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295 results for “approximation”
PIE LTER dissolved nutrient and particulate concentrations of freshwater inputs to the Plum Island estuarine system, Massachusetts, taken approximately monthly.
Multi-year data of water chemistry including nutrient concentrations for various forms of N, P, C, as well as suspended sediments, was determined from monthly grab samples taken at watershed inputs to the Plum Island Sound Estuary. Sampling sites were the Ipswich River (Sylvania Dam, Ipswich, MA), Parker River Dam (Central St, Newbury, MA), Egypt River (Ipswich, MA) Mill River (Newbury, MA), Muddy Run (Ipswich, MA), Little River (Newbury, MA). These nutrient concentrations are then used in conjunction with USGS discharge data (recorded at gages in the Parker River at Byfield, MA and the Ipswich River at Ipswich, MA) to calculate annual nutrient loading to the Plum Island Sound Estuary, coming over each dam. Annual yield is also calculated for both dams. Refer to file WAT-VA-Load for loading data.
Water samples collected for dissolved inorganic carbon and nutrient analysis during tidal creek lateral exchange measurements approximately every 15 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.
Measurement of the lateral exchange of nutrients, sediment, and carbon in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora and high-elevation marsh dominated by Spartina patens located in Rowley, MA.
Coefficients for Tight Logarithmic Approximations and Bounds for Generic Capacity Integrals
<p>This is a supplementary dataset for the publication:</p> <p>I. M. Tanash and T. Riihonen, "Tight Logarithmic Approximations and Bounds for Generic Capacity Integrals and Their Applications to Statistical Analysis of Wireless Systems," in <em>IEEE Transactions on Communications</em>, 2022, doi: 10.1109/TCOMM.2022.3198435.</p> <p>The dataset contains the sets of optimized coefficients for the novel minimax approximations of the Nakagami and lognormal capacity integrals in terms of absolute error. The proposed approximations have the form of a weighted sum of logarithmic functions. The optimized coefficients are found for a wide range of the corresponding fading parameters, namely m for the Nakagami capacity integral and σ (standard deviation) for the lognormal capacity integral. Please note that the optimized coefficients in the provided dataset for the lognormal capacity integral are calculated for σdB (standard deviation in decibels) so σ=0.1 log_e(10) σdB in Eq. 5.</p> <p>The Matlab function (func_extract_coef.m) extracts the required set of optimal coefficients from the provided dataset according to the selected capacity integral, the parameter's value, and the number of terms. See help func_extract_coef for more information.</p> <p>The Matlab script (general_any_func) implements the theory presented in the corresponding journal paper: More specifically, it implements solving Eq. 22 to calculate the optimized coefficients of Eq. 7 for the Nakagami capacity integral. The code also provides general comments on how to generalize it to obtain the optimized coefficients of any communication system in terms of absolute error. Number of supplementary Matlab functions (general_any_func, func_abs_gen_any_func, calc_d_gen, calc_Cappr_gen, calc_d_gen_derivative, calc_Cappr_gen_derivative, Gauss_Laguerre, and peakseek) are provided herein and are used in the main Matlab script.</p> <p>A Matlab script (Example.m) is also provided as an example to illustrate the use of the provided Matlab function (func_extract_coef.m) in extracting the required coefficients from the dataset, to calculate and plot the corresponding absolute error which is shown by figure Example.jpg.</p>
Data for: Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI
<p>Magnetic Resonance Imaging data used in the work "Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p> </p> <p>The *_ind.{cfl,hdr} files store the indices of the different spokes of the corresponding datasets:</p> <p> </p> <p><strong>data_res_S{1597,0987,0377,0233,0089,0055,0021}</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 3.2|2.04</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{16,15,13,12,10,9,7}^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_raga, data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{13}^1, 2\psi^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi^1</p> <p>Sampling: GA</p> <p><br><br> </p> <p><strong>data_invivo_ga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi^1</p> <p>Sampling: Golden-Ratio</p> <p><br> </p> <p> </p> <p><strong>data_invivo_raga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi_{13}^1</p> <p>Sampling: RAGA</p> <p><br> </p>
A new method for approximating fractional derivatives/ integrals as a series of higher-integer-order derivatives - examples and results of applying the method to initial/boundary value problems
<p>The posted research data includes examples of the application of the author's fractional derivative/integral approximation method using the sum of higher integer derivatives. The attached text files contain the numerical solutions of the presented examples, recorded as a set of numerical values obtained from the performed computations.</p> <ul> <li>Example 4.1 <br> \(\begin{cases}<br> \displaystyle<br> ^{C}D^{\alpha}_{a+}\sin (x), \\<br> x \in \langle a, 3\pi \rangle \quad \hbox{and} \quad<br> \alpha = \{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},<br> \end{cases}\)<br><br></li> <li>Example 4.2 <br>\( \begin{cases}<br> \displaystyle<br> I^{\alpha}_{0+} e^{-x}\cos 7x, \\<br> x \in \langle 0,1\rangle \quad \hbox{and} \quad<br> \alpha =\{1.0,\ 1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0 \},<br> \end{cases} \)<br><br></li> <li>Example 5.1 <br>\(\begin{cases}<br> ^{C} D_{0+}y(x)+2y(x)=x+ \frac{2x^{\alpha+1}}{\Gamma(\alpha+2)},\\<br> x\in\langle0,1\rangle, \\<br> y(0) = 0; \quad y(1) = \frac{1}{\Gamma(\alpha+2)}, \\<br> \alpha = \{1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0\}. <br>\end{cases}\)<br><br></li> <li>Example 5.2 <br>\(\begin{cases}<br> ^{C}D_{0+}^{\alpha}y(x)+1.8 y(x)=0,\\<br> x\in \langle 0,2\rangle \quad \hbox{and} \quad \alpha=\{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},\\<br> y(0)=1.<br>\end{cases}\)</li> </ul>
Dataset used in the publication entitled "Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations"
<p>Dataset obtained from experimental research carried out in the prepared laboratory setup. Based on the dataset, the proposed new decomposition method by approximation with pulse waves has been validated in the publication: Kuwałek P., Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations. The description of the prepared laboratory setup is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery
<p><strong>Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery</strong></p> <p>This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the <em>claims.csv</em> file, the <em>AirportCode</em> implies <em>AirportName</em>, as each code should be unique for a given airport.</p> <p>The file <em>ground_truth.csv</em> is a comma separated file containing approximate functional dependencies. <em>table</em> describes the relation we refer to, <em>lhs</em> and <em>rhs</em> reference two columns of those relations where semantically we found that <em>lhs</em> implies <em>rhs</em>.</p> <p>The file <em>excluded_candidates.csv</em> and <em>included_candidates.csv</em> list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the <em>g3_prime</em> value was too small.</p> <p><strong>Dataset References</strong></p> <ul> <li><em>adult.csv</em>: Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.</li> <li><em>claims.csv</em>: TSA Claims Data 2002 to 2006, <a href="https://www.dhs.gov/tsa-claims-data">published by the U.S. Department of Homeland Security</a>.</li> <li><em>dblp10k.csv</em>: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. <a href="https://hpi.de/naumann/projects/repeatability/datasets/dblp-dataset.html">Made available as DBLP Dataset 2</a>.</li> <li><em>hospital.csv</em>: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>t_biocase_...</em> files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>tax.csv</em>: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> </ul>
Dataset exploring the use of quasi-harmonic approximation to understand the thermal properties of Bi2Se3
<p>This data set contains input and output files for DFT calculations on Bi<sub>2</sub>Se<sub>3</sub> for a number of different fixed unit cells with calculations performed using VASP and Phonopy. At each fixed volume, optimisations and phonon calculations have been performed. These have been used to understand the thermal properties of the material using the quasi-harmonic approximation. </p>
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Chlorophyll determined by extraction of samples taken approximately weekly from seawater intake starting at Palmer Station by station personnel including during winter-over period, 1991-2024.
Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Chlorophyll a is determined weekly year-round at the laboratory seawater intake (SWI), from a depth of 6 meters. Concentrations are typically very low (< 1 µg Chl a per liter) in winter (April-October), and higher (1-30 µg/L) following the initiation of the annual spring-summer phytoplankton bloom in November - January.
Measurements of tidal creek discharge measurements during tidal creek lateral exchange measurements approximately every 15 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.
Measurement of the volume of water during lateral exchange measurements in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora (LM1 and LM2) and high-elevation marsh dominated by Spartina patens (West, Nelson, HM1). Creeks are located in Rowley, MA, PIE LTER.
Water-column conductivity, salinity, dissolved oxygen, turbidity, and pH by deployed sonde during tidal creek lateral exchange measurements approximately every 5 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.
Measurement of water-column conductivity, salinity, temperature, dissolved oxygen, turbidity, and pH logged by deployed sonde in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora (LM1 and LM2) and high-elevation marsh dominated by Spartina patens (West, Nelson, HM1). Creeks are located in Rowley, MA, PIE LTER.
Approximate sum of squares decompositions for Adj₅ + k·Op₅ - λΔ₅ ∈ ISAut(F₅)
<p>This is the dataset accompanying <em>On property (T) for Aut(Fₙ) and SLₙ(</em>ℤ<em>) </em>paper (https://arxiv.org/abs/1812.03456). See the appendix thereof and Section 4 of (<a href="https://arxiv.org/abs/1712.07167">Aut(F₅) has property (T)</a>) for more details.</p> <p><strong>Content</strong></p> <ol> <li><code>1812.03456-cf6dee7.zip</code> contains a julia environment specification (<code>Project.toml</code> and <code>Manifest.toml</code>) as well as <code>1812.03456.jl</code> script used for automatic certification and jupyter noteboks in <code>./notebooks</code> directory.</li> <li><code>SAutF5_r2.tar.xz</code> contains the precomputed solutions for expressing <code>Adj₅+2·Op₅-0.28Δ₅</code> and <code>Adj₅+3·Op₅-1.4Δ₅</code> as sum of (hermitian) squares in the group ring of <code>SAut(F₅)</code>. The contents of this archive must be placed inside `<code>1812.03456`</code>directory from the <code>zip</code> file.</li> </ol> <p><strong>Preparation</strong></p> <p>The code needs to be run with <code>julia-1.4.0</code> or higher (tested versions include also versions <code>julia-1.5</code>). In principle any version in <code>[1.4-2.0)</code> should work due to the promise of forward compatibility.</p> <p>While located in the main directory (<code>1812.03456</code>) you should run the following code in <code>julia</code>s <code>REPL</code> console to instantiate the environment for computations:</p> <pre><code class="language-julia">using Pkg Pkg.activate(".") Pkg.instantiate()</code></pre> <p>(this needs to be done once per installation). Then the directory <code>SAutF5_r2</code> (from the <code>SAutF5_r2.tar.xz</code> archive) needs to be placed in <code>1812.03456</code>.</p> <p><strong>Replication: Jupyter notebook</strong></p> <p>A jupyter server may be launched then within the directory <code>1812.03456</code> by issuing from julia command-line (<code>REPL</code>) the following commands.</p> <pre><code>using Pkg Pkg.activate(".") using IJulia notebook(dir=".")</code></pre> <p>During the first run the user may be asked for installation of <code>Jupyter</code> program (a server for running this notebook) within <code>miniconda</code> environment, which will happen automatically after confirmation. To execute the commands in the notebook, one needs to navigate to <code>notebooks</code> subdirectory of <code>1812.03456</code> and click either of the notebooks.</p> <p>One can replicate the main computational results of the paper by executing all the cells in the <code>Positivity of Adj_n + kOp_n in ISAut(F_n)</code> notebook.</p> <p><strong>Replication: script</strong></p> <p>To verify that <em>(Adj₅ + 3.0·Op₅) - 1.4·Δ₅</em> admits an approximate sum of squares decomposition run in <code>1812.03456</code> directory</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 3 -l 1.4</code></p> </blockquote> <p>On a modern laptop computer this should finish in less than 2h.</p> <p>At the end of computations you will see lines such as:</p> <blockquote> <p>┌ Info: λ is certified to be ><br> └ λ_cert.lo = 1.3701131733828074<br> [ Info: i.e Adj_5 + 3.0·Op_5 - (1.3701131733828074)·Δ_5 ∈ Σ²₂ ISAut(F_5)</p> </blockquote> <p>This means that <em>Adj₅ + 3.0·Op₅ - λΔ₅</em> is a sum of Hermitian squares of elements from <em>ISAut(F₅)</em> for every <code>λ < 1.370....</code></p> <p>A similar verification for <em>Adj₅ + 2.0·Op₅ - 0.28·Δ₅</em> can be run by executing</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 2 -l 0.28</code></p> </blockquote> <p><strong>Generating the provided files</strong></p> <p>If you wish to produce the whole certificate on your own (including the generation of group ring and its multiplication table), delete all <code>*.jld</code> files from the <code>SAutF5_r2</code> folder and run one of the above commands with the same (or different) parameters again. Note: To do this you need at least 16GB of RAM and spare 24h of your CPU.</p> <p>This research was supported in part by National Science Center, Poland, grant 2017/26/D/ST1/00103.</p>
Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage
<p>Supplementary information and raw data for Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage. </p>
The data for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant"
<p>Data used for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant" by Siyang Li, Adam G. Riess, Stefano Casertano, Gagandeep S. Anand, Daniel M. Scolnic, Wenlong Yuan, Louise Breuval, and Caroline D. Huang. Magnitudes provided are after correcting for foreground extinction and crowding bias.</p>
Dataset: Analytical Physical Model for Electrolyte Gated Organic Field Effect Transistors in the Helmholtz Approximation
<p>Data corresponding to the figures of the manuscript "Analytical Physical Model for Electrolyte Gated Organic Field Effect Transistors in the Helmholtz Approximation" by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
Phenology metric layers and their classification layers for the NDVI approximated phenological cycle of Donana from 01/12/2015 to 31/11/2016.
<p>Analysis of changes in the phenological cycle of different plant species provide important information that may be used to assess the impact of seasonal and inter-annual climate variations on terrestrial vegetation. Phenex software has been used for estimating phenology related layers for Donana marshes relying on NDVI time series covering one year period from 01/12/2015 to 31/11/2016.</p> <p>“Phenology_metrics_layer_Dec2015_Nov2016.tif” includes the following layers: (i) green up day, (ii) senescence day, (iii) day of max NDVI value, and (iv) total number of NDVI peaks. These layers are also provided separately with the names: “Greenup_day_Dec2015_Nov2016.tif”, “Senescence_day_Dec2015_Nov2016.tif”, “Max_day_Dec2015_Nov2016.tif”, “Number_of_peaks_Dec2015_Nov2016.tif”.</p> <p>Classification layers based on these layers have been also generated. In particular, "ISODATA_classification_all_input_layers_Dec2015_Nov2016.tif" layer contains the classes generated when providing all phenology related layers as input to the ISODATA algorithm, while "ISODATA_classification_three_input_layers_Dec2015_Nov2016.tif" layer contains contains the classes generated when providing three penology related layers (i.e. greenup day, day of max NDVI value, senescence day layers) as input to the ISODATA algorithm.</p> <p>The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p> <p> </p>
Maps related to the detection of abrupt changes in NDVI approximated phenological cycles of Donana marshes for 2007-2016
<p>Monitoring of abrupt changes among annual vegetation cycles of consequent years in Protected Areas is valuable for the recognition of patterns, which represent the reaction of the biomes to external factors, such as changes in the meteorological conditions (e.g. the precipitation regime), human intervention or extreme events (e.g. fire). It is an indicator of the primary production of the area and other relevant functions of the ecosystem. The BFAST, Breaks For Additive Seasonal and Trend, approach can be used for monitoring changes, since it is globally applicable and able to analyze each pixel individually without the need to set thresholds for detecting changes within time series. Thus, BFAST is applied for the detection of abrupt trend changes in NDVI time series in the case of Doñana marshes, as a proxy to phenological metrics per pixel.</p> <p>BFAST outputs are used to generate: (i) a raster with the time of all detected abrupt changes per pixel (filename: “All_break_times_2007_to_2016.tif”), (ii) a raster with the total number of detected abrupt changes per pixel (filename: “Marshes_maximum_number_of_breaks_2007_to_2016.tif”), (iv) a raster with the time for which the biggest change is detected per pixel has the (filename: “Marshes_maximum_break_time_2007_to_2016.tif”).</p> <p>The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.docx” included in the zip containing the dataset.</p>
Supplemental_Data_S1 for "Kmer Manifold Approximation and Projection for visualizing DNA sequences"
<p>This dataset includes the results generated by KMAP software applied to the htselexdata dataset. Each folder within the dataset contains outputs from multiple dimensionality reduction techniques, including KMAP, UMAP, t-SNE, and MDS. Additionally, motifs and logos have been derived using both KMAP and MEME methods. This data provides insights into motif patterns and structures, which can be beneficial for further bioinformatics and computational biology analyses.</p>
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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
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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
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