This record includes the data used for article "Inertial effects in discrete sampling information engines", published in Europhysics Letter (doi: 10.1209/0295-5075/ad8bf0). The preprint is included is this record as "Inertial information engines.pdf"Long 𝜏 limit (Figs. 4 and 5)The matlab script LongTauAnalyse.m convert raw time acquisitions of the cantilever deflection for various demon protocols from the data files in zip folders LongTauDataPosh.zip and LongTauDataNegh.zip to create files LongTauDataPosh.mat and LongTauDataNegh.mat. In these two files, for each protocol (set of parameters L and h), 12400 independent readings of the cantilever position, of the initial and final well position, and of the work performed by the demon are recorded. They are used to plot the work distribution for each parameter set (Fig. 4), and the mean value of the work (Fig. 5). The matlab script readbindata.m is a dependency of the main script LongTauAnalyse.m.Data filesThe archive Data.zip contains the data used to plot the figures. These are pre analysed data. From raw data, all the switching events are detected and for each events different quantities are computed and stored using Python in a dictionnary of numpy arrays. Each file can be loaded as a dictionnary containing several fields: w : work exchanged during a switch of the potential, computed using Stratonovich conventionw_el : work computed as a difference of potential energyq : heat exchangedtrap_position : value of L during the experimenthysteresis : value of h during the experimentduration :  time to the previous switch eventtau_wait : value of 𝜏 Python filesThe archive python.zip contains all the python code used to plot the figures from the pre analysed data.figures_large_tau : figure 4 and 5figures_variation_tau : figure 6figures_pdf_tau_inter : figure 7figures_pdf_short_tau : figure 8figures_comparaison_maps : figure 9figures_info_large_tau : figure 11plot_style_full is the matplotlib configuration file and traitement_stat.py is a dependency of the other python scripts.

This record includes the data used for article "Inertial effects in discrete sampling information engines", published in Europhysics Letter (doi: 10.1209/0295-5075/ad8bf0). The preprint is included is this record as "Inertial information engines.pdf"Long 𝜏 limit (Figs. 4 and 5)The matlab script LongTauAnalyse.m convert raw time acquisitions of the cantilever deflection for various demon protocols from the data files in zip folders LongTauDataPosh.zip and LongTauDataNegh.zip to create files LongTauDataPosh.mat and LongTauDataNegh.mat. In these two files, for each protocol (set of parameters L and h), 12400 independent readings of the cantilever position, of the initial and final well position, and of the work performed by the demon are recorded. They are used to plot the work distribution for each parameter set (Fig. 4), and the mean value of the work (Fig. 5). The matlab script readbindata.m is a dependency of the main script LongTauAnalyse.m.Data filesThe archive Data.zip contains the data used to plot the figures. These are pre analysed data. From raw data, all the switching events are detected and for each events different quantities are computed and stored using Python in a dictionnary of numpy arrays. Each file can be loaded as a dictionnary containing several fields: w : work exchanged during a switch of the potential, computed using Stratonovich conventionw_el : work computed as a difference of potential energyq : heat exchangedtrap_position : value of L during the experimenthysteresis : value of h during the experimentduration :  time to the previous switch eventtau_wait : value of 𝜏 Python filesThe archive python.zip contains all the python code used to plot the figures from the pre analysed data.figures_large_tau : figure 4 and 5figures_variation_tau : figure 6figures_pdf_tau_inter : figure 7figures_pdf_short_tau : figure 8figures_comparaison_maps : figure 9figures_info_large_tau : figure 11plot_style_full is the matplotlib configuration file and traitement_stat.py is a dependency of the other python scripts.

Data set for the article "Learning efficient erasure protocols for an underdamped memory", by Nicolas Barros, Stephen Whitelam, Sergio Ciliberto, and Ludovic Bellon.  Available on arXiv.Inside the compressed file, you will find several folders.It contains:The pdf file of the article, as well as the movies provided in the supplementary material.The folder "Single Erasure" contains the data used for all the protocols attempted in a single iteration. One can find the raw data of the time traces (position z, threshold z0, and center of the well z1) inside the folders "ErasureTo0" and "Erasureto1", depending on the direction of the erasure. Snapshots of the velocities and position pdf are inside "xPDF" and "vPDF", as well as the resulting work distribution "WorkPDF..." and the final success rates "Success...." (a 0 denotes a failure of the erasure protocol, 1 a success).The folder "Chained Erasure" contains the results concerning the repetition of the erasure protocols, repeated 100 times with random targets. For each protocol, we provide the full time traces (z, z0, z1), as well as the resulting work distribution "W...", the mean temperature after each erasure "T...", and the survival rate "R...".Files starting with z, z1, z0 correspond to the raw data of the experimental trajectories and protocols. Each line represents an independent realization of an experiment, with a sampling frequency of 2 MHz.Please contact the authors for any additional data or questions about this dataset.

Data set for the article "Learning efficient erasure protocols for an underdamped memory", by Nicolas Barros, Stephen Whitelam, Sergio Ciliberto, and Ludovic Bellon.  Available on arXiv.Inside the compressed file, you will find several folders.It contains:The pdf file of the article, as well as the movies provided in the supplementary material.The folder "Single Erasure" contains the data used for all the protocols attempted in a single iteration. One can find the raw data of the time traces (position z, threshold z0, and center of the well z1) inside the folders "ErasureTo0" and "Erasureto1", depending on the direction of the erasure. Snapshots of the velocities and position pdf are inside "xPDF" and "vPDF", as well as the resulting work distribution "WorkPDF..." and the final success rates "Success...." (a 0 denotes a failure of the erasure protocol, 1 a success).The folder "Chained Erasure" contains the results concerning the repetition of the erasure protocols, repeated 100 times with random targets. For each protocol, we provide the full time traces (z, z0, z1), as well as the resulting work distribution "W...", the mean temperature after each erasure "T...", and the survival rate "R...".Files starting with z, z1, z0 correspond to the raw data of the experimental trajectories and protocols. Each line represents an independent realization of an experiment, with a sampling frequency of 2 MHz.Please contact the authors for any additional data or questions about this dataset.

Data set for the article "Learning efficient erasure protocols for an underdamped memory" , by Nicolas Barros ,Stephen Whitelam ,Sergio Ciliberto ,and Ludovic Bellon , soon to be published as a preprint on arXiv, and under review.Inside the compressed file, you will find several foldersIt contains:The folder "Paper" , inside which all documents and figure used for the creation of the paper are present.The Folder "Single Erasure" contains the data used for all the protocol tried on a Single iteration. One can find the raw data of the time traces inside the folders "to0" and "to1", timeshots of the velocities  and position pdf inside "xPDF" and "vPDF", as well as the resulting work distribution and success rate "R".The Folder "Multiple Erasure" contains the results used concerning the repetition of the erasure protocols. For each protocol, the mean temperature after each iterations is denoted T, the survivng rate R, and  the full work distribution is provided. We did not include the full time traces of the experiments, as it would be exceed the available storage.Please contact [email protected] for any additional data or question about this dataset.

Data set for the article "Learning efficient erasure protocols for an underdamped memory" , by Nicolas Barros ,Stephen Whitelam ,Sergio Ciliberto ,and Ludovic Bellon , soon to be published as a preprint on arXiv, and under review.Inside the compressed file, you will find several foldersIt contains:The folder "Paper" , inside which all documents and figure used for the creation of the paper are present.The Folder "Single Erasure" contains the data used for all the protocol tried on a Single iteration. One can find the raw data of the time traces inside the folders "to0" and "to1", timeshots of the velocities  and position pdf inside "xPDF" and "vPDF", as well as the resulting work distribution and success rate "R".The Folder "Multiple Erasure" contains the results used concerning the repetition of the erasure protocols. For each protocol, the mean temperature after each iterations is denoted T, the survivng rate R, and  the full work distribution is provided. We did not include the full time traces of the experiments, as it would be exceed the available storage.Please contact [email protected] for any additional data or question about this dataset.

Data set for the article "Probabilistic work extraction on a classical oscillator beyond the second law" , currently published as a preprint on arXiv, and accepted in Physical Review Letters.The preprint and SM have been uploaded for convenience.Inside the compressed file, you will find several foldersIt contains: /Exemple trajectories/ 10 folders named Protocol 0 , Protocol 1 .... each containing the raw data of the 100 first experiments for each protocol. Each protocol was repeated 3000 time to increase the statistics for the article. We provide a representative subset.The Python analysis code "readexperimentalfile.py" . It reads the data in the aforementioned files, and computes the initial potential, the final potential, and the work associated to each protocols. Optionnaly, it saves trajectories used create Fig3 and Fig 4./Figures/Fig 1 : The pdf file for Fig1. (stochastic2.pdf) as presented in the article. Other formats can be provided on demand.Fig 2: The pdf file for Fig2, Setup.pdfFig 3:  The python code used to create Fig3 , named "plotpotentiel.py". To use it, one need the parameters tuning the initial potential used in the fifth protocol 'fitdepart5.npy', the final potential 'fitfin5.npy' as well as the set of trajectories "zfin5.npy" and "zinit5.npy". All those files were created using the provided python code "readexperimentalfile.py" , and are already provided in the same folder.Fig4: The python code used to create Fig4, named "plottrajex.py". "ztotsubset5.npy" and "lambdasubset5.npy" are provided to directly plot z(t) and lambda(t) during the protocol. "fitdepart5.npy" and 'fitfin5.npy", also created using "readexperimentalfile.py", are needed. Fig5: a) Everything needed to plot the first part of Fig 5. using the python file "workdpdf.py".  All the datas of the other experiments are provided, and one can also choose to plot the work probability density function for other protocols (by changing the 5 t o another number. b) "mainfig.py" creates the bottom part of Fig5.

Data set for the article "Probabilistic work extraction on a classical oscillator beyond the second law" , currently published as a preprint on arXiv, and accepted in Physical Review Letters.The preprint and SM have been uploaded for convenience.Inside the compressed file, you will find several foldersIt contains: /Exemple trajectories/ 10 folders named Protocol 0 , Protocol 1 .... each containing the raw data of the 100 first experiments for each protocol. Each protocol was repeated 3000 time to increase the statistics for the article. We provide a representative subset.The Python analysis code "readexperimentalfile.py" . It reads the data in the aforementioned files, and computes the initial potential, the final potential, and the work associated to each protocols. Optionnaly, it saves trajectories used create Fig3 and Fig 4./Figures/Fig 1 : The pdf file for Fig1. (stochastic2.pdf) as presented in the article. Other formats can be provided on demand.Fig 2: The pdf file for Fig2, Setup.pdfFig 3:  The python code used to create Fig3 , named "plotpotentiel.py". To use it, one need the parameters tuning the initial potential used in the fifth protocol 'fitdepart5.npy', the final potential 'fitfin5.npy' as well as the set of trajectories "zfin5.npy" and "zinit5.npy". All those files were created using the provided python code "readexperimentalfile.py" , and are already provided in the same folder.Fig4: The python code used to create Fig4, named "plottrajex.py". "ztotsubset5.npy" and "lambdasubset5.npy" are provided to directly plot z(t) and lambda(t) during the protocol. "fitdepart5.npy" and 'fitfin5.npy", also created using "readexperimentalfile.py", are needed. Fig5: a) Everything needed to plot the first part of Fig 5. using the python file "workdpdf.py".  All the datas of the other experiments are provided, and one can also choose to plot the work probability density function for other protocols (by changing the 5 t o another number. b) "mainfig.py" creates the bottom part of Fig5.

The main files are the following :Multimode_characterization_of_an_optical_beam_deflection_setup.pdf is the preprint of the article.flexion_analysis.m is a Matlab script which analyses the amplitudes and frequencies of the contrasts corresponding to deflexion modes of the cantilever and stored in flexion_data.mat to compute the OBD spot position and size, and plot Figs. 3a, 3b, and 5b of the article.torsion_analysis.m is a Matlab script which analyses the amplitudes and frequencies of the contrasts corresponding to torsion modes of the cantilever and stored in flexion_data.mat to compute the OBD spot position and size, and plot Figs. 3c, 3d, and 5c of the article.ModeNumberStudy.m is a Matlab script which creates Fig. 4 of the article.All other matlab scripts and mat files are dependecies, their use is commented in the main scripts.

The main files are the following :Multimode_characterization_of_an_optical_beam_deflection_setup.pdf is the preprint of the article.flexion_analysis.m is a Matlab script which analyses the amplitudes and frequencies of the contrasts corresponding to deflexion modes of the cantilever and stored in flexion_data.mat to compute the OBD spot position and size, and plot Figs. 3a, 3b, and 5b of the article.torsion_analysis.m is a Matlab script which analyses the amplitudes and frequencies of the contrasts corresponding to torsion modes of the cantilever and stored in flexion_data.mat to compute the OBD spot position and size, and plot Figs. 3c, 3d, and 5c of the article.ModeNumberStudy.m is a Matlab script which creates Fig. 4 of the article.All other matlab scripts and mat files are dependecies, their use is commented in the main scripts.