General information title of the dataset: "data for the article <

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

The dataset underlying the figures in the manuscript is "Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays." Abstract of the paper: Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a Modular Automated Virtualization System (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multi-layer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems. Data description: Each figure folder contains a complete set of files necessary to reproduce figures, including Jupyter Notebooks with the figure source code, Adobe Illustrator, and pre-processed data files (hdf5 and pkl). The complete set of all raw data files used in this study is available at Zenodo. [doi: 10.5281/zenodo.14173838]. Acknowledgments: This research was sponsored in part by the Army Research Office (ARO) under Awards No. W911NF-23-1-0110 and W911NF-23-1-0258. We acknowledge support from the European Union through the IGNITE project with grant agreement No. 101069515 and from the Dutch Research Council (NWO) via the National Growth Fund program Quantum Delta NL (Grant No. NGF.1582.22.001). The views, conclusions, and recommendations contained in this paper are those of the authors and are not necessarily endorsed nor should they be interpreted as representing the official policies, either expressed or implied, of the Army Research Office (ARO) or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by the National Institute of Standards and Technology.

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

Data to recreate Figures 1-5 of paper. Submitted to Physical Review D as a letter. - npy files contain initial states used in Figure 1 and 2 (also exact in Figure 5) - txt files are time evolved occupation numbers for Figure 3 and 4 - json files contain raw counts from IonQ device used in Figure 5

Data corresponding to the figures of Phys. Rev. Lett. 125, 077701 (2020)

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.077701

This data repository contains the original figures, numerical (raw) data, scripts that where used to calculate this data, and plot scripts to reproduce the figures from the publication "General Shiba mapping for on-site four-point correlation functions" at Physical Review Research. The preprint is available on arXiv where also the LaTeX source files can be found. Additional information can be found in the README.

License

The CC-BY license applies to all the data and pdf files. All distributed code is under the MIT license.

Data from "Passive, broadband and low-frequency suppression of laser amplitude noise to the shot-noise limit using hollow-core fibre" Physical Review Applied (10-2019)/(Fig2 b, Fig4 b, Fig4 c) Hollow-core noise suppression and multi-delay interferometer/raw data

This data repository contains the original figures, numerical (raw) data and plot scripts to reproduce the figures from the publication "Two-particle calculations with quantics tensor trains -- solving the parquet equations" at Physical Review Research. The preprint is available on arXiv. Additional information can be found in the README.

License

• The CC-BY license applies to all the data and pdf files. All distributed code is under the MIT license.

Technical details

• The dataset was created among others using the publicly available https://github.com/tensor4all" target="_blank" rel="noopener">tensor4all libraries and https://github.com/ITensor/ITensors.jl" target="_blank" rel="noopener">ITensors.

This data repository contains the original figures, numerical (raw) data, and plot scripts to reproduce the figures from the publication "Protection of Correlation-Induced Phase Instabilities by Exceptional Susceptibilities" at Physical Review Research. LaTeX source files of the preprint available on arXiv can be found at the TU gitlab repository. Additional information can be found in the README.

License

The CC-BY license applies to all the data and pdf files. All distributed code is under the MIT license.

This dataset contains both the research data and analysis code for the figures in the publication "Entanglement of Spin-Pair Qubits with Intrinsic Dephasing Times Exceeding a Minute" in Physical Review X. For each figure containing experimental data, we provide the data points, error bars, and potentially underlying data, as well as the python code used to fit the data and obtain the fit parameters. The dataset is organised per figure where the naming of each figure corresponds to that in the publication in Physical Review X.

Statistical analysis of a data set of number of equations and number of citations of papers published in volumes 94 and 104 of the journal Physical Review Letters. This analysis is referred to by the paper Equation-dense papers receive fewer citations—in physics as well as biology in the New Journal of Physics (vol. 18, article 118003) by Andrew D Higginson and Tim W Fawcett. http://iopscience.iop.org/article/10.1088/1367-2630/18/11/118003

Abstract: Raw data used for the manuscript: 'Bias-Free Access to Orbital Angular Momentum in Two-Dimensional Quantum Materials' published in Physical Review Letters 2024. Data folders are organized as figures appearing in the publication. Please read the corresponding 'README' file attached to each figure subfolder. Other: (Other) We are grateful for funding support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy through the Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter ct.qmat (EXC 2147, Project ID 390858490) as well as through the Collaborative Research Center SFB 1170 ToCoTronics (Project ID 258499086).

Examples of adaptive measurement protocols using optimal Bayesian experiment design. This dataset supports "Simplified algorithms for adaptive experiment design in parameter estimation", arXiv 2202.08344 and submitted to Physical Review Applied. The calculations use python package optbayesexpt, which is available from https://github.com/usnistgov/optbayesexpt. The software applies to measurements of parameters in nonlinear parametric models. In the adaptive protocol, Incoming data influences parameter distributions via Bayesian inference and the parameter distribution influences predictions of the impact of future measurements.

This dataset contains the data for the figures in the paper "EIT spectra of Rydberg atoms dressed with dual tone radio-frequency fields", submitted to Physical Review A. This dataset can be used to recreate the experimental and theory plots from the CSV files. The data show EIT spectra of Rydberg atoms driven with dual-tone RF fields (experimental), and Floquet spectra of numerical models that are used to model these EIT spectra (theory/numerical). The data demonstrate spectra of driven Rydberg atoms in the strong field regime, and the models demonstrate the applicability of two-level Floquet spectra to reproduce the dominant spectral features.

This file contains the data and commands to generate the figures for the accepted Physical Review D article "Bootstrap-determined p values in lattice QCD" by N. H. Christ, R. Eranki, and C. Kelly (2025).

This dataset represents absorption/transmission spectra of resonant probe light power through a Rydberg atom vapor, subject to a simultaneous dressing field and a 'low frequency' field. Data is taken as an oscilloscope average of 5 photodiode voltage traces, with frequency offsets given by a simultaneous reference cell (not included). Some data are given as 2-D arrays, with axes of laser detuning across a waterfall of field strength. Some data represents theory eigen-energies of the system, for comparison. This paper will be submitted to Physical Review Letters.

This dataset provides data for the article submitted in the journal Physical review Applied entitled "Live Cell Imaging and Classification via Microscopic Ghost Imaging".

This dataset includes the measured values of the superfluid transition temperature and the energy gap of the A phase of superfluid helium-3 confined in a 80 nm high slab-shaped cavity with specular quasiparticle scattering boundary conditions. Also corresponding calculations of the suppression of the superfluid transition temperature with almost fully specular boundaries are included. The article detailing the findings of this study, and the used methods and techniques, has been published in Physical Review Letters 134, 136001 (2025) available at https://doi.org/10.1103/PhysRevLett.134.136001.Data from the experiments'80nm_cavity_Tc_suppression_specular.dat' lists the measured superfluid transition temperatures in the confined cavity relative to the bulk-marker transition temperature corresponding to specular boundary condition and various pressures, i.e., various values of effective cavity height. These values are included in Figure 1 in the article.'80nm_cavity_A_phase_gap_specular_[pressure]bar.dat' files give the measured temperature dependence of the energy gap of superfluid A phase of helium-3 corresponding to specular boundary condition and various pressures. Values of the gap are based on the measured NMR frequency shift in the superfluid state as described in the article. Four pressures are included in Figure 3 in the article. All five pressures are shown in Supplementary Figure S3.'80nm_cavity_initial_slopes_specular.dat' gives the measured initial slopes of the superfluid frequency shift versus temperature determined within different ranges below the measured superfluid transition temperature in the cavity for specular boundary conditions and various pressures. These values are needed in conversion between the frequency shift and the energy gap and are plotted in Figure 2(b) and Supplementary Figure S2 in the article.The values of superfluid transition temperature and the initial slopes measured using 192 nm high cavity are shown in couple of figures in the manuscript. Those values come from the earlier work, data of which can be found here: https://doi.org/10.17637/rh.12777620.Calculations:'calc_Tc_suppression_S[specularity].dat' show the calculated suppressed superfluid transition temperatures as a function of effective cavity height for two specularities close to being fully specular. The corresponding lines are shown in Figure 1(c) in the manuscript.In all the calculations we have used the quasiclassical weak-coupling approach, as cited in the manuscript.Literature values of bulk (or fully specular) superfluid transition temperature are given by D. S. Greywall in Phys. Rev. B 33, 7520 (1986) (https://doi.org/10.1103/PhysRevB.33.7520).Values of the weak-coupling bulk energy gap of 3He-A can be found, for example, by using E. V. Thuneberg's calculator from https://users.aalto.fi/~thunebe1/theory/qc/bcsgap.html.The values as a function of temperature are also given in https://doi.org/10.17637/rh.12777620.

Datasets for the publication "Non-Markovian effects of two-level systems in a niobium coaxial resonator with asingle-photon lifetime of 10 milliseconds ", Physical Review Applied (2021). The upload contains the data as well as the evalationb routines to repdroduce the figures in the publication.