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315 results for “Zeros”
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>
Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.
<p>This dataset includes multiple fields: (i) files for monthly and annual fields for the max curl line and the zero curl line at 0.1 degree longitudinal resolutions; (ii) files for monthly and annual GS path obtained from Altimetry and originally processed by Andres (2016) at 0.1 degree longitudinal resolution. The maximum curl line (MCL) and the zero curl line (ZCL) calculations are briefly described here and are based on the original wind data (at 1.25 x 1.25 degree) provided by the Japanese reanalysis (JRA-55; Kobayashi et al., 2015) and available at https://zenodo.org/record/8200832 (Gifford et al. 2023). For details see Gifford, 2023. </p> <p>The wind stress curl (WSC) fields used for the MCL and ZCL calculations extend from 80W to 45W and 30N to 45N at the 1.25 by 1.25-degree resolution. The MCL is defined as the maximum WSC values greater than zero within the domain per 1.25 degree longitude. As such, it is a function of longitude and is not a constant WSC value unlike the zero contour. High wind stress curl values that occurred near the coast were not included within this calculation. After MCL at the 1.25 resolution was obtained the line was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. The smoothed MCL lines at 0.1 degree resolution are provided in separate files for monthly and annual averages (2 files). Similarly, 2 other files (monthly and annual) are provided for the ZCL. </p> <p>Like the MCL, the ZCL is a line derived from 1.25 degree longitude throughout the domain under the condition that it's the line of zero WSC. The ZCL is constant at 0 and does not vary spatially like the MCL. If there are more than one location of zero curl for a given longitude the first location south of the MCL is selected. Similar to the MCL, the ZCL was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. </p> <p>The above files span the years from 1980 through 2019. So, the monthly files have 480 months starting January 1980, and the annual files have 40 years of data. The files are organized with each row being a new time step and each column being a different longitude. Therefore, the monthly MCL and ZCL files are each 480 x 351 for the 0.1 resolution data. Similarly, the annual files are 40 x 351 for the 0.1 degree resolution data. </p> <p><strong>Note that the monthly MCLs and ZCLs are obtained from the monthly wind-stress curl fields. The annual MCLs and ZCLs are obtained from the annual wind-stress curl fields.</strong></p> <p>Since the monthly curl fields preserves more atmospheric mesoscales than the annual curl fields, the 12-month average of the monthly MCLs and ZCLs will not match with the annual MCLs and ZCLs derived from the annual curl field. The annual MCLs and ZCLs provided here are obtained from the annual curl fields and representative metrics of the wind forcing on an annual time-scale. </p> <p>Furthermore, the monthly Gulf Stream axis path (25 cm isoheight from Altimeter, reprocessed by Andres (2016) technique) from 1993 through 2019 have been made available here. A total of 324 monthly paths of the Gulf Stream are tabulated. In addition, the annual GS paths for these 27 years (1993-2019) of altimetry era have been put together for ease of use. The monthly Gulf Stream paths have been resampled and reprocessed for uniqueness at every 0.1 degree longitude from 75W to 50W and smoothed with a 100 km (10 point) running average via matlab. The uniqueness has been achieved by using Consolidator algorithm (D’Errico, 2023). </p> <p>Each monthly or annual GS path has 251 points between 75W to 50W at 0.1 degree resolution. </p>
Species-level estimated abundances and zero counts of nighttime collected female mosquitoes 2014 - 2022 (Derived from NEON Mosquitoes sampled from CO2 traps (DP1.10043.001, RELEASE-2024))
This Level 2 data package contains species level estimated abundances, including zero counts, and estimated mean number of female mosquitoes per trap derived from the NEON Mosquitoes sampled from CO2 traps (DP1.10043.001), RELEASE-2024 Level 0 data (https://doi.org/10.48443/3cyq-6v47). The data set includes mosquito records of traps collecting mosquito samples at night, for up to 24 trap hours, across a total of 20 terrestrial core and 27 terrestrial gradient sites from 2014 to 2022. To ensure high confidence in abundance estimates, records were only included when at least 90% of collected individuals were identified to sex, and 90% of female specimens were identified to species. Information across multiple QC/QA fields within the NEON mosquito data was evaluated to identify and exclude records where confidence in estimated abundances may have been compromised. Species level zero counts were added for all species collected at least once within the sampling year and trap location. Additionally, species level zero counts were included for trap events where only male mosquitoes had been collected or where QC/QA remarks indicated traps were inactive due to cold temperatures. The data set provides an analysis ready time series of estimated abundances across NEON sites and plots. An R Markdown file that contains descriptions of the QC/QA and data filtering steps along with annotated code, as well as data tables used to filter active and inactive trap events based on QC/QA fields, are published with the data package. Any questions about this data package should be directed to Amely Bauer listed under contacts.
Zero-degree isotherm latitude (ZIL) position over Antarctica: Historical and Projections
<p>This is the dataset associated to the research 'Southward migration of the zero-degree isotherm latitude over the Southern Ocean and the Antarctic Peninsula: extent and implications' published in <i>Science of the Total Environment</i>.</p><p>This repository contains:</p><ul><li><strong>ZIL_ERA5_1957-2020_position.zip:</strong> Historical position of the ZIL for every longitude point in ERA5 from 1957 to 2020 for different <i>seasons</i>. Files named:<ul><li>ZIL_ERA5_1957-2020<i>[season]</i>position.csv<ul><li>Dimensions: [lons, years]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_timeseries.csv:</strong> Historical spatially averaged position of the ZIL for Antartica (Ant) and the Antarctic Peninsula (AP) in ERA5 from 1957 to 2020 for different <i>seasons</i>. File named:<ul><li>ZIL_ERA5_1957-2020_timeseries.csv<ul><li>Dimensions: [years, season_area]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_meanposition.csv:</strong> Historical temporally averaged position of the ZIL in ERA5 from 1957 to 2020 for different <i>seasons </i>and <i>months</i>. File named:<ul><li>ZIL_ERA5_1957-2020_meanposition.csv<ul><li>Dimensions: [lons, season/month]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_Historical_position.zip:</strong> Mean position of the ZIL for every longitude point in Historical simulations of CEMIP6 from 1957 to 2014 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_Hist_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP2-45.zip:</strong> Mean position of the ZIL for every longitude point under the SSP2-4.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP2-45_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP2-45_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP5-85.zip:</strong> Mean position of the ZIL for every longitude point under the SSP5-8.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 and trends for the period 2015-99 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP5-85_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP5-85_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li></ul><p><i><strong>seasons</strong></i> are:</p><ul><li>ANN: Annual mean</li><li>DJF: December-January-February (Summer)</li><li>MAM: March-April-May (Autumn)</li><li>JJA: June-July-August (Winter)</li><li>SON: September-October-November (Spring)</li></ul><p><i><strong>areas</strong></i> are:</p><ul><li>Ant: All Antarctica</li><li>AP: Antarctic Peninsula</li></ul><p><strong>Note:</strong> CEMIPT6 models include a column with CEMIP6 model average</p><p><strong>Version control</strong></p><p>v1.0 - Initial version<br>v1.1 - Change ERA5 dataset calculations from preliminary version of ERA5 to final version of ERA5</p><p> </p><p><strong>How to cite</strong></p><p>If you use this dataset, please cite the accompanying paper as:</p><p> </p><p><strong>Complementary code</strong></p><p>You can find the jupyter notebooks to complement the research in: <a href="https://doi.org/10.5281/zenodo.10063849">https://doi.org/10.5281/zenodo.10063849</a></p><p> </p><p><strong>Contact</strong></p><p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></p>
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
Set of N integers between -30 and 30 with sum and cubic sum up to zero for 4<N<13
<p><strong>Anomalies</strong></p> <p>Solutions obtained with the python package: <a href="http://doi.org/10.5281/zenodo.5526558">anomalies</a> based on the method to find anomaly free solutions of the standard model extended with an Abelian Dark Symmetry with <em>N</em> right-handed singlet chiral fields described in <a href="https://arxiv.org/abs/1905.13729">arXiv:1905.13729</a> [PRL]:</p> <p><strong>Data scheme</strong></p> <ul> <li>'l': integer lists → input to obtain the 'solution' by using the <a href="http://doi.org/10.5281/zenodo.5526558">anomalies</a> package</li> <li>'k': integer lists → input to obtain the 'solution' by using hte <a href="http://doi.org/10.5281/zenodo.5526558">anomalies</a> package</li> <li>'solution': list → of integers, <span class="math-tex">\(\large z_i\)</span><sub> </sub>which satisfy <span class="math-tex">\(\large\displaystyle{ \sum_{i=1}^N z_i=0}\)</span> and <span class="math-tex">\(\large\displaystyle{ \sum_{i=1}^N z_i^3=0}\)</span> .</li> <li>'n': integer → number of integers in 'solution', <em>N</em>.</li> </ul> <pre> <strong>USAGE</strong></pre> <pre><code class="language-python">#Example of JSON file usage in Python with pandas (see also json module) >>> import pandas as pd >>> df=pd.read_json('solutions.json.gz') >>> df[:2] l k solution gcd n 0 [1, 2] [0, -3] [1, 5, -7, -8, 9] 1 5 1 [-2, -1] [0, -1] [2, 4, -7, -9, 10] 1 5</code></pre> <p><strong>Data:</strong><br> 2 296 615 solutions with <span class="math-tex">\(\large 5\le N\le 12\)</span> integers until `|32|` [JSON]</p>
Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based cathodes used in CO2 electrolysers"
<p>Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based CO2 electrolysers", published in Journal of Materials Chemistry A 2023 11:5083–5094, doi: <a href="https://doi.org/10.1039/D2TA06965B">10.1039/D2TA06965B</a></p> <p>Folder names describe the type of data content.</p>
Data for "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments"
<p>The processed data supporting the Global Environmental Change publication "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments".</p> <p>These data can be analyzed and visualized with the code at: <a href="https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare">https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare</a></p> <p>For a description of each file & the variables contained, please look to the README file.</p>
Unintended cation crossover influences CO2 reduction selectivity in Cu-based zero-gap electrolysers
<p>Dataset for the publication "Unintended cation crossover influences CO2 reduction selectivity in Cu-based zero-gap electrolysers"</p>
Replication Data for: "Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts"
<p>These datasets contain all the raw data needed to replicate the results from our paper <em>Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts</em> published in eLife - <a href="https://doi.org/10.7554/eLife.81080">https://doi.org/10.7554/eLife.81080</a></p>
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
CESM2 Idealized Experiment Output: Summer atmospheric response to zero May North American snow cover
<p>The National Center for Atmospheric Research’s Community Earth System Model version 2.2 (CESM2) (Danabasoglu et al., 2020) was run in the Atmospheric Model Intercomparison Project (AMIP) configuration. SSTs and sea-ice were prescribed as monthly varying seasonal cycles based on the observed climatology from 2005 to 2015 (i.e., component set: F2010climo) (Hurrell et al., 2008). We employed the Community Atmosphere Model version 6 (CAM6) (Bogenschutz et al., 2018)<span> </span>as the atmospheric component and the Community Land Model version 5 (CLM5) (Lawrence et al., 2019) as the land-surface component. Each model was run with a horizontal resolution of 0.9˚ latitude by 1.25˚ longitude.</p> <p>We ran a control simulation in this configuration for ten consecutive years. We then modified the land-surface restart files for May 1st of each year by reducing the snow cover over North America to zero. Using these modified files, we then completed a reduced snow simulation by rerunning three-month simulations from May through July for each of the ten years. </p>
Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison''
<p>Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison'', published in Applied Catalysis B: Environmental 2023 335:122885, doi: <a href="https://doi.org/10.1016/j.apcatb.2023.122885">10.1016/j.apcatb.2023.122885</a></p> <p>Folder names describe the type of data content.</p>
Data Analysis for "Laser Cooling of a Nanomechanical Oscillator to Its Zero-Point Energy"
<p>Data Analysis for the paper "Laser Cooling of a Nanomechanical Oscillator to Its Zero-Point Energy". All the original data and analysis codes in Matlab are provided. In addition, we provide a python notebook with detailed description of the data analysis.</p>
Wide-angle and simultaneously wideband blazing (deflection) enabling multifunctionality in metagratings comprising epsilon-near-zero materials
<p>Dataset for the selected figures from the publication.</p> <p>The research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413.</p>
Emissions gap NDC and net zero findings after COP27
<p>Analysis of the level and robustness of various government climate pledges, both 2030 NDCs and net zero goals. This repository contains the data required to run <a href="https://github.com/Rlamboll/Emissions_Gap">https://github.com/Rlamboll/Emissions_Gap</a>.</p> <p>"Analysis_update_12_14 (2).xlsx" contains the 2023 assessment of net zero pledges by governments in terms of both their quality and content. "Data_forextensions_PostCOP27_master.xlsx" contains the estimates of emissions until 2030 based on NDCs after COP27, "Data_forextensions_EGR2023_master.xlsx" contains the updated version for the paper "Credibility gap in net-zero climate targets leaves world at high risk".</p> <p>"2022_emission_gap_temp_summary_data.csv" is the output of the complete process. </p> <p>"kyoto_and_co2_emissions_summary_23.65.csv" is the Kyoto total and CO2 emissions for each scenario, "infilled_extended_and_infilled_unep_23.65.csv" is the complete set of emissions for each scenario. </p> <p>Versions 1.0.0 and 1.0.1 are identical in terms of processing, but more data is uploaded for 1.0.1 with scenarios with additional carbon price increase rates. Version 1.1.0 contains the Data_forextensions_EGR2023_master file. Version 1.2.0 contains a bugfix affecting net zero targets applied to the OECD+ regional emissions, and includes emissions data. </p> <p>Data and calculation are associated with the paper at DOI: 10.1126/science.adg6248</p>
Data Release: "No evidence that the majority of black holes in binaries have zero spin"
<p>This dataset contains the results presented in "<em>No evidence that the majority of black holes in binaries have zero spin</em>".</p> <p>In this paper, we systematically explored the effective and component spin distributions of binary black holes among the LIGO/Virgo GWTC-3 catalog. In particular, we tried to answer the following core questions, which have been the subject of active exploration and some debate in the literature:</p> <p><em>1. Is there an excess of binary black holes with vanishing spin, as predicted by some theories of angular momentum transport in stellar cores?</em></p> <p><strong>We find no evidence for an excess of vanishing spin systems.</strong> This finding is confirmed by three complementary analyses: one relying only on the Bayes factors between spinning and non-spinning priors for each BBH observation, one that seeks to model the distribution of effective aligned spins, and one modeling the distribution of component spin magnitudes and misalignment angles. Instead, we find BBH spin magnitudes to be consistent with a single, continuous distribution that remains finite at magnitude zero.</p> <p><em>2. Do there exist binaries with component spins misaligned by more than 90 degrees relative to their orbits?</em></p> <p><strong>We find a strong preference for the existence of such strongly misaligned spins.</strong> Our analysis of the BBH component spin distribution indicates that at least some component spins are misaligned from their orbits by more than 90 degrees. This result is robust under a variety of modeling choices regarding both the distribution of component spin magnitudes and tilts.</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/gwtc3-spin-studies/">https://github.com/tcallister/gwtc3-spin-studies/</a>. This repository includes <a href="https://github.com/tcallister/gwtc3-spin-studies/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/gwtc3-spin-studies/build/html/index.html">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The files <em>sampleDict_FAR_1_in_1_yr.pickle</em> and <em>injectionDict_FAR_1_in_1.pickle</em>, used as inputs to our analyses, are created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a> (see also <a href="https://zenodo.org/record/6505409">https://zenodo.org/record/6505409</a>).</li> <li>The file <em>posteriors_gaussian_spin_samples_FAR_1_in_1.json</em>, used for figure generation, was published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="https://zenodo.org/record/5655785">https://zenodo.org/record/5655785</a>).</li> </ul>
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature
<p><strong>Datasets</strong></p> <ol> <li>DISTANT-CTO is a weakly-labelled dataset of 'Intervention' and 'Comparator' entity annotated sentences. The dataset was obtained using candidate generation the approach described in "DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low Resource Entity Extraction Using Clinical Trials Literature". <ol> <li>distantcto_high_conf.txt - ds conf 1.0 (full dataset)</li> <li>extraction1_pos_posnegtrail_conf09.txt - ds conf 0.9 (partial dataset)</li> </ol> </li> <li>The physio test set is a dataset comprising 153 PICO annotated randomized controlled trial abstracts from Physiotherapy and Rehabilitation. This dataset was used as an additional benchmark to evaluate the generalization power of the weakly annotated dataset and NER model for this sub-domain.</li> </ol> <p> </p> <p><strong>Utility</strong></p> <p>The dataset could be used as an input for training 'Intervention' named-entity recognition (NER) models.</p> <p> </p> <p><strong>Availability</strong></p> <p>This directory includes extraction1_pos_posnegtrail_conf09.txt - This text data file contains all the weak annotations (source intervention terms mapped onto target sentences) from clinicaltrials.org (CTO) with a confidence score of 0.9 and above.</p> <p>The directory also includes ‘physio_sent_annot2POS_posnegtrail.txt’ – This data file contains manually annotated (Intervention entity) data from the physiotherapy and rehabilitation domain. It follows a roughly similar structure as described in the ‘Description for long targets’ section. (‘Participant’ and ‘Outcome’ annotations are removed from this file)</p>
Raw data for the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer"
<p>In the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer" we described our investigation on the size-dependent degradation behavior of Ag NPs (10, 40, and 100 nm in size) on GDE during CO<sub>2</sub> electrolysis. Here we present the dataset the work was based on. For each figure in the article and the supporting information we provide a set of raw and unprocessed data.</p>
A Review Of Metaheuristics in Fuzzy Time Series Applied To Zero Inflated Datasets
<p>The manufacturing efficiency reflects directly on the use of natural resources and leads to a higher environmental impact than needed. Efficiency of an industry can be achieved in many ways, but it always starts with demand management. However some products have erratic and irregular demand patterns as the nature of the usage varies, and this often leads to zero inflated demand datasets, said datasets are difficult to forecast due to the nature of traditional models which usually use moving averages, state of the art machine learning models can achieve good results but use too much data for training. Under this background, this paper investigates the Fuzzy Time Series models and how it evolved from its inception to present time and how the usage of metaheuristics can help with forecasting demand on a small dataset with a high count of zeros, then applies the techniques to other zero inflated dataset to verify its generalization capabilities. Finally another model is applied as comparison.</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.