SummaryDATA_IQR_N20.txt</p> <p>contain the variability of the maximum hand opening (indicating JNDs) in grasping and the estimation aperture in the perceptual task for every participant and condition (can also be calculated from the single trial data) once determined as standard deviations (SD) and once determined as the interquartile range (IQR).</p> <p>There is a separate description file (README.rtf) providing further relevant information for the data files uploaded.</p> <p>For further questions, please contact:<br> c.hesse[at]abdn.ac.uk</p>
Wikidata Subsets of 6 Wikiproject (Gene Wiki, Taxonomy, Astronomy, Music, Law, Ships)
<p>This dataset contains the N-Triples files of 6 Wikidata subsets corresponding to 6 different Wikidata WikiProject extracted from October 2016 and June 2021 dumps. For each project, there are .nt.gz files containing RDF data. The projects are:</p> <ul> <li>Gene Wiki: There is a GeneWiki_2016.zip (extracted from the 2016 dump) and a GeneWiki_2021.zip (extracted from the 2021 dump), Each of which contains 25 .nt.gz files extracted by the WDumper tool.</li> <li>Astronomy: Astronomy_2016.nt.gz (extracted from the 2016 dump) and Astronomy_2021.nt.gz (extracted from the 2021 dump).</li> <li>Taxonomy: There is a Taxonomy_2016.zip (extracted from the 2016 dump) and a Taxonomy_2021.zip (extracted from the 2021 dump), Each of which contains two .nt.gz files.</li> <li>Law: Law_2016.nt.gz (extracted from the 2016 dump) and Law_2021.nt.gz (extracted from the 2021 dump).</li> <li>Music: Music_2016.nt.gz (extracted from the 2016 dump) and Music_2021.nt.gz (extracted from the 2021 dump).</li> <li>Ships: Ships_2016.nt.gz (extracted from the 2016 dump) and Ships_2021.nt.gz (extracted from the 2021 dump).</li> </ul> <p> </p> <ul> </ul>
A generalized Hooke's law to study the transport properties of rocks
<p>The nonlinear characteristics of the rock transport properties as a function of stress (permeability and electrical conductivity in this study) is closely related to the geometry of the pore space, which consists of stiff pores, cracks and fractures. We consider two behaviors of the pore space, one linear and the other exponential, related to the stiff pores and fractures, respectively, where a generalized Hooke's law describes the relation between stress and strain. Using this theory, we derive relations between porosity, transport properties and effective stress (confining minus pore pressure), which are validated by experimental data of four tight sandstones collected from the Shaximiao Formation of Sichuan Basin, southwest China. The agreement is good. At low effective stresses, the closure of cracks is the main mechanism affecting the transport properties, whose behavior is similar in terms of their parameters. Subsequently, experimental data of six tight sandstones from the Yanchang Formation, collected from the Ordos Basin, west China, are employed to confirm the previous results, indicating that the fluid and electrical current follow the same path in the pore space. In addition, we find that the effect of metallic minerals on the electrical conductivity is more significant than that of clay.</p>
Dataset Literatur Review on Cyber Law Privacy
<p>berikut merupakan Dataset Literatur Review on Cyber Law Privacy</p>
Dataset Literatur Review on Cyberlaw OR Cyber Law AND Identification
<p>Berikut merupakan Dataset Literatur Review on Cyberlaw OR Cyber Law AND Identification</p>
Dataset Literatur Review on Cyber Law Focus Groups
<p>berikut merupakan Dataset Literatur Rivew on Cyber Law Focus Groups</p>
Dataset literatul reviuw on Cyberlaw OR Cyber Law AND image splicing
<p>Ini merupakan dataset dari artikel dataset literatul reviuw on Cyberlaw OR Cyber Law AND image splicing</p>
Research exceptions in copyright laws around the world in Legal reform to enhance global text and data mining research.
Research exceptions in copyright laws around the world
Data of "Inferring the Basal Friction Law from long term observations of Glacier Length, Thickness and Velocity changes on an Alpine Glacier"
<p>This dataset contains the surface velocity and elevation of Argentière Glacier in 2003 and 2018 used in:</p> <p>Gilbert, A., Gimbert, F., Gagliardini, O., & Vincent, C. (2023). Inferring the Basal Friction Law From Long Term Changes of Glacier Length, Thickness and Velocity on an Alpine Glacier. <em>Geophysical Research Letters</em>, <em>50</em>(16), e2023GL104503. <a href="https://doi.org/10.1029/2023GL104503">https://doi.org/10.1029/2023GL104503</a></p> <p>Surface DEM files contain elevation Z on a 20X20 meter grid (geotif files, coordinnate are in Lambert 2E ( EPSG:27572 ) )</p> <p>Horizontal Velocity files contain measured horizontal velocities Vh in m/yr on a 20X20 meter grid (geotif files, coordinnate are in Lambert 2E ( EPSG:27572 ) )</p>
Wildlife as sentinels of compliance with law: an example with GPS-tagged scavengers and sanitary regulations
<p>Monitoring compliance with environmental laws is essential to overcoming possible implementation shortfalls jeopardizing their effectiveness. Besides improving our ecological understanding of wildlife, remote tracking technologies also allow us to take advantage of such ecological knowledge to use wildlife as sentinels of compliance with law.</p> <p>We illustrate this sentinel potential of wildlife using GPS-tracking of large scavengers with complementary functional traits (i.e., 21 griffon vultures and 13 wolves) to assess compliance with EU sanitary regulations allowing livestock carcass disposal in the field. Wildlife sentinels allowed the systematic evaluation of 489 livestock carcasses left in the field, which revealed an important mismatch between on-paper and in-reality implementation of these regulations. While <45 % of the carcasses were placed in authorized areas, compliance with all the criteria required by the regulations on livestock carcass disposal (e.g., from carcass characteristics such as species, age, or production system to its location far away from water, buildings, or roads) ranged from 0 to 4.2%, with no major differences between regions with uneven implementation. Major gaps in compliance pointed towards insufficient and over-bureaucratized designation of Scavenger Feeding Zones (SFZs), where livestock carcass disposal is authorized. The indiscriminate nature of distance criteria from carcasses to watercourses, buildings, and infrastructure further affected compliance.</p> <p>Synthesis and applications: GPS-tagged scavengers allow the on-ground monitoring of carcasses, the addressing of potential risks for wildlife, livestock, and human health, the quantitative assessment of compliance with the law and would improve estimates of carcass availability, substantially contributing to more effective legislation enforcement. Our results show the huge potential of GPS-tagged wildlife as sentinels for monitoring compliance to enhance the environmental rule of law.</p>
Linguistic law-like compression strategies emerge to maximize coding efficiency in marmoset vocal communication
<p>Human language follows statistical regularities or linguistic laws. For instance, Zipf's law of brevity states that the more frequently a word is used, the shorter it tends to be. All human languages adhere to this word structure. However, it is unclear whether Zipf's law emerged de novo in humans or whether it also exists in the non-linguistic vocal systems of our primate ancestors. Using a vocal conditioning paradigm, we examined the capacity of marmoset monkeys to efficiently encode vocalizations. We observed that marmosets adopted vocal compression strategies at three levels: (i) increasing call rate, (ii) decreasing call duration, and (iii) increasing the proportion of short calls. Our results demonstrate that marmosets, when able to freely choose what to vocalize, exhibit vocal statistical regularities consistent with Zipf's law of brevity that go beyond their context-specific natural vocal behavior. This suggests that linguistic laws emerged in non-linguistic vocal systems in the primate lineage.</p>
Cockburn Law, Scottish Borders
Iron Age hillfort at Cockburn Law, Scottish Borders. Model derived from aerial LiDAR data for the Whiteadder Project: http://whiteadder.aocarchaeology.com/ Source: Objaverse 1.0 / Sketchfab
Mindfulness-Based Resilience Training for Aggression, Health, and Stress Among Law Enforcement Officers
ClinicalTrials.gov study NCT03784846. IPD Sharing: NO. Countries: 1. Publications: 3.
The Influence Law of Eccentric Load on the Performance of Yielding Bolt
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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)
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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.