Skip to main content
Powered by ShareScore

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.

5

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

5 results for “quantum field theory”

Learn how ShareScore rates datasets ↗
zenodo48/100

Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

<p>Network parameters of continuous normalizing flows trained for the&nbsp;<span class="math-tex">\(\varphi^4\)</span> theory.</p> <p>Corresponding article:&nbsp;Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [<a href="https://arxiv.org/abs/2207.00283">2207.00283</a>]</p> <p>Abstract:&nbsp;We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the&nbsp;<span class="math-tex">\(\varphi^4\)</span> theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods.</p>

opencc-by-4.0Jan 2023View details →
zenodo40/100

Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory -- data release

<p>This submission contains the Markov-Chain Monte Carlo&nbsp;data &nbsp;required &nbsp;to reproduce&nbsp;central results of the paper&nbsp;&quot;Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory&quot; (<a href="https://arxiv.org/abs/2009.14768">https://arxiv.org/abs/2009.14768</a>).&nbsp;</p> <p>The Python code required to read and analyse the data&nbsp;can be found under https://github.com/andreasjuettner/Finite-Size-Scaling-Analysis (the relevant release is attached to 10.5281/zenodo.4290508).</p> <p>For any questions please get in touch: juettner@soton.ac.uk.</p>

opencc-by-4.0Nov 2020View details →
zenodo36/100

Raw Data to 'Experimental verification of the area law of mutual information in quantum field theory', arXiv:2206.10563

<p><strong>Absorption images representing the raw data for&nbsp;arXiv:2206.10563</strong></p> <p>&#39;scan5722.zip&#39; contains the raw data for figures 2 and 3.</p> <p>&#39;scan5831.zip&#39; and &#39;scan9617.zip&#39; contain the raw data for figure 5, right and left, respectively.</p> <p>All three datasets, 9617, 5722, and 5831, are used to obtain the data points in figure 4, from low to high temperatures.</p> <p>&nbsp;</p> <p><strong>Scans 5722 &amp; 5831</strong></p> <p>The absorption images are numbered consecutively.</p> <p>The first image for scans 5722 and 5831 is taken along the axial (longitudinal) direction with our &#39;longitudinal&#39; imaging system after 10 ms time of flight (TOF) to measure the atom number balance between the two wells. The measurement is performed&nbsp;before ramping up the DW barrier. Two images are taken in each cycle:&nbsp;One&nbsp;shot&nbsp;with atoms (&#39;1-atomcloud.tif&#39;) and&nbsp;a second image to record the intensity of the imaging beam without atoms&nbsp;(&#39;1-withoutatoms.tif&#39;). These two pictures are used to extract the atomic density (see the Matlab script).</p> <p>The second image for scans 5722 and 5831 is&nbsp;taken in the direction of the double-well (DW) separation with our &#39;transverse&#39; imaging system. The measurement is performed&nbsp;before ramping up the DW barrier.&nbsp;The image is taken after&nbsp;11.2 ms TOF.</p> <p>The subsequent images record&nbsp;the interference fringes for the different evolution times (again, always pairs &#39;-atomcloud.tif&#39; and &#39;-withoutatoms.tif&#39;). They are taken with our &#39;vertical&#39; imaging system after 15.6 ms TOF. The imaging direction is perpendicular to the weakly confined direction of the clouds and the DW separation.</p> <p>The recorded evolution times for scan 5722 are -1.9 ms (right before ramping up&nbsp;the DW barrier), 0 ms (right after the DW barrier is ramped up), and then in steps of 2.5 ms until 65 ms. This means that &#39;3-atomcloud.tif&#39; corresponds to -1.9 ms, and &#39;30-atomcloud.tif&#39; corresponds to 65 ms. This completes the &#39;first repeat&#39;. The next two shots, &#39;31-atomcloud.tif&#39; and &#39;32-atomcloud.tif&#39;, belong to the &#39;second repeat&#39; and are again taken with the &#39;longitudinal&#39; &amp; &#39;transverse&#39; imaging systems, respectively. The picture &#39;33-atomcloud.tif&#39; is again taken with the &#39;vertical&#39; imaging and corresponds to -1.9 ms. And so forth.</p> <p>In the same way, the pictures scan 5831 are ordered. The evolution times (in ms) for these two scans are:</p> <p>scan 5722: -1.9 from 0 to 65 (in steps of 2.5)</p> <p>scan 5831: -1.8, from 0 to 65 (in steps of 2.5)</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up.&nbsp;0 is always right after the barrier was ramped up.</p> <p>&nbsp;</p> <p><strong>Scan 9617</strong></p> <p>This scan only contains images with the &#39;vertical&#39; imaging system with 15.6 ms TOF. The evolution times (in ms) are as follows:</p> <p>scan 9617:&nbsp;-2.8, from 0 to 15 (in steps of 1.5),&nbsp; 22, 25, 28</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up.&nbsp;0 is always right after the barrier was ramped up.</p> <p>This means, that &#39;1-atomcloud.tif&#39;, &#39;16-atomcloud.tif&#39;, &nbsp;&#39;31-atomcloud.tif&#39; and so on correspond to -2.8 ms, and &#39;15-atomcloud.tif&#39;, &#39;30-atomcloud.tif&#39;, &#39;45-atomcloud.tif&#39; and so on correspond to 28 ms.</p> <p>&nbsp;</p> <p><strong>Matlab script</strong></p> <p>In addition to the data, a Matlab script (calc_atomic_density.m) illustrates how to extract the two-dimensional&nbsp;atomic density from the absorption images. It contains all relevant parameters of the imaging systems.</p>

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

Mean-field theories are simple for neural quantum states

<p>Data used in the figures of the paper to appear under the title &quot;Mean-field theories are simple for neural quantum states&quot;. All the data is written in csv files.</p>

opencc-by-4.0Aug 2023View details →
zenodo36/100

Derivation of the Local Lorentz Gauge Transformation of a Dirac Spinor Field in Quantum Einstein-Cartan Theory

<p>I examine the groups which underly classical mechanics, non-relativistic quantum mechanics, special relativity, relativistic quantum mechanics, quantum electrodynamics, quantum flavourdynamics, quantum chromodynamics, and general relativity. This examination includes the rotations SO(2) and SO(3), the Pauli algebra, the Lorentz transformations, the Dirac algebra, and the U(1), SU(2), and SU(3) gauge transformations. I argue that general relativity must be generalized to Einstein-Cartan theory, so that Dirac spinors can be described within the framework of gravitation theory.</p>

opencc-by-4.0Oct 2023View details →

ScienceDex guides

Understand access before you commit

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

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

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

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

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

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

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

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

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

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

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