Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,001
datasets available to search
ShareScore release 0.9.0
Dataset results
2,001 results for “Orders”
PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures
<h1>PAN-AR</h1> <p>This is <strong>PAN-AR</strong> (Panoramas, Ambient Noise & Ambisonics RIRs), a dataset described in the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Filippo Denti, Davide Fantini, Federico Avanzini and Giorgio Presti. PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures. In <em>Proceedings of the 19th International Audio Mostly Conference</em>, Milan, Italy, September 2024.</p> </blockquote> <p>The dataset includes Spatial Room Impulse Responses (SRIRs) in second-order Ambisonics format, ambient noise recordings, and spherical photos. These data have been captured in four environments with different configurations of the source and listener positions:</p> <ol> <li>Printer room</li> <li>Meeting room</li> <li>Classroom</li> <li>Underground parking area</li> </ol> <p>Panoramas and planimetries are provided in a temporary version. The final version with post-processed panoramas and complete planimetries will be available soon. An example of the final panoramas is provided for position A of the printer room, while an example of complete planimetry is provided for the printer and the meeting rooms.</p> <h2>SOFA</h2> <p>The SRIRs are also provided in SOFA format <a href="https://sofacoustics.org/data/database/pan-ar/" target="_blank" rel="noopener">here</a>.</p> <h2>How to cite</h2> <p>If you use the PAN-AR dataset, please cite the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <pre><code>@inproceedings{denti2024panar,</code><br><code> title = {{PAN-AR}: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures},</code><br><code> author = {Denti, Filippo and Fantini, Davide and Avanzini, Federico and Presti, Giorgio},</code><br><code> year = {2024},</code><br><code> month = {September},</code><br><code> booktitle = {Proceedings of the 19th International Audio Mostly Conference (AM '24)},</code><br><code> location = {Milan, Italy},</code><br><code> publisher = {ACM},</code><br><code> isbn = {979-8-4007-0968-5/24/09},</code><br><code> doi = {10.1145/3678299.3678332}</code><br><code>}</code></pre>
Harmful algal bloom and aquatic weeds data from the Sacramento-San Joaquin Delta, collected to evaluate the impact of the 2021 Temporary Urgency Change Order and Emergency Drought Barrier
Condition 8 of the June 2021 Temporary Urgency Change Order for the Central Valley Project (CVP) and State Water Project (SWP) requires a special study of harmful algal blooms (HABs) in the Sacramento–San Joaquin Delta (Delta) and the spread of submersed aquatic vegetation (SAV), and floating aquatic vegetation (FAV), also referred to as “aquatic weeds”. A report on the study was submitted to the State Water Resources Control Board on June 1, 2022. This data package contains all publicly available data used in the report, including visual cyanobacteria reports, cyanotoxin data, water quality, nutrients, flow/hydrodynamics, chlorophyll-a concentrations, temperature, coverage of SAV and FAV, use of herbicides, and human populations. Many of these data were derived from other datasets, though some were collected specifically for this study
Supplementary data for Nested sampling cross-checks using order statistics
<p>This is the raw data for tables 1 and 2 in <a href="https://arxiv.org/abs/2006.03371">Nested sampling cross-checks using order statistics</a>. The file names for the MultiNest results in table 1 are:</p> <pre><code>MN_{PROBLEM}_{number of dimensions}d_efr_{MultiNest efr parameter}.txt</code></pre> <p>The file names for the PolyChord results in table 2 are:</p> <pre><code>PC_{PROBLEM}_{number of dimensions}d_nr_{PolyChord number of repeats}.txt</code></pre> <p>where PROBLEM specifies one of four test functions described in Appendix C</p> <ul> <li>gaussian = Gaussian</li> <li>mixture = Gaussian-log-gamma mixture</li> <li>rosenbrock = Rosenbrock function</li> <li>shells = Gaussian shells</li> </ul> <p>Each file contains 100 rows (corresponding to 100 runs) and 8 columns</p> <ol> <li>log evidence</li> <li>error log evidence</li> <li>KS statistic from test on all iterations</li> <li>number of iterations</li> <li>p-value from all iterations</li> <li>Greatest KS statistic from tests on chunks of iterations</li> <li>Iteration of start of chunk at which greatest KS statistic occurred</li> <li>Bonferroni corrected p-value from greatest KS statistic from tests on chunks of iterations</li> </ol>
Superposition for Lambda-Free Higher-Order Logic — Supplementary Material
<p>We provide the following supplementary material for our <a href="http://matryoshka.gforge.inria.fr/pubs/lfhosup_paper.pdf">paper</a>. The longer version of our paper can be found here: <a href="http://matryoshka.gforge.inria.fr/pubs/lfhosup_report.pdf">report</a>.</p> <p><strong>Compiling Zipperposition</strong></p> <p>Compilation instructions for Zipperposition, in particular instructions for compilation for <a href="https://www.starexec.org">StarExec</a>, can also be found in the <a href="https://github.com/c-cube/zipperposition/#starexec">Zipperposition readme</a>.</p> <p>In general, follow the following steps:</p> <ul> <li>Install <a href="https://opam.ocaml.org/doc/Install.html">OPAM</a></li> <li>Zipperposition requires OCaml >= 4.03.0, e.g. use <pre>opam switch 4.05.0+flambda eval `opam config env`</pre> </li> <li>You might have to install gmplib on your system, e.g. <pre>sudo apt-get install libgmp3-dev</pre> </li> <li>Zipperposition requires the following dependencies. <pre>opam install zarith containers sequence oasis msat menhir jbuilder</pre> </li> <li>Clone Zipperposition and compile it (we used commit 7fe2ebeb0500e350fd0f1dd4c4ad402ba9cf99b6): <pre>git clone https://github.com/c-cube/zipperposition.git --branch dev cd zipperposition make build</pre> This will create the binary zipperposition.native.</li> <li>Compile the applicative encoder if needed: <pre>jbuilder build ./src/tools/app_encode.exe</pre> This will create the binary ./_build/default/src/tools/app_encode.exe.</li> </ul> <p><strong>Problems</strong></p> <p>We used the following first-order (TFF) and the higher-order (THF) <a href="http://www.tptp.org/">TPTP (v7.0.0) problems</a> for the evaluation:<br> <a href="/record/3975512/files/list_TFF.txt">TFF problem list </a><br> <a href="/record/3975512/files/list_THF.txt">THF problem list </a></p> <p>The selection criteria for the problems are described in our paper. We used these scripts to create these problems lists and to generate an XML file to link these problems into a seperate space in Starexec: <a href="/record/3975512/files/starexec_xml.tar.gz"> Problem selection & StarExec XML generation scripts </a></p> <p><strong>Run scripts</strong></p> <p>We used the following run scripts on starexec. This archive also contains the Zipperposition binary and the applicative encoder binary, compiled for Starexec: <a href="/record/3975512/files/run_scripts.tar.gz"> StarExec run scripts </a></p> <p>The scripts use the following command-line options for Zipperposition:</p> <ul> <li>First-order mode: <pre>./zipperposition.native -i tptp -o tptp --no-ho --no-avatar --no-induction --no-unif-pattern --simultaneous-sup false --no-max-vars --no-fool</pre> </li> <li>Intensional purifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --ho-purify int --ho-no-ext-pos --ho-no-ext-neg --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> <li>Intensional nonpurifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --sup-at-vars --ho-no-ext-pos --ho-no-ext-neg --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> <li>Extensional purifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --ho-purify ext --ho-ext-axiom --restrict-hidden-sup-at-vars --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> <li>Extensional nonpurifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --sup-at-vars --restrict-hidden-sup-at-vars --ho-ext-axiom --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> </ul> <p>Additionally, we provided the problem's filename as the first argument, the order (<code>--ord rpo6</code> or <code>--ord kbo</code>), the timeout (<code>--timeout 600</code>) and the memory limit (<code>--mem-limit 128000</code>). We used <code>--timeout 600</code> and Starexec's Wallclock timeout was set to 600s, but Starexec's CPU timeout was set to 300s. So effectively we allowed only 300s of calculation time.</p> <p>For the applicative encoder app_encode.exe, we provide the problem's filename as the first argument. The option <code>-o tptp</code> should be used to ensure that the output is also in TPTP format. To add the extensionality axiom to the applicative encoding, we use <code>--app-encode-extensional</code>.</p> <p><strong>Results</strong></p> <p>Download the raw output of the evaluation and the .csv files created by StarExec here: <a href="/record/3975512/files/output.tar.gz"> Evaluation output </a></p> <p>We used our own scripts to statistically analyse the results instead of using StarExec's statistics: <a href="/record/3975512/files/eval.tar.gz"> Evaluation scripts</a>. Set the location of the xml-file and the output files produced by StarExec in config.py and run stats.py to obtain the statistics displayed in the paper.</p> <p><strong>Examples</strong></p> <p>We tested the examples given in our paper in Zipperposition. Here are the problem files we used. Some are in TPTP format (.p) and some are in Zipperposition format (.zf).</p> <ul> <li><a href="/record/3975512/files/example_varcond.zf">Example "Justification for the weaker variable condition in the intensional variant" </a></li> <li><a href="/record/3975512/files/example_posext.zf">Example "Justification PosExt" (only in report) </a></li> <li><a href="/record/3975512/files/example_supatvars_rpo.zf">Example "Justification for purification and SUP inferences at variables" (only in report) </a></li> <li><a href="/record/3975512/files/example_add_equiv_defs.p">Example "Addition of Peano numbers" </a></li> <li><a href="/record/3975512/files/example_add_equiv_defs_app.p">Example "Addition of Peano numbers" (applicative encoding) </a></li> </ul>
Correlated order at the tipping point in the kagome metal CsV3Sb5
<p>Data deposite for the manuscript entitled "Correlated order at the tipping point in the kagome metal CsV3Sb5". The manuscript will soon appear online. </p>
Higher orders for cosmological phase transitions: a global study in a Yukawa model
<p>Data used in the article with preprint title: <a href="https://arxiv.org/abs/2310.02308">Higher orders for cosmological phase transitions: a global study in a Yukawa model by Oliver Gould and Cheng Xie</a></p><p>Contains file: dataPublishedExport.csv, which consists of the variable used in, and evaluations from the global parameter scan. More details of the content are specified in README.txt.</p>
Construction Industry Steel Ordering Lists (CISOL) Dataset
<p>The Construction Industry Steel Ordering Lists (CISOL) dataset comprises table-centric, real-world documents from the construction industry, annotated to facilitate the testing and training of deep learning models for table detection (TD) and table structure recognition (TSR). </p> <p>CISOL Key Features:</p> <ul> <li>Steel ordering lists from 24 construction projects carried out between 2015-2023, contributed by 10 distinct German structural engineering firms.</li> <li>Anonymized images to ensure the unrecognizability of specific project or creator information.</li> <li>A total of 3280 images, with 844 annotated following the CISOL annotation guidelines.</li> </ul> <p>CISOL is structured into two tracks:</p> <ul> <li><strong>Track A: TD-TSR </strong>version for end-to-end table detection and table structure recognition tasks.</li> <li><strong>Track B: TSR-</strong>only version for table structure recognition tasks, featuring images cropped to the actual table areas with accordingly adjusted annotations.</li> </ul> <p>The dataset is developed in accordance with the FAIR Principles, ensuring that it is Findable, Accessible, Interoperable, and Reusable. The CISOL dataset permits expansion following the established annotation guideline.</p> <p>Access to the CISOL Leaderboard will be provided at <a href="https://eval.ai/web/challenges/challenge-page/2257" target="_blank" rel="noopener">EvalAI.</a></p> <p> </p>
MeV TOF SIMS determination of deposition order between optically distinguishable and indistinguishable inks
<p>In the forensic investigation of questioned documents, it is often very important to know the deposition order of ink traces from two different writing tools at their intersection on a paper. In the present work, intersections of inks from several writing tools were studied using optical techniques that are standardly applied for questioned documents examination in a forensic laboratory, and an accelerator-based Ion Beam Analysis (IBA) technique called Secondary Ion Mass Spectrometry using MeV ions (MeV SIMS) that is applied in an accelerator facility. MeV SIMS provides molecular information about the studied inks from writing tools, which is an added value and can be also applied for the determination of deposition order but was so far relatively rarely used in forensic studies. Aim of this paper is to compare performance of optical techniques and MeV SIMS for several combinations of intersecting lines. Cases were divided into those in which optical techniques can distinguish used inks and those which are optically completely indistinguishable. In the latter cases, we show that although mass spectra of used inks (from blue ballpoint pens) had extremely small differences, these in combination with advanced and most importantly objective multivariate algorithms could be very beneficial in resolving the deposition order at the intersection of optically indistinguishable inks. In general, MeV SIMS proved to be more efficient for oil-based inks while difficulties were encountered with water-based ones, similar to optical methods.</p>
3D magnetotelluric modeling using high-order tetrahedral Nédélec elementson massively parallel computing platforms
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Modesto, D., Queralt, P., Marcuello, A., Ledo, J., Amor-Martin, A., de la Puente, J., García-Castillo, L.E. (2021) 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms. Computers & Geosciences, vol.(160): 105030 DOI: 10.1016/j.cageo.2021.105030. ISSN 0098-3004, Elsevier.</p> </blockquote>
Replication data for: 'A First-Order Statistical Exploration of the Mathematical Limits of Micromagnetic Tomography'
<p>This repository contains the random data generated for obtaining results described in "A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography". All tested parameters are systematically divided over different folders and subfolders. This dataset contains only .npy files, generated with python version 3.8.8 and numpy version 1.21.5.</p> <p>Each file can be opened with numpy.load(filename)</p> <p>The resulting figures are constructed with data of at least 15 iterations; each iteration is stored in a separate folder 'test_' followed by the iteration number.</p> <p>The README file inside provides a detailled overview of the files included.</p>
First-order electroweak phase transitions: a nonperturbative update, dataset
<p>This deposit contains data from Monte-Carlo and Langevin simulations of the SU(2) Higgs model, related to the paper "First-order electroweak phase transitions: a nonperturbative update". Everything is contained within the archive file <em>su2higgs_data.tar.gz</em>, a tarball compressed with Gzip.</p> <p>The data covers four equilibrium quantities: the critical mass, the jump in the quadratic Higgs condensate, the jump in the quartic Higgs condensate, and the surface tension. It also includes data for the bubble nucleation rate. Further details on the contents of the dataset are explained in README.md.</p>
First-order electroweak phase transitions: a nonperturbative update, videos
<p>This deposit contains videos created from Monte-Carlo and Langevin simulations of the SU(2) Higgs model, related to the paper "First-order electroweak phase transitions: a nonperturbative update". There are two videos, both in MPEG-4 format. Both files show data at the parameter point <span class="math-tex">\(x=0.0152473\)</span>, <span class="math-tex">\(y=0.0303620\)</span>, <span class="math-tex">\(ag_3^2= 4/7.332605\)</span>, <span class="math-tex">\(L=60a\)</span>, <span class="math-tex">\(\eta=10\)</span>, using the notation of the paper.</p> <p>The file <em>smoothing.mp4</em> shows the process of smoothing, or coarse-graining, the Higgs field, according to the method described in the section "Visualising bubble nucleation" of the paper. The particular configuration chosen is from the separatrix between phases, i.e. it is a critical bubble.</p> <p>The file <em>trajectory.mp4</em> shows a trajectory of the Langevin time evolution of a configuration starting in the symmetric phase and nucleating into the broken phase. The configurations in this trajectory have been smoothed, or coarse-grained, 10 times.</p>
Predicted USDA soil orders at 250 m (probabilities)
<p>Distribution of the USDA orders (12) based on machine learning predictions of great groups (<a href="https://doi.org/10.5281/zenodo.1476844">https://doi.org/10.5281/zenodo.1476844</a>) from global compilation of soil profiles. To learn more about soil orders and great groups please refer to the <a href="https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download/?cid=stelprdb1247203.pdf">Illustrated Guide to Soil Taxonomy - NRCS - USDA</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/tree/master/soil">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>order = variable: USDA order,</li> <li>usda.histosols = determination method: USDA soil taxonomy class Histosols,</li> <li>p = probability,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: soil surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
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>
Der königlich sächsische Hausorden der Rautenkrone. Genese, Verfasstheit und Verleihungspraxis eines Hausordens des 19. Jahrhunderts (The Royal Saxon House Order of the Rue Crown. Origin, constitution and award practice of a house order of the 19th century.)
<p>This data set was produced as part of a <a href="https://www.academia.edu/86314498/Der_königlich_sächsische_Hausorden_der_Rautenkrone_Genese_Verfasstheit_und_Verleihungspraxis_eines_Hausordens_des_19_Jahrhunderts">bachelor's thesis on the Royal Saxon House Order of the Rue Crown</a> (<em>Orden der Rautenkrone</em>) at the University of Greifswald. The thesis examines the award practices of the Grand Masters of the Order and attempts to draw conclusions about social circumstances. </p> <p>For the work, a data set was created that includes all knights of the Order of the Rue Crown in the period from 1807 to 1918. The names of the beloved were expanded to include a standardised name (GND) and their life data, GND/Wikidata identifier and main geographical affiliation as well as rank and profession. </p> <p>The data here are provided as Numbers and Excel files. Furthermore, the individual tables have been exported into CSV format (Note: in Excel, the CSV files may be displayed incorrectly despite UTF-8 encoding - especially with special characters and umlauts)</p> <p>The dates are not yet completely accurate. For example, in the case of the standardised names, since the persons concerned may have received the corresponding status (king, etc.) only later after the award. The data sets are in constant development. If you have additional information about an entry or have discovered an error, please feel free to contact me. </p>
Dataset for "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns"
<p>This archive contains the source code and data sets presented in the publication "Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns".</p> <p>Kabus, D., De Coster, T., de Vries, A. A., Pijnappels, D. A., & Dierckx, H. (2024). Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns. <em>Computers in Biology and Medicine</em>, 107949. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compbiomed.2024.107949" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.compbiomed.2024.107949</span></a></p>
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
Phlorest phylogeny derived from Dunn et al. 2011 'Evolved structure of language shows lineage-specific trends in word-order universals'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Dunn M, Greenhill SJ, Levinson SC & Gray RD. 2011. Evolved structure of language shows lineage-specific trends in word-order universals. Nature, 473(7345), 79-82.</p> </blockquote>
The price of safety: Order picking in warehouses with in-house traffic regulations (Supplementary material)
<p>In what follows, you will find the code and results of the paper:</p> <p>"The price of safety: Order picking in warehouses with in-house traffic regulations" published in IISE Transactions.</p> <p> </p> <p>List of files:</p> <p>- Zip file: "Order Picking Problem with in-house traffic regulations" containing C# Code used to generate solutions for all safety policies</p> <p>- Result.csv containing all generated results</p> <p>- createPlots.py containing code to generate figures and tables from the paper</p> <p> </p> <p>The C# code is object-oriented and contains a Main function in the Program.cs file that converts the Example.OPP file with the InstanceReaders to an OPPInstance and uses the Solve function from either the DynamicProgrammic.cs or RuralPostman.cs file to solve the OPPInstance with all the TrafficRegulations as described in the paper.</p> <p> </p> <p>The Example.OPP defines the Depot location (0: decentral, 1: central), AisleLength, i.e. the number of pick positions within each aisle, and other dimensions of the warehouse. Finally, the items are defined by their picking aisle, shelf, position in the shelf, and region.</p> <p> </p> <p>The dynamic program (DP) described in the paper is implemented in DynamicProgrammic.cs. A HashSet of DPNode represents each layer of the DP. A DPNode basically consists of components, nodeDegrees, and a value. Depending on the TrafficRegulation the nodeDegrees are either NodeDegreeClassic, i.e. Null, Uneven, or Even, or NodeDegreeInAndOutDifference, i.e. the difference of the in- and out-degree. To construct the solution at the end, the inEdge is also saved for each DPNode and the additional member depotIsConnected ensures that the depot is visited. The DPNodes in the next layer of the DP are created by the functions MakeNextLayerVertical and MakeNextLayerHorizontal by determining all possibleTransitions per node in the current layer and combining them into a newNode. Products are stored with their position on the shelves in the item list within a PickingAisle. All vertical possibleTransitions are determined in a preprocessing step depending on the TrafficRegulations and are saved within the respective PickingAisle. All horizontal possibleTransitions are determined during the DP with specific functions depending on the TrafficRegulation in HorizontalTransition.cs. When the layers are created, the best feasible DPNode per layer is saved and the best one, i.e. the one with the lowest value, is returned at the end.</p> <p> </p> <p>The paper describes that certain safety policies cannot be solved with the DP. These OPPInstances are solved as a RuralPostman problem (RPP) by generating a Graph that adopts the rectangular structure of the warehouse. Within the Graph, requiredEdges are determined that correspond to PickingAisles containing items. The resulting RPP can be transformed into a traveling salesman problem (TSP) as described by applying an arc-oriented Dijkstra or, in certain cases, to a generalized TSP (GTSP) where one of the two directed edges must be visited. If necessary, the GTSP is transformed to an asymmetric TSP in GTSPInstance and then solved with TSPSolver using LKH-3.exe (Helsgaun 2017, http://webhotel4.ruc.dk/~keld/research/LKH-3/). To use LKH-3.exe, the TSP instance is saved in a TSPLIB format and a parameter file (.par) for LKH and a solution file (.sol) are created in the bin folder. These files are named according to the name specified in the instance.Solve function, where one can also choose to save or delete these files afterward.</p> <p> </p> <p>For more information on LKH-3 see: Keld Helsgaun: An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems (Technical Report, Roskilde University, 2017)</p> <p> </p> <p>Evaluation.py</p> <p>A Python script that generates figures 8, 9, and 10 and tables 6, 7 and 8 (in csv-format) of the paper by processing data from Results.csv.</p> <p>It requires Results.csv to be in the same directory as the code.</p> <p>It also requires the following Python packages:</p> <p>- matplotlib</p> <p>- pandas</p> <p>- seaborn</p>
Data from: Direct visualization of a static incommensurate antiferromagnetic order in Fe-doped Bi2Sr2CaCu2O8+δ
<p>This database contains all the necessary data of the manuscript "Direct visualization of a static incommensurate antiferromagnetic order in Fe-doped Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub>". Compared to the previous version (https://doi.org/10.5281/zenodo.5558592), the data of Figure S13B and Figure S13D are exchanges.</p> <p><strong>Abstract of the manuscript</strong></p> <p>In cuprate superconductors, due to strong electronic correlations, there are multiple intertwined orders which either coexist or compete with superconductivity. Among them, the antiferromagnetic (AF) order is the most prominent one. In the region where superconductivity sets in, the long-range AF order is destroyed. Yet the residual short-range AF spin fluctuations are present up to a much higher doping, and their role in the emergence of the superconducting phase is still highly debated. Here, by using a spin-polarized scanning tunneling microscope, we directly visualize an emergent incommensurate AF order in the nearby region of Fe impurities embedded in the optimally doped Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub> (Bi2212). Remarkably, the Fe impurities suppress the superconducting coherence peaks with the gapped feature intact, but pin down the ubiquitous short-range incommensurate AF order. Our work shows an intimate relation between antiferromagnetism and superconductivity.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.