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117 results for “Jamming”

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zenodo44/100

Shear-stabilized jammed packings

<p>Authors are listed in alphabetical order.</p> <p>This data set contains approximately 140,000 shear-stabilized jammed packings, as described in [1].</p> <p>These packings contain <span class="math-tex">\(N = 16 \ldots 4096\)</span>&nbsp;particles with harmonic interactions,&nbsp;under a confining pressure &nbsp;<span class="math-tex">\(p=10^{-7}\ldots10^{-2}\)</span>. Ensemble sizes range from 10 (N=4096) to 5000 (N=16).&nbsp;</p> <p><strong>Particle interactions</strong></p> <p>The simulation code minimizes the enthalpy</p> <p><span class="math-tex">\(H = \sum_{} \frac{k}{2} \delta_{ij}^2 + pL^2\)</span></p> <p>where L&sup2; is the simulation box area, p the externally applied pressure, k=1 the spring constant and&nbsp;</p> <p><span class="math-tex">\(\delta_{ij} = \left\{ \begin{array}{ll} R_i + R_j - |\vec{r_{ij}}| &amp; \textrm{if } |\vec{r_{ij}}| &lt; R_i + R_j, \\ 0 &amp; \textrm{otherwise.} \end{array}\right.\)</span></p> <p>&nbsp;</p> <p><strong>Data files</strong></p> <p>The packings are stored in an HDF5 data file, with the following format:</p> <ul> <li>Example name: N1024P3162e-3_tables.h5 <ul> <li>Packings with <span class="math-tex">\(N=1024\)</span>&nbsp;particles</li> <li>Pressure&nbsp;<span class="math-tex">\(p = 3.162\cdot 10^{-3}\)</span></li> </ul> </li> <li>/packing_attr_cache is a data table containing properties of each packing, such as <ul> <li>the lattice&nbsp;vectors L1 and L2, describing the positions of periodic copies,</li> <li>sxx, syy, sxy, the boundary stresses,</li> <li>phi, the packing fraction,</li> <li>N - Ncorrected, the effective number of particles,</li> <li>Z, the contact number, and</li> <li>path, the path in the HDF5 file this packing can be found</li> </ul> </li> <li>Packings are stored in a directory structure, e.g. /N1024/P3.1620e-03/0090 <ul> <li>Each directory has attributes with the same data as in&nbsp;packing_attr_cache&nbsp;</li> <li>Each directory contains a table &#39;particles&#39; which stores x,y and r. <ul> <li>HDF5 does not support float128 values, so the positions are stored as two float64 values x and x_err. Sum them as float128 to get the full-resolution value.</li> </ul> </li> </ul> </li> </ul> <p>&nbsp;</p> <p>[1] Simon Dagois-Bohy, Brian P. Tighe, Johannes Simon, Silke Henkes, and Martin van Hecke.&nbsp;<em>Soft-Sphere Packings at Finite Pressure but Unstable to Shear.&nbsp;</em>Phys. Rev. Lett.&nbsp;<strong>109</strong>, 095703.&nbsp;http://dx.doi.org/10.1103/PhysRevLett.109.095703</p>

opencc-by-4.0Jul 2015View details →
zenodo44/100

Contact changes in shear-stabilized jammed packings

<p>Authors are listed in alphabetical order.</p> <p>This data set contains the result of small simple shear deformations applied to approximately 140,000 shear-stabilized jammed packings (see [...]), focusing on contact changes, as described in [1,2,3,4].</p> <p>These packings contain&nbsp;<span class="math-tex">\(N = 16 \ldots 4096\)</span>&nbsp;particles with harmonic interactions,&nbsp;under a confining pressure &nbsp;<span class="math-tex">\(p=10^{-7}\ldots10^{-2}\)</span>. Ensemble sizes range from 10 (N=4096) to 5000 (N=16).&nbsp;</p> <p>In addition, a data file summarizing properties of the first contact change for each packing is provided.</p> <p><strong>Particle interactions</strong></p> <p>The simulation code minimizes the enthalpy</p> <p><span class="math-tex">\(H = \sum_{} \frac{k}{2} \delta_{ij}^2 + pL^2\)</span></p> <p>where L&sup2; is the simulation box area, p the externally applied pressure, k=1 the spring constant and&nbsp;</p> <p><span class="math-tex">\(\delta_{ij} = \left\{ \begin{array}{ll} R_i + R_j - |\vec{r_{ij}}| &amp; \textrm{if } |\vec{r_{ij}}| &lt; R_i + R_j, \\ 0 &amp; \textrm{otherwise.} \end{array}\right.\)</span></p> <p>&nbsp;</p> <p>During shear, the boundary conditions are changed, and the system is relaxed to the new state. The simulation uses a bisection algorithm to efficiently step towards each subsequent contact change; see [3,4] for details.</p> <p><strong>Data files</strong></p> <p>The packings are stored in HDF5 data files. For each ensemble, we provide two files: one with and one without particle positions:</p> <ul> <li>N1024~P3162e-3_shear_noparticles.h5 includes all simulation data, but omits particle positions (see below for which data is included).</li> <li>For small data sets,&nbsp;N1024~P3162e-3_shear.h5 contains all simulations and all particle positions</li> <li>For large data sets, N1024~P3162e-3_shear_partial.h5 contains&nbsp;<em>a subset</em>&nbsp;of all simulations, but with all particle positions.</li> <li>Full particle positions for all simulations are available upon request to the authors. Please contact Martin van Hecke .</li> </ul> <p>All files follow the same HDF5 layout:</p> <ul> <li>Example name: N1024~P3162e-3_shear.h5 and&nbsp;N1024~P3162e-3_shear_noparticles.h5 <ul> <li>Packings with&nbsp;<span class="math-tex">\(N=1024\)</span>&nbsp;particles</li> <li>Pressure&nbsp;<span class="math-tex">\(p = 3.162\cdot 10^{-3}\)</span></li> </ul> </li> <li>Packings are stored in a directory structure, e.g. /N1024/P3.1620e-03/0090/SR for the packing with id 0090. <ul> <li>​This directory contains a table &#39;data&#39; indicating system parameters for each simulation step: <ul> <li>boundary conditions L1 and L2 (also as&nbsp;L,&nbsp;alpha, delta)</li> <li>pressure P,</li> <li>strain gamma,</li> <li>stresses s_xy (simple shear), s_xx and s_yy,&nbsp;</li> <li>number of contacts Ncontacts, contact number Z and number of rattlers #rattler</li> <li>number of changed contacts for this contact change bisection Nchanges, N+ (created), N- (broken)</li> <li>contact number Z</li> <li>energy U, enthalpy H and their change in the last relaxation step (dU, dH)</li> <li>step runtime t_run (seconds), #CG, #FIRE (number of conjugate gradient and FIRE iterations)</li> <li>path to the packing corresponding to this state (does not always exist for each state for older simulations)</li> </ul> </li> <li>Each state is saved in /N1024/P3.1620e-03/0090/SR/0000 (initial),&nbsp;/N1024/P3.1620e-03/0090/SR/0001, ...etc. <ul> <li>States are not included in the _noparticles.h5 files</li> <li>Some files omit intermediate positions, and only store positions just before and just after a contact change.</li> <li>The format of these directories is the same as in https://dx.doi.org/10.5281/zenodo.59216.</li> </ul> </li> </ul> </li> </ul> <p>Finally, we provide a summary file (shear_summary_cache.h5)&nbsp;which contains one table (&#39;data&#39;) with properties of the first contact change of all packings. We provide the following columns:</p> <ul> <li>The variable postfix determines whether the value was calculated in the initial state (_base), just before the first contact change (_min) or just after the first contact change (_plus).</li> </ul> <p>&nbsp;</p> <ul> <li>General/simulation properties <ul> <li>Number of particles &#39;N&#39;</li> <li>Random seed [&#39;num&#39;, &#39;PackingNumber_base&#39;]</li> <li>External pressure &#39;P0_base&#39;</li> <li>Simulation step [&#39;i_min&#39;, &#39;i_plus&#39;]</li> </ul> </li> <li>Relaxation statistics <ul> <li>Last change in enthalpy during relaxation [&#39;dH_base&#39;, &#39;dH_plus&#39;, &#39;dH_min&#39;]</li> <li>Last change in energy during relaxation [&#39;dU_base&#39;, &#39;dU_plus&#39;, &#39;dU_min&#39;]</li> <li>Maximum gradient [&#39;maxGrad_base&#39;, &#39;gg_min&#39;, &#39;gg_plus&#39;]</li> <li>Initial simulation runtime [&#39;runtime (s)_base&#39;]</li> </ul> </li> <li>State properties <ul> <li>Number of rattlers &#39;N - Ncorrected_base&#39;</li> <li>Number of non-rattler particles [&#39;Neff_min&#39;, &#39;Neff_plus&#39;]</li> <li>Number of contacts [&#39;Ncontacts_plus&#39;, &#39;Ncontacts_min&#39;]</li> <li>Contact number z [&#39;Z_base&#39;, &#39;Z_min&#39;, &#39;Z_plus&#39;]</li> <li>Internal pressure [&#39;P&#39;, &#39;P_base&#39;, &#39;P_min&#39;, &#39;P_plus&#39;]</li> <li>Mean overlap &delta; [&#39;mean_delta_base&#39;]</li> <li>Packing fraction [&#39;phi_base&#39;, &#39;phi_min&#39;, &#39;phi_plus&#39;]</li> <li>Enthalpy [&#39;H_base&#39;, &#39;H_plus&#39;, &#39;H_min&#39;]</li> <li>Energy [&#39;Uhelper_base&#39;, &#39;U_min&#39;, &#39;U_plus&#39;]</li> <li>Simple shear parameter alpha [&#39;alpha_base&#39;, &#39;alpha_min&#39;, &#39;alpha_plus&#39;]</li> <li>Pure shear parameter delta [&#39;delta_base&#39;, &#39;delta_plus&#39;, &#39;delta_min&#39;]</li> <li>Square root of area [&#39;L_base&#39;, &#39;L_min&#39;, &#39;L_plus&#39;]</li> <li>Stresses on boundaries: <ul> <li>xx &nbsp;[&#39;sxx_base&#39;, &#39;s_xx_min&#39;, &#39;s_xx_plus&#39;,]</li> <li>yy &nbsp;[ &#39;syy_base&#39;, &#39;s_yy_plus&#39;, &#39;s_yy_min&#39;,]</li> <li>xy [&#39;sxy_base&#39;, &#39;s_xy_plus&#39;, &#39;s_xy_min&#39;]</li> </ul> </li> <li>Elastic moduli: <ul> <li>&nbsp;[&#39;c1_base&#39;, &#39;c1_min&#39;, &#39;c1_plus&#39;,</li> <li>&#39;c2_base&#39;, &#39;c2_min&#39;, &#39;c2_plus&#39;,</li> <li>&#39;c3_base&#39;, &#39;c3_min&#39;, &#39;c3_plus&#39;,</li> <li>&#39;c4_base&#39;, &#39;c4_plus&#39;, &#39;c4_min&#39;,</li> <li>&#39;c5_base&#39;, &#39;c5_plus&#39;, &#39;c5_min&#39;,</li> <li>&#39;c6_base&#39;, &#39;c6_min&#39;, &#39;c6_plus&#39;,</li> <li>&#39;Dac_base&#39;, &#39;Dac_plus&#39;, &#39;Dac_min&#39;,</li> </ul> </li> <li>AC component of G(&theta;) [&#39;Gac_base&#39;, &#39;Gac_min&#39;, &#39;Gac_plus&#39;]</li> <li>DC component of G(&theta;) [&#39;Gdc_base&#39;, &#39;Gdc_min&#39;, &#39;Gdc_plus&#39;,]</li> <li>AC component of U(&theta;) [&#39;Uac_base&#39;, &#39;Uac_plus&#39;, &#39;Uac_min&#39;]</li> <li>DC component of U(&theta;) [&#39;Udc_base&#39;, &#39;Udc_plus&#39;, &#39;Udc_min&#39;]</li> <li>Simple shear [&#39;Galpha_base&#39;, &#39;Galpha_plus&#39;, &#39;Galpha_min&#39; ]</li> </ul> </li> <li>Contact change properties <ul> <li>Applied strain gamma [&#39;gamma_plus&#39;, &#39;gamma_min&#39;] <ul> <li><em>gamma_min is used as contact change strain</em></li> </ul> </li> <li>Number of created/broken contacts [&#39;N+_plus&#39;, &#39;N+_min&#39;, &#39;N-_plus&#39;, &#39;N-_min&#39;]</li> <li>Number of changed contacts (=N<sup>+</sup> + N<sup>-</sup>) [&#39;Nchanges_plus&#39;, &#39;Nchanges_min&#39;]</li> <li>Making &amp; breaking strain from upar and uperp: <ul> <li>simple linear (SL) solution: [&#39;gmk_SL_base&#39;, &#39;gbk_SL_base&#39;]</li> <li>full quadratic (FQ) solution: [&#39;gmk_FQ_base&#39; &#39;gbk_FQ_base&#39;]</li> </ul> </li> <li>G up to CC from fit &sigma;=G&gamma; &amp; error bar [&#39;Glin&#39;, &#39;Glinerr&#39;]</li> <li>G up to CC from fit &sigma;=G&gamma; + &lambda;&gamma;&sup2; &amp; error bar [&#39;Gquad&#39;, &#39;Gquaderr&#39;]</li> <li>&lambda; up to CC from fit &sigma;=G&gamma; + &lambda;&gamma;&sup2; &amp; error bar [&#39;lambdaquad&#39;, &#39;lambdaquaderr&#39;]</li> </ul> </li> </ul> <p>[1]&nbsp;Simon Dagois-Bohy, Brian P. Tighe, Johannes Simon, Silke Henkes, and Martin van Hecke.&nbsp;<em>Soft-Sphere Packings at Finite Pressure but Unstable to Shear.&nbsp;</em>Phys. Rev. Lett.&nbsp;<strong>109</strong>, 095703.&nbsp;http://dx.doi.org/10.1103/PhysRevLett.109.095703</p> <p>[2]&nbsp;Merlijn S. van Deen, Johannes Simon, Zorana Zeravcic, Simon Dagois-Bohy, Brian P. Tighe, and Martin van Hecke.&nbsp;<em>Contact changes near jamming</em>. Phys. Rev. E&nbsp;<strong>90</strong>&nbsp;020202(R). http://dx.doi.org/10.1103/PhysRevE.90.020202</p> <p>[3]&nbsp;Merlijn S. van Deen, Brian P. Tighe, and Martin van Hecke.&nbsp;<em>Contact Changes of Sheared Systems: Scaling, Correlations, and Mechanisms</em>. arXiv:1606.04799.&nbsp;https://arxiv.org/abs/1606.04799</p> <p>[4] Merlijn S. van Deen.&nbsp;<em>Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements</em>. PhD Thesis, Leiden University, 2016.&nbsp;https://openaccess.leidenuniv.nl/handle/1887/40902</p>

opencc-by-4.0Jul 2016View details →
zenodo40/100

Dataset del análisis de los juegos de Fight COVID Jam 2019

<p>Dataset realizado por medio del modelo &quot;Modelo de an&aacute;lisis de serious games en el entorno de las game jam&quot; (https://zenodo.org/record/6462394)&nbsp;en la muestra de juegos presentados en la Fight COVID Jam 2019 (https://fightcovid.gamebcn.co/).</p>

opencc-by-4.0Apr 2022View details →
zenodo40/100

WiFi 2.4 GHz Jamming attack scenario P2 measurements using ADALM Pluto and Maia SDR

<p>The dataset comprises physical-layer data measurements (I-Q samples) collected using an ADALM Pluto SDR version B. The original firmware from Analog Devices was replaced with the Maia-SDR Firmware (<a href="https://maia-sdr.org/">https://maia-sdr.org/</a>). The data was gathered within a 250 square meter area of the WIRID-LAB (<a href="https://wirid-lab.umng.edu.co/">https://wirid-lab.umng.edu.co/</a> laboratory at the Military University Nueva Granada.</p> <p>The dataset is divided into two groups of measurements labeled 'JAMMER' and 'NORMAL', each containing 165 files. These files represent data collected from 15 different points across 11 WiFi channels.</p> <ul> <li><strong>NORMAL Group:</strong> Measurements were taken under standard WiFi traffic conditions without any interference from a jammer.</li> <li><strong>JAMMER Group:</strong> Measurements were taken while deploying a Legacy Short Training Field Jammer attack from a static point.</li> </ul> <p>Each .zip compressed file contains data for 15 measurement points, with each point captured over one second at a sampling rate of 15 Msps. The data is formatted according to the Signal Metadata Format (SigMF), with each measurement point having one <code>.sigmf-data</code> file and one <code>.sigmf-meta</code> file.</p> <p>File names indicate the WiFi channel (enumerated from 1 to 11), signal type (Jammer or Normal), and the attacker node's position 'P2'.</p> <p>An accompanying image (Deployment of a Jammer Attack Scenario inside WiridLAB.png) illustrates the test scenario."</p>

opencc-by-4.0Jan 2024View details →
zenodo40/100

UWB Trustworthiness (Jamming and Position Dilution of Precision)

<div> <div> <div> <div>Two datasets are published. The first experiment shows a localization scenario subject to a synchronization header attack ("Jammer"). The second scenario demonstrates the effect of dilution of precision in ultra-wideband localization ("Position Dilution of Precision").</div> </div> </div> </div>

opencc-by-4.0Jul 2024View details →
zenodo40/100

Data underlying the research paper "Articulating Social Issues with Open Data: Exploring a Game Jam Approach"

<p>Contains research data underlying the following research paper:</p> <blockquote> <p>Davide Di Staso, L&aelig;rke Christiansen, Fernando Kleiman, and Marijn Janssen. 2024. Articulating Social Issues with Open Data: Exploring a Game Jam Approach. In Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events (ICGJ &rsquo;24), October 11, 2024, Copenhagen, Denmark. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3697789.3697798</p> </blockquote> <p>The authors acknowledge the financial support from the European Union&rsquo;s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).</p>

opencc-by-4.0Oct 2024View details →
zenodo36/100

Jam gadang

this was a 3D model of jam gadang, bukit tinggi west sumatera heritage architecture. i made it with blender Source: Objaverse 1.0 / Sketchfab

opencc-byMay 2021View details →
zenodo36/100

Simple Low Poly Crown (for Global Game Jam 2020)

A simple low-poly crown model that I created for Global Game Jam 2020. This model was made to a very strict time limit, as the game jam lasted only 48 hours. It was fully modelled, UV mapped and textured in around 1 hour. While it is very simple and only originally a base colour map (metallic and roughness maps were added afterwards in Sketchfab), I think it shows the level of 3D art I can produce in a very short time frame! Source: Objaverse 1.0 / Sketchfab

opencc-byJun 2020View details →
zenodo36/100

Granular piston-probing in microgravity: powder compression, from densification to jamming

<p>The datasets represents all data used in the article &quot;Granular piston-probing in microgravity: powder compression, from densification to jamming&quot;, by Olfa D&#39;Angelo, Anabelle Horb, Aidan Cowley, Matthias Sperl, and W. Till Kranz, published in npj Microgravity (2022).</p>

opencc-by-4.0Sep 2022View details →
zenodo36/100

A Dataset of IQ samples in Indoor Jamming Scenarios

<p>This dataset includes physical-layer radio information (IQ samples) acquired from indoor communications affected by different types of jamming techniques. Specifically, it includes data acquired from 7 different Software Defined Radios (SDRs), i.e., the USRP Ettus Research X310, operating in an office environment while the transmitter and receiver communicates without the Line of Sight (nLoS). Each experiment is characterized by a transmitter, a receiver, and a jammer. While the hardware of the transmitter and the receiver are kept the same for all the experiments, the hardware of the jammer is changed adopting 5 different radios of the same model and brand. The dataset includes different jamming types, e.g., no jamming (silent), tone (sinusoidal), and Gaussian noise. Moreover, the dataset includes different transmission distances and jamming power levels. In each experiment, a pre-determined sequence of bits ([0, 255]) has been modulated using the BPSK scheme, and then stored, at the receiver, as a 2-columns matrix of raw I/Q samples.</p>

opencc-by-4.0Oct 2022View details →
zenodo36/100

UJIAN TENGAH SEMESTER (UTS) PRAKTEK AKUNTANSI _PRODI AKUNTANSI_25 OKTOBER 2022_UNIVERSITAS GRESIK_JAM 18.00-20.00

<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Untuk mengevaluasi atau mengukur kemampuan mahasiswa dalam penyerapan materi dari pertemuan pertama sampai dengan pertemuan tujuh</p> <p><strong>Indikator Pencapaian</strong></p> <p>1. Tes Lisan</p> <p>2. Tes Tulis</p>

opencc-by-4.0Oct 2022View details →
zenodo36/100

Lanscape jam gadang

Jam Gadang (Minangkabau for "Big Clock") is a clock tower, major landmark, and tourist attraction in the city of Bukittinggi, West Sumatra, Indonesia. It is in the centre of the city, near the main market, Pasar Ateh. It has large clocks on each face. photogrammetry from mavic 2 pro photo by M. Bahrun https://youtube.com/c/MBahrunIChourmain https://www.instagram.com/bahrun85 buat temen2 sebangsa dan se tanah air yang butuh model aerial photogrammetry buat kerjaan atau asik2 an silahkan email aja, free lah pokok nya *syarat dan ketentuan berlaku 😆 Source: Objaverse 1.0 / Sketchfab

opencc-byAug 2020View details →
zenodo36/100

Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities

<p>Dataset associated to the paper "Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities"</p>

opencc-by-4.0Jun 2024View details →
zenodo36/100

Dataset: Backwater rise due to jams with a lower gap in an experimental flume

<p>This dataset includes flow measurements and wood accumulation characteristics of flume experiments conducted at the Hydro-environmental Resarch Centre, Cardiff University, Cardiff, UK.</p>

opencc-by-4.0Jul 2021View details →
zenodo36/100

PETEMUAN 6_26 OKTOBER 2022_STRUKTUR DAN POLA BELANJA DAERAH_STIESIA SURABAYA_JAM 18.00-20.59

<p><strong>Capaian Pembelajaran Mata kuliah (CPMK)</strong></p> <p>Setelah menempuh mata kulian ini mahasiswa mampu menganalisis (C4) pelaporan keuangan sektor publik dan mengevaluasi (C5) penerapan kebijakan manajemen keuangan dalam proses pengambilan keputusan di berbagai kegiatan sektor publik dan terampil (P5) merancang dan mengimplementasikan berbagai taktik dalam manajemen keuangan sektor publik secara mandiri dan bertanggung jawab (A4).</p> <p>Mahasiswa memahami konsep dan klasifikasi biaya/belanja</p> <p><strong>Pengertian Biaya Daerah</strong></p> <p>Biaya Daerah Adalah Semua transaksi keuangan untuk menutup defisit atau untuk memanfaatkan surplus. Pembiayaan daerah dirinci menurut pemerintahan daerah, organisasi, kelompok, jenis, objek dan rincian objek pembiayaan. Pembiayaan ditetapkan untuk menutup defisit yang disebabkan oleh lebih besarnya belanja daerah dibandingkan dengan pendapatan yang diperoleh.</p> <p>Penyebab utama terjadinya defisit anggaran adalah adanya kebutuhan pembangunan daerah yang semakin meningkat. Pembiayaan daerah terdiri atas penerimaan pembiayaan dan pengeluaran pembiayaan. Untuk itu arah kebijakan pembiayaan daerah pada tahun 2010 &ndash; 2014 ini terdiri atas arah kebijakan penerimaan daerah dan arah kebijakan pengeluaran daerah.</p>

opencc-by-4.0Oct 2022View details →
zenodo36/100

PERTEMUAN 9_ 07 NOVEMBER 2022_PENGENDALIAN BIAYA TENAGA KERJA, AKUNTANSI BIAYA _UNIVERSITAS GRESIK JAM 20.00-21.30

<p><strong>Capaian Pembelajan Mata Kuliah (CPMK)</strong></p> <p>Setelah megikuti perkuliahan ini mahasiswa Mampu menghitung dan mengerjakan Pengendalian: Biaya Tenaga Kerja</p> <p><strong>Indikator Pembelajaran</strong></p> <p>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Karakteristik tenaga kerja</p> <p>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pengendalian biaya tenaga kerja</p> <p>3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Produktivitas tenaga kerja</p> <p>4.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pengukuran produktivitas</p> <p>5.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Tuntutan mutu</p> <p>6.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Akunntansi biaya tenaga kerja</p> <p>7.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Rencana upah insentif</p> <p>8.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Kurva belajar</p> <p>9.&nbsp;&nbsp; Latihan kasus</p> <p><strong>Pengertian Tenaga Kerja</strong></p> <p>Biaya tenaga kerja adalah jumlah upah dan gaji yang dibayarkan kepada para pekerja (labour cost). Biaya tenaga kerja adalah untuk pembayaran yang dinamakan &ldquo;upah&rdquo;. Beda halnya dengan Gaji. Jika gaji merupakan pembayaran kepada tenaga kerja atau karyawan yang didasarkan pada rentang waktu seperti gaji mingguan, bulanan dan lain sebagainya. &nbsp;Sedangkan, upah dibebankan melalui&nbsp;rekening&nbsp;biaya tenaga kerja langsung, dan gaji dibebankan melalui rekening biaya overhead pabrik.</p>

opencc-by-4.0Nov 2022View details →
zenodo36/100

PERTEMUAN 7_02 NOVEMBER 2022_Manajemen Kas Daerah_STIESIA SUBABAYA JAM 18.00-21.00

<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Setelah menempuh mata kulian ini mahasiswa mampu menganalisis (C4) pelaporan keuangan sektor publik dan mengevaluasi (C5) penerapan kebijakan manajemen keuangan dalam proses pengambilan keputusan di berbagai kegiatan sektor publik dan terampil (P5) merancang dan mengimplementasikan berbagai taktik dalam manajemen keuangan sektor publik secara mandiri dan bertanggung jawab (A4)</p> <p>&nbsp;</p> <p>Mahasiswa mampu memahami tujuan dan siklus manajemen kas daerah, serta ruang lingkup dan anggaran kas daerah</p>

opencc-by-4.0Nov 2022View details →
zenodo36/100

PERTEMUAN 6_03 NOVEMBER 2022_Pelunasan PPh dalam Tahun Berjalan_PERPAJAKAN_UNIVERSITAS WIJAYA PUTRA_JAM 8.00-10.00

<p><strong>Capaian Pembelajaran Mata Kulah (CPMK)</strong></p> <p>Mahasiswa mampu menjelaskan Gambaran Umum Pelunasan&nbsp; PPh Dalam Tahun Berjalan</p> <p><strong>Gambaran Umum&nbsp; Pelunasan PPh Dalam Tahun Berjalan:</strong></p> <p>Pelunasaan melalui pemotong an/ pemungutan oleh pihak lain (PPh Pasal 21/26, PPh Pasal 22, PPh Pasal 23/26, PPh Pasal 24, PPh Final Pasal 4 ayat 2 dan lain-lain)</p>

opencc-by-4.0Nov 2022View details →
zenodo36/100

PERTEMUAN 9_07 NOVEMBER 2022_Penjualan Konsinyasi Untuk Pengamanat_AKL1_JAM 18.00-20.00

<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Mahasiswa Mampu mengetahui dan menghitung Akuntansi Penjualan Konsinyasi Untuk Pengamanat (Consignor)</p> <p><strong>Indikator Pembelajaran</strong></p> <p>1. Pendahuluan</p> <p>2. Akuntansi Konsinyasi Pengamanat Dengan Menggunakan Metode Laba Terpisah</p> <p>3. Akuntansi Konsinyasi Untuk Pengamanat Dengan Menggunakan Metode Laba Tak Terpisah</p> <p>4. Latihan kasus</p>

opencc-by-4.0Nov 2022View details →
zenodo36/100

PERTEMUAN 7_10 nOVEMBER 2022_MODEL KESEIMBANGAN :CAPITAL ASSETS PRICING MODEL DAN ARBITRAGE PRICING THEORY_AIMR_JAM 18.00-21.30

<p><strong>Capaian Pembelajaran Mata Kuliah (CPM</strong></p> <p>Mahasiswa mampu Memahami:Model-model Keseimbangan: Capital Assets Pricing Model dan Arbitrage Pricing Theory</p> <p><strong>Indikator Pembelajaran</strong></p> <p>Model-model Keseimbangan: Capital Assets Pricing Model</p> <p>Arbitrage Pricing Theory</p>

opencc-by-4.0Nov 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record