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supplementary material for "Analyzing the joint effect of volcanic carbon and sulfur emissions on the marine carbon cycle", for Geochemistry, Geophysics, Geosystems
<p>We provide our LOSCAR output and eruption history data files, along with IPython Jupyter notebook code to plot the various main text and supplementary figures.</p> <p>Please see README.txt for more information.</p> <p> </p>
Additional Material for: Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners
<p>Additional Material for: Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners submitted to ICSA 2025.<br><br>The files contain:<br>1. README.md - A short file describing the contents of the replication package.<br>2. Coding guide.docx - a guide used for coding, with examples from the experiment's participants.<br>3. Coding_results.xlsx - data with all measurements for all participants.<br>4. Questionnaire.xlsx - questionnaire used to gather demographic participant's data.<br>5. Experiment-plan.docx - the detailed plan of all the experiment steps.<br>6. Debiasing-workshop-slides.pptx - slides used during the debiasing workshop.<br>7. Debiasing-workshop-plan.docx - a detailed plan of the debiasing workshop, explaining how exactly the debiasing presentation should be used.<br>8. Consent_form.docx - a consent form that was signed by the participants from one of the author's countries, where guidelines suggested the use of gathering additional written consent.<br><br></p>
Supplementary Material, Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo
<p>This repository contains all the files used in the experiments described in the paper "Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo", which appeared in "RAISE 2025: Requirements engineering for AI-powered SoftwarE", an ICSE 2025 workshop, Ottawa, May 3, 2025.</p>
supplementary material
<div>Supplementary Table S1 Described the causal effect of insomnia on MetS components in the reverse MR analysis</div> <div> </div> <div>Supplementary Figure S1 Described the scatter plots of the association between genetically predicted in the reverse MR analysis</div> <div> </div> <div>Supplementary Figure S2 Described the funnel plots of the association between insomnia and MetS components genetically in the reverse MR analysis</div> <div> </div> <div>Supplementary Figure S3 Described the leave-one-out analysis of the association between insomnia and MetS components genetically in the reverse MR analysis</div>
Logical coherence in 2D compass codes Supplementary Material
<h1>Logical coherence in 2D compass codes</h1> <p>This repo contains the tools to reproduce and share the data for the paper "Logical Coherence in 2D Compass Codes" by Balint Pato, Will Judd Staples, and Kenneth R. Brown (2025) as well as data for B. Pato, Q. Miao and K. R. Brown, "Optimal Decoding of 2D Compass Codes Under Coherent Noise," 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2024, pp. 448-449, doi: 10.1109/QCE60285.2024.10349.</p> <h1>Setup</h1> <p>Python 3.x is required. The instructions for a Unix/Linux system:</p> <pre><code>cd coherence-in-compass-codes-paper python3 -m venv ~/.virtualenvs/cicc . ~/.virtualenvs/cicc/bin/activate pip install -r requirements.txt -r msim/requirements.txt -r msim/requirements.dev.txt </code></pre> <p>Before running any Python file, make sure the source folder is in PYTHONPATH, for example:</p> <pre><code>export PYTHONPATH="." </code></pre> <p>A quick end-to-end test that everything works:</p> <pre><code>check/all </code></pre> <h1>Downloading data</h1> <p>Pregenerated data is available in <code>data/codes.db</code> for the Logical Coherence in 2D Compass Codes paper.</p> <p>The number of samples per code family:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>qshor[0.166667]_13x13_0</td> <td>2,178,400</td> </tr> <tr> <td>qshor[0.166667]_17x17_0</td> <td>2,185,800</td> </tr> <tr> <td>qshor[0.166667]_21x21_0</td> <td>2,210,800</td> </tr> <tr> <td>qshor[0.166667]_9x9_0</td> <td>2,176,600</td> </tr> <tr> <td>qshor[0.333333]_13x13_0</td> <td>3,310,800</td> </tr> <tr> <td>qshor[0.333333]_17x17_0</td> <td>3,317,870</td> </tr> <tr> <td>qshor[0.333333]_21x21_0</td> <td>3,344,800</td> </tr> <tr> <td>qshor[0.333333]_9x9_0</td> <td>3,308,000</td> </tr> <tr> <td>qshor[0.5]_13x13_0</td> <td>2,381,400</td> </tr> <tr> <td>qshor[0.5]_17x17_0</td> <td>2,388,000</td> </tr> <tr> <td>qshor[0.5]_21x21_0</td> <td>2,398,600</td> </tr> <tr> <td>qshor[0.5]_9x9_0</td> <td>2,379,400</td> </tr> <tr> <td>qshor[0.666667]_13x13_0</td> <td>2,808,400</td> </tr> <tr> <td>qshor[0.666667]_17x17_0</td> <td>2,827,000</td> </tr> <tr> <td>qshor[0.666667]_21x21_0</td> <td>2,876,600</td> </tr> <tr> <td>qshor[0.666667]_9x9_0</td> <td>2,805,600</td> </tr> <tr> <td>qshor[0.833333]_13x13_0</td> <td>3,869,600</td> </tr> <tr> <td>qshor[0.833333]_17x17_0</td> <td>3,883,200</td> </tr> <tr> <td>qshor[0.833333]_21x21_0</td> <td>3,913,400</td> </tr> <tr> <td>qshor[0.833333]_9x9_0</td> <td>3,861,400</td> </tr> <tr> <td>surface_13x13</td> <td>2,004,800</td> </tr> <tr> <td>surface_17x17</td> <td>2,004,800</td> </tr> <tr> <td>surface_21x21</td> <td>2,004,800</td> </tr> <tr> <td>surface_9x9</td> <td>2,004,800</td> </tr> <tr> <td>zstackedshor[h11]_11x11_0</td> <td>612,250</td> </tr> <tr> <td>zstackedshor[h1]_11x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_5x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_7x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_9x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h2]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h5]_5x5_0</td> <td>700,000</td> </tr> <tr> <td>zstackedshor[h7]_7x7_0</td> <td>700,000</td> </tr> </tbody> </table> <p>For the surface code ML database:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>surface_101x101</td> <td>40,000</td> </tr> <tr> <td>surface_13x13</td> <td>1,599,200</td> </tr> <tr> <td>surface_151x151</td> <td>40,000</td> </tr> <tr> <td>surface_17x17</td> <td>1,599,200</td> </tr> <tr> <td>surface_201x201</td> <td>40,000</td> </tr> <tr> <td>surface_21x21</td> <td>1,599,200</td> </tr> <tr> <td>surface_23x23</td> <td>1,600,000</td> </tr> <tr> <td>surface_251x251</td> <td>40,000</td> </tr> <tr> <td>surface_25x25</td> <td>1,600,000</td> </tr> <tr> <td>surface_27x27</td> <td>1,600,000</td> </tr> <tr> <td>surface_29x29</td> <td>1,600,000</td> </tr> <tr> <td>surface_33x33</td> <td>1,600,000</td> </tr> <tr> <td>surface_37x37</td> <td>1,600,000</td> </tr> <tr> <td>surface_45x45</td> <td>2,000,000</td> </tr> <tr> <td>surface_53x53</td> <td>2,000,000</td> </tr> <tr> <td>surface_61x61</td> <td>2,000,000</td> </tr> <tr> <td>surface_69x69</td> <td>2,000,000</td> </tr> <tr> <td>surface_9x9</td> <td>1,599,380</td> </tr> </tbody> </table> <p>Sample queries:</p> <ul> <li>to find the number of samples above:</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>order</span> <span>by</span> theta_phys <span>asc</span>; </code></pre> <ul> <li>to find the number of samples per datapoint for surface codes</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>having</span> code_id <span>like</span> <span>'surface%'</span> <span>order</span> <span>by</span> theta_phys <span>asc</span> ; </code></pre> <ul> <li>to query data underlying the plots for infidelity metrics for the d=9 qshor=5/6 compass codes:</li> </ul> <pre><code><span>select</span> <span>*</span> <span>from</span> qshor_loaf <span>where</span> code_id <span>like</span> <span>'qshor%0.83%9x9%'</span>; </code></pre> <h2>Database schema</h2> <pre><code><span>-- code families are the roots of parametrized code hierarchies, e.g. surface, qshor. The convention is to also use the code_family as table names describing the parametrized members. </span> <span>CREATE</span> <span>TABLE</span> CODE_FAMILIES(CODE_FAMILY, <span>UNIQUE</span>(CODE_FAMILY)); <span>-- surface code members. Technically possible to create non-square surface codes, but in this paper we only explored square ones. </span> <span>CREATE</span> <span>TABLE</span> SURFACE(CODE_ID, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>-- families of qshor (x check density) parametrized random compass codes. </span> <span>CREATE</span> <span>TABLE</span> qshor(CODE_ID, QSHOR, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>--the main samples table, for a given code, and physical rotation angle the logical rotation angle is recorded</span> <span>CREATE</span> <span>TABLE</span> LOGICAL_ANGLE_SAMPLES(CODE_ID,THETA_PHYS TEXT, THETA_LOGICAL TEXT); <span>-- diamond distance metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_loaf(code_id, theta_phys, mean, std, num_records); <span>-- diamond distance metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor(CODE_ID, ZSHOR_HEIGHT, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>-- ML decoder version </span> <span>CREATE</span> <span>TABLE</span> qshor_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> qshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_loaf_ml(code_id, theta_phys, mean, std, num_records); </code></pre> <h1>Plots</h1> <p>Make sure that you have the data under <code>data/codes.db</code> - this needs to be a SQLite database. See the previous section on how to create it from the published data.</p> <p>Plots are generated under the folder <code>figures</code>. There are separate entry points for each figure in the paper:</p> <ul> <li>combined thresholds in the appendix resulting in <code>figures/threshold_plot-combined_full.pdf</code> and <code>figures/threshold_plot-combined_zoom.pdf</code>:</li> </ul> <pre><code>python cicc/plot/combined_threshold.py </code></pre> <ul> <li>the main plot containing the manually extracted thresholds for the random compass codes and the surface code:</li> </ul> <pre><code>python cicc/plot/qshor_family.py </code></pre> <ul> <li>the QCE2024 poster / extended abstract plots</li> </ul> <pre><code>python cicc/plot/qce2024_figures.py </code></pre> <ul> <li>the repetition code and Z-stacked Shor code plots matching the formulae plots</li> </ul> <pre><code>python cicc/plot/zstack_zshor_thresholds.py </code></pre> <h1>A note on angle conventions</h1> <p>All rotations in this project are around the Z-axis. There are two ways one can interpret the rotation angle based on the unitary rotation:</p> <ul> <li>spin angles: Rz(theta_spin) = exp(-i theta_spin/2 Z)</li> <li>direct angles: Rz(theta_direct) = exp(i theta_direct Z)</li> </ul> <p>Thus, for the same rotation unitary, theta_spin = - 2 * theta_direct. The paper by Bravyi, Engelbrecht, Konig and Peard [^bravyi2018] for the rotated surface code uses the direct angles convention, and similarly, msim uses direct angles. However, this paper uses spin angles closer to the physics convention.</p> <p>[^bravyi2018]:Bravyi, Sergey, Matthias Englbrecht, Robert König, and Nolan Peard, ‘Correcting Coherent Errors with Surface Codes’, Npj Quantum Information, 4.1 (2018), 55 <a href="https://doi.org/10.1038/s41534-018-0106-y">https://doi.org/10.1038/s41534-018-0106-y</a></p> <p>The table below summarizes the angle conventions in different parts of the codebase to avoid confusion:</p> <table> <tbody><tr> <th>Place</th> <th>convention</th> </tr> </tbody><tbody> <tr> <td>physical angles for the samplers in this project</td> <td>direct angles / pi</td> </tr> <tr> <td>msim simulator</td> <td>direct angles</td> </tr> <tr> <td>msim coeffs framework</td> <td>spin angles</td> </tr> <tr> <td>database logical angles</td> <td>direct angles</td> </tr> <tr> <td>database physical angles</td> <td>direct angles</td> </tr> <tr> <td>plots</td> <td>spin angles / pi</td> </tr> </tbody> </table> <h1>Generating your own data</h1> <h2>Random Compass Codes</h2> <p>Generating data for random compass codes for a given set of qshor values, distances and physical theta values:</p> <pre><code> python cicc/sample_angles/qshor_angles.py --help usage: qshor_angles.py [-h] [--db DB] --dz DZ --q Q --theta_phys THETA_PHYS [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --q Q Qshor probability metric --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples, which is divided across sqrt(num_runs) randomly generated codes </code></pre> <p>For example:</p> <pre><code>python cicc/sample_angles/qshor_angles.py --dz "[9, 13, 17, 21]" --theta_phys="np.linspace(0.01,0.25, 10)" --qshor="[1/6, 2/6, 3/6, 4/6, 5/6]" </code></pre> <h2>Surface code</h2> <p>Generating data for the rotated surface code:</p> <pre><code>python cicc/sample_angles/rsc_angles.py --help usage: rsc_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--batch_size BATCH_SIZE] [--num_workers NUM_WORKERS] [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --batch_size BATCH_SIZE batch size for writing to the db --num_workers NUM_WORKERS parallelism --num_runs NUM_RUNS number of iterations: the total number of samples </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/rsc_angles.py --dz "[5, 9, 13, 17, 21, 25, 29]" --theta_phys="np.linspace(0.01,0.25, 10)" </code></pre> <h2>Repetition codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: repcodes_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --dz "[5,7,9,11]" --theta_phys "list(reversed(list(np.linspace(0.04,0.18,20))))" --num_runs 10000 </code></pre> <h2>Z-stacked Shor codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: zstacked_shor_angles.py [-h] [--db DB] --dz DZ [--height HEIGHT] [--zshor ZSHOR] --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] Sample angles for ZStackedShor codes. Example usage: Run 10,000 samples for height-2 and height-3 ZStackedShor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height [2,3] --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for Z-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --zshor --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for X-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height 1 --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --height HEIGHT height of the Z-Shor code block --zshor ZSHOR Z-shor code - Z-Shor height equals to dz --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <h2>Regenerating aggregated statistics</h2> <p>In the combined plot (<code>cicc/plot/combined_threshold.py</code>), by default, the already computed statistics are plotted. Hence, when new data is generated, these need to be recalculated. In <code>cicc/plot/combined_threshold.py</code> there is a section that can be modified to trigger the required recalculation (slow):</p> <pre><code> recompute( conn, <span># set to True to recompute qshor statistics </span> recompute_qshor=<span>False</span>, <span># set to True to recompute surface code statistics </span> recompute_rsc=<span>False</span>, <span># set to True to recompute qshor statistics with ML decoding </span> recompute_ml_qshor=<span>False</span>, <span># set to True to recompute surface code statistics with ML decoding</span> recompute_ml_rsc=<span>False</span>, )</code></pre>
Supporting material for Petricca et al. (2024), "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets
<p>This archive contains the supplementary material for the paper "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets</p> <p>Content of the dataset:</p> <ol> <li>Synthetic gravity fields for Ariel and Titania generated in the study</li> <li>SPICE kernels of the trajectory of the Uranus Orbiter and Probe designed at JPL</li> </ol> <p> </p> <p>---------------------------------------------------------------</p> <p>Synthetic gravity fields</p> <p>---------------------------------------------------------------</p> <p>The gravity fields are generated following the procedures described in Section 2.1.2 of the main paper. The hydrosphere thickness is assumed to be 190 km and 220 km for Ariel and Titania, respectively. The ocean density is fixed at 1050 kg/m^3. The syntethic topography is generated with pyshtools (Wieczorek and Meschede, 2018). The label of the file indicates the amplitude of the topography of each interface (ice shell or ocean floor) and the maximum degree of the spherical harmonics expansion. The files are formatted according to the Spherical Harmonics ASCII Data Record (SHADR) standard.</p> <p>The header of each file contains: reference radius (km), GM (km^3 / s^2), uncertatinty in the GM (not used and set to zero), maximum degree <em>l </em>of the<em> </em>expansion, maximum order<em> m </em>of the expansion, normalization (0 for unnormalized, 1 for 4pi normalization), reference latitude, reference longitude</p> <p>The columns contain: degree <em>l</em>, order <em>m</em>, coefficient C_<em>lm</em>, coefficient S_<em>lm</em></p> <p>---------------------------------------------------------------</p> <p>UOP trajectories</p> <p>---------------------------------------------------------------</p> <p>The reference positions and velocities of the UOP were generated by Damon Landau (JPL) as part of an internal study at JPL. These initial positions and velocities were numerically integrated by Flavio Petricca (JPL) using the dynamical models described in the main paper. For this reason, the trajectories only cover +- 8 hours from closest approach with each moon and not the entire tour.</p> <p>The ID of the spacecraft is set to -999. The simple text kernel provided here (id_name_map.txt) can be loaded in the kernel pool to associate the ID code with the SPICE names 'URANUS ORBITER PROBE' and 'UOP' for a more explicit and user-friendly access to the trajectories.</p>
Survey material about the Perceived Impact of Maternity on Software Engineering
<p>The material used in the research performed to investigate the perceived impact of maternity on Software Engineering</p>
JAX Imaging Workshop materials
Open the record for dataset details and reuse information.
Supplementary Materials for LAMeD: LLM-generated Annotations for Memory leak Detection
Open the record for dataset details and reuse information.
Supplementary Material: MyPyPSA-Ger: Introducing CO2 taxes on a multi-regional myopic roadmap of the German electricity system towards achieving the 1.5 °C target by 2050
<p>Supplementary Material to run the open-source model MyPyPSA-Ger</p>
Supplementary material 2 from: Armijos-Ojeda D, Székely D, Székely P, Cogălniceanu D, Cisneros-Heredia DF, Ordóñez-Delgado L, Escudero A, Espinosa CI (2021) Amphibians of the equatorial seasonally dry forests of Ecuador and Peru. ZooKeys 1063: 23-48. https://doi.org/10.3897/zookeys.1063.69580
Appendix 1. Reference list for life-history characteristics of amphibians of the Equatorial Seasonally Dry Forest (Table 1)
Automatic exhaustive calculations of large material space by Korringa-Kohn-Rostoker coherent approximation method --- Applied to equiatomic quaternary high entropy alloys
<p>Calculated data of equiatomic quaternary solid solution phase (high-entropy alloys) on local magnetic moment, total magnetization, magnetic phase transition temperature and residual resistivity.</p> <p>The data was added on October 28.</p>
Supplementary material S20: Instances of jolting pulses with double-peaks, compared to those with single peaks.
<p>A video to demonstrate a double peak jolt and single peak jolt in quick succession. Video excerpts taken from the brood-comb jolting video (S10) here show instances of a double peak jolt with instances of a single peak jolt. The video and audio are slowed four times both for easier viewing and hearing of the jolting vibration, which is here heard as a ‘knocking’ sound. A double peak jolt can first be seen near to the 29.655 min time stamp on the x axis, immediately followed by a single peak jolt near to the 29.665 min time stamp. The difference in body motion can be best seen when viewing these jolting instances side-by-side. For the double peak, the mite appears to move its body right and then left, whereas for the single peak the body only moves to the right. This is immediately followed by two consecutive instances of single jolts at time stamps 30.3 min and 30.315 min time stamps where the body only moves in a single downward motion. A second instance of a double peak can then be seen at the 31.84 min time stamp, where the mite appears to move the body in a downward motion followed by movement to the right. Two audible ‘knocks’ can be clearly heard for the double peaks, in comparison to the one audible ‘knock’ for the single peaks.</p>
Supplementary material S16: All honeycomb jolting pulses shown as individual spectrograms.
<p>Video showcasing all 28 honeycomb jolting pulse spectrograms. All pulses are showcased in the same way as those seen in S12 and S14 and share the same analysis. The magnitude of acceleration is logarithmic (to the base 10), where the highest magnitude is 6.5x10<sup>-4 </sup>m/s<sup>2</sup>, and the lowest magnitude set to be 1/40 of the maximum.</p>
Supplementary material S12: All petri-dish jolting pulses shown as individual spectrograms.
<p>Video showcasing the full collection of 250 petri-dish <em>Varroa</em> jolting pulses. Every pulse in the collection is aligned to the centre of the window and presented in decreasing order of strength. The full breadth of variation can be seen between the jolting pulses when viewing them in this way. The magnitude of acceleration is logarithmic (to the base 10) where the highest magnitude is 2x10<sup>-3 </sup>m/s<sup>2</sup> and the lowest magnitude forced to be 1/40 of the maximum to reduce the contribution of meaningless noise.</p>
Supplementary material S14: All brood-comb jolting pulses shown as individual spectrograms.
<p>Video showcasing all 189 brood-comb jolting pulse spectrograms. The jolting pulse spectra have undergone the same centring and analysis as in S12 and S16. The magnitude of acceleration is logarithmic (to the base 10), where the highest magnitude is 2x10<sup>-3 </sup>m/s<sup>2</sup>, and the lowest magnitude set to be 1/40 of the maximum.</p>
Supplementary material S7: An alternative view of Varroa jolting behaviour on the brood-comb substrate.
<p>A short video to demonstrate a few instances of jolting behaviour where the mite appears to push its body in an up and down motion by flexing its legs, rather than the more commonly seen side to side motion. Each instance is of the same mite individual during the same filming session, after it has been edited to remove periods of time where this behaviour is not seen.</p>
Supplementary material S4: Interaction behaviour.
<p>A short video extract of three <em>Varroa </em>mites interacting with one another. The cause of this interaction is unknown but has been seen on a handful of occasions where mites come into close contact with one another and appear to be become ‘stuck’ on each other’s legs. This causes them to roll around the petri-dish until they become dislodged from one another. The vibratory signal produced by this interaction can be heard in this video. The soundtrack of the video is, again, the signal coming from the accelerometer that can be seen secured to the petri-dish.</p>
Supplementary material S11: Video analysis of Varroa jolting pulses on honeycomb.
<p>Video showing the accelerometer data in spectrogram format and synchronous displacements of the <em>Varroa</em> individual on empty, fully built honeycomb. The processing for all panels in this video has been completed in the same way as for S9 and S10, with the video slowed to four times for ease of viewing. The lowest point of acceleration magnitude is here forced to be 1/10<sup>th</sup> of the maximum. The mite in this video pulses 28 times over a period of approximately 4 minutes, so the frame rate did not need to be slowed down to 5 frames per second as with S8, where jolting pulses are rapid and continuous. Due to the less regular production of pulses, there are many stretches of time where no jolting pulses occur. To view the synchronicity quickly and easily between mite displacement and accelerometer trace, instances of jolting have been cut together, thereby removing those time periods where no activity occurred. Overall, this video accumulates approximately 24 seconds of mite activity. The soundtrack (i.e. the signal from the accelerometer) has also been included, as in S9 and S10, also slowed accordingly. The jolting pulses can be heard in some instances as a ‘clapping’ noise.</p>
Supplementary material S10: Video analysis of Varroa jolting pulses on brood-comb.
<p>Video showing the accelerometer data in spectrogram format and synchronous displacements of the <em>Varroa</em> individual on brood-comb. The processing for all panels in this video has been completed in the same way as for S9 and S11, with the video slowed to four times for ease of viewing. The lowest point of acceleration magnitude is here forced to be 1/60<sup>th</sup> of the maximum As the mite jolting pulses are more spread out over time on this substrate and the vibrational pulse is inherently of a lower frequency, the frame rate did not need to be slowed as much as it did for S9. The soundtrack has also been slowed accordingly and still reveals an audible jolting pulse in some instances, this time as a ‘knocking’ noise. Several jolting instances are included in this video from different points in time to showcase occurrences where the jolt produces a measurable vibrational trace. Although this mite jolted frequently throughout the video, detectable instances of vibration occurred at more irregular intervals. Here too, the synchronicity between mite movement and accelerometer trace can be clearly viewed. Overall, this video amounts to approximately 38 seconds of real-time data.</p> <p> </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.