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Dataset results
14 results for “zero modes”
Zero Modes and Classification of Combinatorial Metamaterials
<p>This dataset contains the simulation data of the combinatorial metamaterial as used for the paper 'Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials', as published in Physical Review Letters.</p> <p>In this paper, the data is used to classify each <span class="math-tex">\(k \times k\)</span> unit cell design into one of two classes (C or I) based on the scaling (linear or constant) of the number of zero modes <span class="math-tex">\(M_k(n)\)</span> for metamaterials consisting of an <span class="math-tex">\(n\times n\)</span> tiling of the corresponding unit cell. Additionally, a random walk through the design space starting from class C unit cells was performed to characterize the boundary between class C and I in design space. A more detailed description of the contents of the dataset follows below.</p> <p><strong>Modescaling_raw_data.zip</strong></p> <p>This file contains uniformly sampled unit cell designs for metamaterial M2 and <span class="math-tex">\(M_k(n)\)</span> for <span class="math-tex">\(1\leq n\leq 4\)</span>, which was used to classify the unit cell designs for the data set. There is a small subset of designs for <span class="math-tex">\(k=\{3, 4, 5\}\)</span> that do not neatly fall into the class C and I classification, and instead require additional simulation for <span class="math-tex">\(4 \leq n \leq 6\)</span> before either saturating to a constant number of zero modes (class I) or linearly increasing (class C). This file contains the simulation data of size <span class="math-tex">\(3 \leq k \leq 8\)</span> unit cells. The data is organized as follows.</p> <p>Simulation data for <span class="math-tex">\(3 \leq k \leq 5\)</span> and <span class="math-tex">\(1 \leq n \leq 4\)</span> is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4.npy", and contain a [Nsim, 1+k*k+4] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:</p> <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original <span class="math-tex">\(k \times k\)</span> form. </li> <li>col k*k+1 - k*k+5: number of zero modes <span class="math-tex">\(M_k(n)\)</span> in ascending order of <span class="math-tex">\(n\)</span>, so: <span class="math-tex">\(\{M_k(1), M_k(2), M_k(3), M_k(4)\}\)</span>.</li> </ul> <p><strong>Note:</strong> the unit cell design uses the numbers <span class="math-tex">\(\{0, 1, 2, 3\}\)</span> to refer to each building block orientation. The building block orientations can be characterized through the orientation of the missing diagonal bar (see Fig. 2 in the paper), which can be Left Up (LU), Left Down (LD), Right Up (RU), or Right Down (RD). The numbers correspond to the building block orientation <span class="math-tex">\(\{0, 1, 2, 3\} = \{\mathrm{LU, RU, RD, LD}\}\)</span>.</p> <p>Simulation data for <span class="math-tex">\(3 \leq k \leq 5\)</span> and <span class="math-tex">\(1 \leq n \leq 6\)</span> for unit cells that cannot be classified as class C or I for <span class="math-tex">\(1 \leq n \leq 4\)</span> is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4_classX_extend.npy", and contain a [Nsim, 1+k*k+6] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:</p> <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original <span class="math-tex">\(k \times k\)</span> form. </li> <li>col k*k+1 - k*k+5: number of zero modes <span class="math-tex">\(M_k(n)\)</span> in ascending order of <span class="math-tex">\(n\)</span>, so: <span class="math-tex">\(\{M_k(1), M_k(2), M_k(3), M_k(4), M_k(5), M_k(6)\}\)</span>.</li> </ul> <p>Simulation data for <span class="math-tex">\(6 \leq k \leq 8\)</span> unit cells are stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. Note that the number of modes is now calculated for <span class="math-tex">\(n_x \times n_y\)</span> metamaterials, where we calculate <span class="math-tex">\((n_x, n_y) = \{(1,1), (2, 2), (3, 2), (4,2), (2, 3), (2, 4)\}\)</span> rather than <span class="math-tex">\(n_x=n_y=n\)</span> to save computation time. These files are named "data_new_rrQR_i_n_Mx_My_n4_kxk(_extended).npy", and contain a [Nsim, 1+k*k+8] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows:</p> <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original <span class="math-tex">\(k \times k\)</span> form. </li> <li>col k*k+1 - k*k+9: number of zero modes <span class="math-tex">\(M_k(n_x, n_y)\)</span> in order: <span class="math-tex">\(\{M_k(1, 1), M_k(2, 2), M_k(3, 2), M_k(4, 2), M_k(1, 1), M_k(2, 2), M_k(2, 3), M_k(2, 4)\}\)</span>.</li> </ul> <p>Simulation data of metamaterial M1 for <span class="math-tex">\(k_x \times k_y\)</span> metamaterials are stored in compressed numpy array format (.npz) and can be loaded in Python with the Numpy package using the numpy.load command. These files are named "smiley_cube_x_y_<span class="math-tex">\(k_x\)</span>x<span class="math-tex">\(k_y\)</span>.npz", which contain all possible metamaterial designs, and "smiley_cube_uniform_sample_x_y_<span class="math-tex">\(k_x\)</span>x<span class="math-tex">\(k_y\)</span>.npz", which contain uniformly sampled metamaterial designs. The configurations are accessed with the keyword argument 'configs'. The classification is accessed with the keyword argument 'compatible'. The configurations array is of shape [Nsim, <span class="math-tex">\(k_x\)</span>, <span class="math-tex">\(k_y\)</span>], the classification array is of shape [Nsim]. The building blocks in the configuration are denoted by 0 or 1, which correspond to the red/green and white/dashed building blocks respectively. Classification is 0 or 1, which corresponds to I and C respectively.</p> <p><strong>Modescaling_classification_results.zip</strong></p> <p>This file contains the classification, slope, and offset of the scaling of the number of zero modes <span class="math-tex">\(M_k(n)\)</span> for the unit cells of metamaterial M2 in Modescaling_raw_data.zip. The data is organized as follows.</p> <p>The results for <span class="math-tex">\(3 \leq k \leq 5\)</span> based on the <span class="math-tex">\(1 \leq n \leq 4\)</span> mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:</p> <p>col 0: label number to keep track</p> <p>col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n \leq 4\)</span>)</p> <p>col 2: slope from <span class="math-tex">\(n \geq 2\)</span> onward (undefined for class X)</p> <p>col 3: the offset is defined as <span class="math-tex">\(M_k(2) - 2 \cdot \mathrm{slope}\)</span></p> <p>col 4: <span class="math-tex">\(M_k(1)\)</span></p> <p>The results for <span class="math-tex">\(3 \leq k \leq 5\)</span> based on the extended <span class="math-tex">\(1 \leq n \leq 6\)</span> mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4_classC_extend.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:</p> <p>col 0: label number to keep track</p> <p>col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n \leq 6\)</span>)</p> <p>col 2: slope from <span class="math-tex">\(n \geq 2\)</span> onward (undefined for class X)</p> <p>col 3: the offset is defined as <span class="math-tex">\(M_k(2) - 2 \cdot \mathrm{slope}\)</span></p> <p>col 4: <span class="math-tex">\(M_k(1)\)</span></p> <p>The results for <span class="math-tex">\(6 \leq k \leq 8\)</span> based on the <span class="math-tex">\(1 \leq n \leq 4\)</span> mode scaling data is stored in "results_analysis_new_rrQR_i_Scenx_Sceny_slopex_slopey_offsetx_offsety_M1k_kxk(_extended).txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows:</p> <p>col 0: label number to keep track</p> <p>col 1: the class_x based on <span class="math-tex">\(M_k(n_x, 2)\)</span>, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n_x \leq 4\)</span>)</p> <p>col 2: the class_y based on <span class="math-tex">\(M_k(2, n_y)\)</span>, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for <span class="math-tex">\(1 \leq n_y \leq 4\)</span>)</p> <p>col 3: slope_x from <span class="math-tex">\(n_x \geq 2\)</span> onward (undefined for class X)</p> <p>col 4: slope_y from <span class="math-tex">\(n_y \geq 2\)</span> onward (undefined for class X)</p> <p>col 5: the offset_x is defined as <span class="math-tex">\(M_k(2, 2) - 2 \cdot \mathrm{slope_x}\)</span></p> <p>col 6: the offset_x is defined as <span class="math-tex">\(M_k(2, 2) - 2 \cdot \mathrm{slope_y}\)</span></p> <p>col 7: <span class="math-tex">\(M_k(1, 1)\)</span></p> <p>Additionally, results including classification for M2.ii can be found in the "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scen_slope_M_M1k_kxk.txt and "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scenx_Sceny_slopex_slopey_Mx_My_M1k_kxk.txt" files.</p> <p><strong>Random Walks Data</strong></p> <p>This file contains the random walks for <span class="math-tex">\(3 \leq k \leq 8\)</span> unit cells of metamaterial M2. The random walk starts from a class C unit cell design (classification M2.ii), for each step <span class="math-tex">\(s\)</span> a randomly picked unit cell is changed to a random new orientation for a total of <span class="math-tex">\(s=k^2\)</span> steps. The data is organized as follows.</p> <p>The configurations for each step are stored in the files named "configlist_test_i.npy", where i is a number and corresponds to a different starting unit cell. The stored array has the shape [k*k+1, 2*k+2, 2*k+2]. The first dimension denotes the step <span class="math-tex">\(s\)</span>, where <span class="math-tex">\(s=0\)</span> is the initial configuration. The second and third dimension denote the unit cell configuration in the pixel representation (see paper) padded with a single pixel wide layer using periodic boundary conditions. </p> <p>The class for each configuration are stored in "lmlist_test_i.npy", where i corresponds to the same number as for the configurations in the "configlist_test_i.npy" file. The stored array has the shape [k*k+1], where the index corresponds to the step <span class="math-tex">\(s\)</span> and displays the class for the accompanying unit cell. The stored number corresponds to the class as <span class="math-tex">\(\{0, 1\} = \{\mathrm{I}, \mathrm{C}\}\)</span>. </p>
Exact solution and Majorana zero mode generation on a Kitaev chain composed out of noisy qubits
<p>Attached are the data sets in forms of python pickle files from the following submission https://arxiv.org/abs/2108.07235</p> <p>Abstract:</p> <p>Majorana zero modes were predicted to exist as edge states of a physical system called the Kitaev chain. Such zero modes should host particles that are their own antiparticles and could be used as a basis for a qubit that is to large extent immune to noise - the topological qubit. However, all attempts to prove their existence gave inconclusive results. Here, I experimentally show that Majorana zero modes do in fact exist on a Kitaev chain composed out of 3 noisy qubits on a publicly available quantum computer. The signature of Majorana zero modes is a degeneracy with the ground state which is not lifted by noise of the quantum computer. I also confirm that Majorana zero modes have a number of theoretically predicted features: a well-defined parity with switches at specific points and a non-conserved particle number. Furthermore, I show that Majorana zero modes favour long-range Majorana pairing at low chemical potential and short-range pairing at large values of the chemical potential. The results presented here are a most comprehensive set of validations ever conducted towards confirming the existence of Majorana zero modes in nature. I foresee that the findings presented here would allow any user with an internet connection to perform experiments with Majorana zero modes. Furthermore, the noisy intermediate scale quantum computing community can start building topological processors composed out of contemporary noisy qubits.</p>
Datasets underlying the paper Zero-mode waveguide nanowells for single-molecule detection in living cells
<p>Different datasets underlying the paper Zero-mode waveguide nanowells for single-molecule detection in living cells. The repository contains .zip archives, mostly containing a readme file with additional information.</p> <pre>Cell imaging experiments.zip contains the raw image files acquired on arrays of version 1 or version 2 using a Nikon TI inverted microscope and used in figures 4-6. </pre> <p>Gla_0127_14.zip contains SEM images of the fabrication of arrays of version 1</p> <p>Gla_29_Pd_1.zip contains SEM images of the fabrication of arrays of version 2</p> <p>SM experiments.zip contains the raw single-molecule fluorescence data acquired on an array of version 1 using a PicoQuant Microtime microscope together with the analysis files.</p> <p>FDTD simulations.zip contains the simulation files for the use in the software Lumerical</p>
Parametric exploration of zero-energy modes in three-terminal InSb-Al nanowire devices
<p>Files inlcude 1) Raw data for all figure 2) data process file 3) Generated figures</p>
Replication data and theory code for: Observation of a Majorana zero mode in a topologically protected edge channel
<p>Replication Data for: Observation of a Majorana zero mode in a topologically protected edge channel</p>
Easy-to-configure zero-dimensional valley-chiral modes in a graphene point junction
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Interaction-induced strong zero modes in short quantum dot chains with time-reversal symmetry
<p>The code repository for the manuscript "<span>Interaction-induced strong zero modes in short quantum dot</span><br><span>chains with time-reversal symmetry".</span></p>
Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor
<p>Dataset for the paper "Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor"</p>
Data and code for "Robust poor man's Majorana zero modes using Yu-Shiba-Rusinov states"
<p>This folder contains the raw data and code used to generate the plots for the paper <i>Robust poor man's Majorana zero modes using Yu-Shiba-Rusinov states</i>.</p><p>To run the Jupyter notebook, install Anaconda and execute:</p><blockquote><p><i>conda env create -f env.yml </i></p></blockquote><p>followed by:</p><blockquote><p><i>conda activate robust-pmm</i></p></blockquote><p>Finally,</p><blockquote><p><i>jupyter lab</i></p></blockquote><p>to launch the notebook.</p><p>Raw data are stored in netCDF (.nc) format. The experimental datasets are exported by the data acquisition package QCoDeS and can be read as an xarray Dataset. Both the experimental and the numerical datasets were saved as netCDF files using xarray built-in function.</p>
Signature of a pair of Majorana zero modes in superconducting gold surface states
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MMS 1 Electron Drift Instrument (EDI) Quality Zero Counts, Level 2 (L2), Survey Mode, 0.125 s Data
Electron Drift Instrument (EDI) Q0 Survey, Level 2, 0.125 s Data (8 samples/s). EDI has two scientific data acquisition modes, called electric field mode and ambient mode. In electric field mode, two coded electron beams are emitted such that they return to the detectors after one or more gyrations in the ambient magnetic and electric field. The firing directions and times-of-flight allow the derivation of the drift velocity and electric field. In ambient mode, the electron beams are not used. The detectors with their large geometric factors and their ability to adjust the field of view quickly allow continuous sampling of ambient electrons at a selected pitch angle and fixed but selectable energy. To find the beam directions that will hit the detector, EDI sweeps each beam in the plane perpendicular to B at a fixed angular rate of 0.22 °/ms until a signal has been acquired by the detector. Once signal has been acquired, the beams are swept back and forth to stay on target. Beam detection is not determined from the changes in the count-rates directly, but from the square of the beam counts divided by the background counts from ambient electrons, i.e., from the square of the instantaneous signal-to-noise ratio (SNR). This quantity is computed from data provided by the correlator in the Gun-Detector Electronics that also generates the coding pattern imposed on the outgoing beams. If the squared SNR ratio exceeds a threshold, this is taken as evidence that the beam is returning to the detector. The thresholds for SNR are chosen dependent on background fluxes. They represent a compromise between getting false hits (induced by strong variations in background electron fluxes) and missing true beam hits. The basic software loop that controls EDI operations is executed every 2 ms. As the times when the beams hit their detectors are neither synchronized with the telemetry nor equidistant, EDI data have no fixed time-resolution. Data are reported in telemetry slots. In Survey, using the standard packing mode 0, there are eight telemetry slots per second and Gyn Detector Unit (GDU). The last beam detected during the previous slot will be reported in the current slot. If no beam has been detected, the data quality will be set to zero. In Burst telemetry there are 128 slots per second and GDU. The data in each slot consists of information regarding the beam firing directions (stored in the form of analytic gun deflection voltages), times-of-flight (if successfully measured), quality indicators, time stamps of the beam hits, and some auxiliary correlator-related information. Whenever EDI is not in electron drift mode, it uses its ambient electron mode. The mode has the capability to sample at either 90 degrees pitch angle or at 0/180 degrees (field aligned), or to alternate between 90 degrees and field aligned with selectable dwell times. While all options have been demonstrated during the commissioning phase, only the field aligned mode has been used in the routine operations phase. The choices for energy are 250 eV, 500 eV, and 1 keV. The two detectors, which are facing opposite hemispheres, are looking strictly into opposite directions, so while one detector is looking along B the other is looking antiparallel to B (corresponding to pitch angles of 180 and 0 degrees, respectively). The two detectors switch roles every half spin of the spacecraft as the tip of the magnetic field vector spins outside the field of view of one detector and into the field of view of the other detector. These data are a by-product generated from data collected in electric field mode. Whenever no return beam is found in a particular time slot by the flight software to be reported will be flagged with the lowest quality level (quality zero). The ground processing generates a separate data product from these counts data. The EDI instrument paper can be found at: http://link.springer.com/article/10.1007%2Fs11214-015-0182-7. The EDI instrument data products guide can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 2 Electron Drift Instrument (EDI) Quality Zero Counts, Level 2 (L2), Survey Mode, 0.125 s Data
Electron Drift Instrument (EDI) Q0 Survey, Level 2, 0.125 s Data (8 samples/s). EDI has two scientific data acquisition modes, called electric field mode and ambient mode. In electric field mode, two coded electron beams are emitted such that they return to the detectors after one or more gyrations in the ambient magnetic and electric field. The firing directions and times-of-flight allow the derivation of the drift velocity and electric field. In ambient mode, the electron beams are not used. The detectors with their large geometric factors and their ability to adjust the field of view quickly allow continuous sampling of ambient electrons at a selected pitch angle and fixed but selectable energy. To find the beam directions that will hit the detector, EDI sweeps each beam in the plane perpendicular to B at a fixed angular rate of 0.22 °/ms until a signal has been acquired by the detector. Once signal has been acquired, the beams are swept back and forth to stay on target. Beam detection is not determined from the changes in the count-rates directly, but from the square of the beam counts divided by the background counts from ambient electrons, i.e., from the square of the instantaneous signal-to-noise ratio (SNR). This quantity is computed from data provided by the correlator in the Gun-Detector Electronics that also generates the coding pattern imposed on the outgoing beams. If the squared SNR ratio exceeds a threshold, this is taken as evidence that the beam is returning to the detector. The thresholds for SNR are chosen dependent on background fluxes. They represent a compromise between getting false hits (induced by strong variations in background electron fluxes) and missing true beam hits. The basic software loop that controls EDI operations is executed every 2 ms. As the times when the beams hit their detectors are neither synchronized with the telemetry nor equidistant, EDI data have no fixed time-resolution. Data are reported in telemetry slots. In Survey, using the standard packing mode 0, there are eight telemetry slots per second and Gyn Detector Unit (GDU). The last beam detected during the previous slot will be reported in the current slot. If no beam has been detected, the data quality will be set to zero. In Burst telemetry there are 128 slots per second and GDU. The data in each slot consists of information regarding the beam firing directions (stored in the form of analytic gun deflection voltages), times-of-flight (if successfully measured), quality indicators, time stamps of the beam hits, and some auxiliary correlator-related information. Whenever EDI is not in electron drift mode, it uses its ambient electron mode. The mode has the capability to sample at either 90 degrees pitch angle or at 0/180 degrees (field aligned), or to alternate between 90 degrees and field aligned with selectable dwell times. While all options have been demonstrated during the commissioning phase, only the field aligned mode has been used in the routine operations phase. The choices for energy are 250 eV, 500 eV, and 1 keV. The two detectors, which are facing opposite hemispheres, are looking strictly into opposite directions, so while one detector is looking along B the other is looking antiparallel to B (corresponding to pitch angles of 180 and 0 degrees, respectively). The two detectors switch roles every half spin of the spacecraft as the tip of the magnetic field vector spins outside the field of view of one detector and into the field of view of the other detector. These data are a by-product generated from data collected in electric field mode. Whenever no return beam is found in a particular time slot by the flight software to be reported will be flagged with the lowest quality level (quality zero). The ground processing generates a separate data product from these counts data. The EDI instrument paper can be found at: http://link.springer.com/article/10.1007%2Fs11214-015-0182-7. The EDI instrument data products guide can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 3 Electron Drift Instrument (EDI) Quality Zero Counts, Level 2 (L2), Survey Mode, 0.125 s Data
Electron Drift Instrument (EDI) Q0 Survey, Level 2, 0.125 s Data (8 samples/s). EDI has two scientific data acquisition modes, called electric field mode and ambient mode. In electric field mode, two coded electron beams are emitted such that they return to the detectors after one or more gyrations in the ambient magnetic and electric field. The firing directions and times-of-flight allow the derivation of the drift velocity and electric field. In ambient mode, the electron beams are not used. The detectors with their large geometric factors and their ability to adjust the field of view quickly allow continuous sampling of ambient electrons at a selected pitch angle and fixed but selectable energy. To find the beam directions that will hit the detector, EDI sweeps each beam in the plane perpendicular to B at a fixed angular rate of 0.22 °/ms until a signal has been acquired by the detector. Once signal has been acquired, the beams are swept back and forth to stay on target. Beam detection is not determined from the changes in the count-rates directly, but from the square of the beam counts divided by the background counts from ambient electrons, i.e., from the square of the instantaneous signal-to-noise ratio (SNR). This quantity is computed from data provided by the correlator in the Gun-Detector Electronics that also generates the coding pattern imposed on the outgoing beams. If the squared SNR ratio exceeds a threshold, this is taken as evidence that the beam is returning to the detector. The thresholds for SNR are chosen dependent on background fluxes. They represent a compromise between getting false hits (induced by strong variations in background electron fluxes) and missing true beam hits. The basic software loop that controls EDI operations is executed every 2 ms. As the times when the beams hit their detectors are neither synchronized with the telemetry nor equidistant, EDI data have no fixed time-resolution. Data are reported in telemetry slots. In Survey, using the standard packing mode 0, there are eight telemetry slots per second and Gyn Detector Unit (GDU). The last beam detected during the previous slot will be reported in the current slot. If no beam has been detected, the data quality will be set to zero. In Burst telemetry there are 128 slots per second and GDU. The data in each slot consists of information regarding the beam firing directions (stored in the form of analytic gun deflection voltages), times-of-flight (if successfully measured), quality indicators, time stamps of the beam hits, and some auxiliary correlator-related information. Whenever EDI is not in electron drift mode, it uses its ambient electron mode. The mode has the capability to sample at either 90 degrees pitch angle or at 0/180 degrees (field aligned), or to alternate between 90 degrees and field aligned with selectable dwell times. While all options have been demonstrated during the commissioning phase, only the field aligned mode has been used in the routine operations phase. The choices for energy are 250 eV, 500 eV, and 1 keV. The two detectors, which are facing opposite hemispheres, are looking strictly into opposite directions, so while one detector is looking along B the other is looking antiparallel to B (corresponding to pitch angles of 180 and 0 degrees, respectively). The two detectors switch roles every half spin of the spacecraft as the tip of the magnetic field vector spins outside the field of view of one detector and into the field of view of the other detector. These data are a by-product generated from data collected in electric field mode. Whenever no return beam is found in a particular time slot by the flight software to be reported will be flagged with the lowest quality level (quality zero). The ground processing generates a separate data product from these counts data. The EDI instrument paper can be found at: http://link.springer.com/article/10.1007%2Fs11214-015-0182-7. The EDI instrument data products guide can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 4 Electron Drift Instrument (EDI) Quality Zero Counts, Level 2 (L2), Survey Mode, 0.125 s Data
Electron Drift Instrument (EDI) Q0 Survey, Level 2, 0.125 s Data (8 samples/s). EDI has two scientific data acquisition modes, called electric field mode and ambient mode. In electric field mode, two coded electron beams are emitted such that they return to the detectors after one or more gyrations in the ambient magnetic and electric field. The firing directions and times-of-flight allow the derivation of the drift velocity and electric field. In ambient mode, the electron beams are not used. The detectors with their large geometric factors and their ability to adjust the field of view quickly allow continuous sampling of ambient electrons at a selected pitch angle and fixed but selectable energy. To find the beam directions that will hit the detector, EDI sweeps each beam in the plane perpendicular to B at a fixed angular rate of 0.22 °/ms until a signal has been acquired by the detector. Once signal has been acquired, the beams are swept back and forth to stay on target. Beam detection is not determined from the changes in the count-rates directly, but from the square of the beam counts divided by the background counts from ambient electrons, i.e., from the square of the instantaneous signal-to-noise ratio (SNR). This quantity is computed from data provided by the correlator in the Gun-Detector Electronics that also generates the coding pattern imposed on the outgoing beams. If the squared SNR ratio exceeds a threshold, this is taken as evidence that the beam is returning to the detector. The thresholds for SNR are chosen dependent on background fluxes. They represent a compromise between getting false hits (induced by strong variations in background electron fluxes) and missing true beam hits. The basic software loop that controls EDI operations is executed every 2 ms. As the times when the beams hit their detectors are neither synchronized with the telemetry nor equidistant, EDI data have no fixed time-resolution. Data are reported in telemetry slots. In Survey, using the standard packing mode 0, there are eight telemetry slots per second and Gyn Detector Unit (GDU). The last beam detected during the previous slot will be reported in the current slot. If no beam has been detected, the data quality will be set to zero. In Burst telemetry there are 128 slots per second and GDU. The data in each slot consists of information regarding the beam firing directions (stored in the form of analytic gun deflection voltages), times-of-flight (if successfully measured), quality indicators, time stamps of the beam hits, and some auxiliary correlator-related information. Whenever EDI is not in electron drift mode, it uses its ambient electron mode. The mode has the capability to sample at either 90 degrees pitch angle or at 0/180 degrees (field aligned), or to alternate between 90 degrees and field aligned with selectable dwell times. While all options have been demonstrated during the commissioning phase, only the field aligned mode has been used in the routine operations phase. The choices for energy are 250 eV, 500 eV, and 1 keV. The two detectors, which are facing opposite hemispheres, are looking strictly into opposite directions, so while one detector is looking along B the other is looking antiparallel to B (corresponding to pitch angles of 180 and 0 degrees, respectively). The two detectors switch roles every half spin of the spacecraft as the tip of the magnetic field vector spins outside the field of view of one detector and into the field of view of the other detector. These data are a by-product generated from data collected in electric field mode. Whenever no return beam is found in a particular time slot by the flight software to be reported will be flagged with the lowest quality level (quality zero). The ground processing generates a separate data product from these counts data. The EDI instrument paper can be found at: http://link.springer.com/article/10.1007%2Fs11214-015-0182-7. The EDI instrument data products guide can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
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.