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301 results for “law”
Appendix for Review Paper Entitled: "A literature review of "lawful" text and data mining."
<p><span>This appendix complements the review paper entitled ““A literature review of “lawful” text and data mining” with 8 Tables highlighting which scholarly works were used for each section of the literature review, but also how those works were used. </span></p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop.</p> <p>For the purposes of <em>neurosynchro</em>, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.</p>
The speed-curvature power law in tongue movements of repetitive speech [dataset]
<p>Files in this record contain data used to produce results presented in the<br> paper:</p> <p>Title: The speed-curvature power law in tongue movements of repetitive speech<br> Authors: Stephan R. Kuberski and Adamantios I. Gafos<br> DOI: <a href="https://doi.org/10.1371/journal.pone.0213851">https://doi.org/10.1371/journal.pone.0213851</a></p> <p>For further details refer to the included file README.txt.</p>
Fitts' law in tongue movements of repetitive speech [dataset]
<p>Files in this record contain data used to produce results presented in the paper:</p> <p> </p> <p>Title: Fitts' law in tongue movements of repetitive speech</p> <p>Authors: Stephan R. Kuberski and Adamantios I. Gafos</p> <p>DOI: TBA</p> <p> </p> <p>For further details refer to the included file README.txt.</p>
H2020 OPERA Project: Power Take-Off and Control Law testing in MARMOK-A-5 Wave Energy Converter at BiMEP
<p>Funded under European Union's Horizon 2020 Programme, <a href="http://opera-h2020.eu/">OPERA</a> project’s main objective is to reduce the time to market of wave energy, by further advancing in 4 key innovations aiming to reduce up to 50% the Levelized Cost of Energy (LCOE) projections of a floating Oscillating Water Column (OWC) technology.</p> <p>As part of project activities, a series of Power Take-Off (PTO) and Control Law (CL) tests were carried out using IDOM's MARMOK-A-5 wave energy converter, while this was deployed in the Biscay Marine Energy Platform (BiMEP) from October 2018 to June 2019.</p> <p>The datasets herein contain a collection of PTO and CL testing results obtained during this extensive testing campaign, providing quantitative evidence of the performance of both innovations. </p>
Compilation of hydraulic models for the study of the spatial averaging on flow laws
<p><strong>1.Summary</strong></p> <p>Datasets used in the article written by Ernesto Rodríguez, Michael Durand and Renato Prata de Moraes Frasson entitled “Observing rivers with varying spatial scales”.</p> <p><strong>2.File description</strong></p> <p>Data will be contained in one NetCDF file per river. The file contains the following groups and variables:</p> <p><strong>/River_Info/</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>/XS_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>Z: Bed elevation in meters. Dimension: Cross-section, time step.</p> <p>xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.</p> <p>X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.</p> <p>W: River width in meters. Dimension: Cross-section, time step.</p> <p>Q: Discharge (m<sup>3</sup>/s). Dimension: Cross-section, time step.</p> <p>H: Water surface elevation in meters. Dimension: Cross-section, time step.</p> <p>A: Cross-sectional area of flow in m<sup>2</sup>. Dimension: Cross-section, time step.</p> <p>P: Wetted perimeter in meters. Dimension: Cross-section, time step.</p> <p>n: Manning’s roughness. Dimension: Cross-section, time step.</p> <p><strong>/Reach_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>W: Reach averaged river width in meters. Dimension: Reach, time step.</p> <p>Q: Reach averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.</p> <p>S: Reach averaged water surface slope in meters per meter. Reach, time step.</p> <p>A: Reach averaged area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p>P: Reach averaged wetted perimeter in meters. Not available for all rivers. Fill value: NaN. Dimension: Reach, time step.</p> <p><strong>References</strong></p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: <a href="https://doi.org/10.1016/j.gloplacha.2014.01.011">10.1016/j.gloplacha.2014.01.011</a>.</p> <p>Rodríguez, E., Durand, M. T., & Frasson, R. P. d. M. (2020). Observing rivers with varying spatial scales. Water Resources Research. doi: <a href="https://doi.org/10.1029/2019WR026476">10.1029/2019WR026476 </a></p>
Raw Results of the paper "Automated Synthesis of Social Laws in STRIPS"
<p>The following file contains the empirical evaluation of the algorithm that was presented in the paper "Automated Synthesis of Social Laws in STRIPS" (AAAI-20) on several PDDL benchmarks. </p>
Appendix for Review Paper Entitled: "A literature review of "lawful" text and data mining."
<p>This appendix complements the review paper entitled ““A literature review of “lawful” text and data mining” with 8 Tables highlighting which scholarly works were used for each section of the literature review, but also how those works were used.</p>
DATA FOR STUDY OF PRIVACY ATTITUDE OF USERS OF SOCIAL NETWORKING SITES AND THEIR EXPECTATIONS FROM LAW IN INDIA
<p>In the present study, on Indian population a disproportionate, stratified, purposive, convenience mixed sampling technique has been adopted in order to ensure proper representation of all the stakeholders concerned with regard to the issue of data privacy in India among the population of the study. The stratified sampling technique is popularly used for a large size population and where it is desirable to purposively have an adequate representation of the all the sub-groups. It is estimated that there are about 0.2 million Law Enforcement Officers (Directors General of Police to Assistant Sub- Inspector), 2.2 million Legal Professionals, including 21,586 Judges, 1.5 million Academicians, about 5000 Information Assurance and Privacy Experts, and 450 million Internet Users in India, out of which 196 million use SNSs. It may be clarified here that all these stakeholders are not only the users of social networking sites but also Indian citizens who are stakeholders in enactment and implementation of the privacy law as and when it is enacted.</p> <p>Based on the size of the total population, a statistically adequate sample size of 385, having a 95 per cent Confidence Level, 5 per cent Margin of Error (Confidence Interval), 0.5 Standard Deviation, a 1.96 Z-score was calculated.</p> <p> </p> <p> </p> <p> </p>
Dataset Literature Riview Bussines Law Enforcement
<p>Bahwa data ini digunakan untuk membuat penelitian ssesuai dengan literatur riview dengan kata kunci Bussines dan Law Enforcement</p>
Dataset Literature Review on Cyber Law and Behavioral Reactions
<p>Merupakan dataset dari Literature Review tentang cyber Law and Behavioral Reactions</p>
Dataset Literature Riview Online Business Law Enforcement
<p>Data ini digunakan untuk membuat penelitian sesuai dengan literatur review dengan kata kunci online business dan law enforcement</p>
DATA UTS CYBER LAW
<p>Dibuat untuk memenuhi syarat penilaian UTS(ujian Tengah Semester) Mata Kuliah Hukum Cyber di UMSIDA </p>
Dataset Literature Review Online Business Law Enforcement
<p>Bahwa data ini digunakan untuk membuat penelitian denga literatur review dengan kata kunci online business law enforcement</p>
ANALISIS DATA JURNAL CYBER LAW INVESTIGATIONS
<p>Abad ke 21 merupakan era dimana teknologi informasi berkembang dan menjadi bagian dari perkembangan kualitas manusia dalam kehidupan bermasyarakat. Mulai dari segala bagian dari kehidupan sosial tidak terlepas dari penggunaan internet. Sehingga Sistem hukum harus memungkinkan pengamanan data elektronik dan akses yang cepat terhadap data elektronik, yang kerap sangat penting bagi keberhasilan investigasi kejahatan. Dalam artikel ini akan menganalisis jurnal dengan topic cyber law investigasi dengan sumber jurnal dari lens.org. Banyak sekali topic investigations pada dunia cyber pada lens.org.</p>
Dataset Literature Review Online Business and Choice of Law
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci Online Business dan Choice of Law.</p>
Data from paper analysing the Quota Law in Federal Universities of Brazil in SISU 2021
<p><strong>Portuguese: </strong>Material Suplementar de artigo analisando cotistas preenchendo vagas de ampla concorrência em universidades federais: a respeito da concorrência concomitante no SISU 2021-1, relacionada à Lei de Cotas e ações afirmativas no Brasil.</p><p><strong>English: </strong>Supplementary Material for paper about affirmative actions and Quota Law in Brazil in SISU 2021.</p>
Soft governance and soft law literature, gathered from Scopus and Web of Science
<p>This is a list of literature related to soft governance and soft law.</p> <p>The soft governance literature was collected on the 12th of July, 2022. 110 articles from Web of Science and 169 articles from Scopus that had any variance of “soft govern*” in either their title, abstract or keywords were consolidated into one collection of 179 articles. These were randomly labelled with a SoftGov_000 identifier.</p> <p>The soft law literature was collected on the 12th of December, 2022. 1026 articles from Web of Science and 1803 articles from Scopus that had any variance of “soft law” in either their title, abstract or keywords were consolidated into one collection of 1960 articles. These were randomly labelled with a SoftLaw_0000 identifier.</p> <p>For the Web of Science searches in both phases, the specific WoS indices searched are “Science Citation Index Expanded” (SCI-EXPANDED)--1955-present, “Social Sciences Citation Index” (SSCI)--1956-present, “Arts & Humanities Citation Index” (AHCI)--1975-present, “Conference Proceedings Citation Index” – Science (CPCI-S)--1990-present, “Conference Proceedings Citation Index” – Social Science & Humanities (CPCI-SSH)--1990-present and “Emerging Sources Citation Index” (ESCI)--2017-present</p>
Mars Watershed boundary data for "Global Spatial Distribution of Hack's Law Exponent on Mars Consistent with Early Arid Climate "
<p>This is the watershed boundary data for Mars (along with Hack's Law exponent) as described in Luo et al. "Global Spatial Distribution of Hack’s Law Exponent on Mars Consistent with Early Arid Climate" accepted for publication in Geophysical Research Letters on 3/10/2023.</p> <p>The attributes are as follows:</p> <p>Id, gridcode = ID of basin</p> <p>Shape_Length = perimeter of the basin</p> <p>Shape_area = area of the basin</p> <p>geoArea = geodesic area of the basin</p> <p>geoLength = geodesic perimeter of the basin</p> <p>areaR = geoArea / Shape_area</p> <p>LengthR = geoLength / Shape_Length</p> <p>n_exponent = Hack’s Law Exponent (h in L = k A^h)</p> <p>n_coefficient = Hack’s Law exponent (k in L = k A^h)</p> <p>n_r2 = r^2 of the nonlinear fit (optimize.curve_fit function in SciPy)</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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.