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opencc-by-4.0Jan 2022View details →

Supporting Data - Double Ridge Formation over Shallow Water Sills on Jupiter's Moon Europa

This archive contains data produced in support of R. Culberg, D. M. Schroeder, G. Steinbrügge, Double Ridge Formation Over Shallow Water Sills on Jupiter’s Moon Europa, Nature Communications, 2022. This includes the WorldView imagery in Figure 1, the reprocessed radargrams underlying the radar analysis and inversion, and outputs of all inversion and sensitivity test runs. See the README file for a complete description of the files available in this archive.

opencc-by-4.0Jan 2022View details →

Supporting model output for article "Assessing the potential impact of river chemistry on Arctic coastal production"

The following is a summary of processed model output data from a series of HiLAT model runs. A description of the model runs and visualization of model output and analysis can be found in the accompanying manuscript. Gibson G. A., Elliott, S., Piliouras, A. Clement Kinney, J., Jeffery, N. (2022) Assessing the potential impact of river nitrate on coastal production in the Arctic. Frontiers in Marine Science: Coastal Ocean Processes. This work was supported by the Regional and Global Model Analysis (RGMA) program of the US Department of Energy’s Office of Science as a contribution to the HiLAT project. Additional support for this project was provided by the National Science Foundation, under award #173886.   River Nutrient Forcing The experiments involved modifying the nutrient concentrations in the river nutrient forcing files. The river nutrient files are specified during model setup. For use in the HiLAT model, GNEWS annual river nutrient inputs were partitioned into twelve monthly forcing values. The nearest ocean grid point to each of the GNEWS river mouth locations was identified and then, as with the runoff, the nutrient inputs for each river basin were spatially mapped to surface ocean model grid cells, which are 10 meters thick, such that the spatial pattern of river nutrient dispersion follows river water inputs to the oceans. The experiments were: i) The baseline model simulation: The 12 monthly values for each grid cell were constant in time. river_nutrients_GNEWS2000_gx1v6.nc ii) an experiment in which baseline Arctic River Nitrogen (NO 3 and NH 4 ) concentrations were doubled. river_nutrients_GNEWS2000_gx1v6_x2Arctic.nc iii) an experiment in which baseline Arctic River Nitrogen (DON and DIN) concentrations were scaled to the river volume discharge contained in runoff.daitren.iaf.20120419.nc river_nutrients_GNEWS2000_gx1v6_scaled_climatology.nc iv) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by two months. river_nutrients_GNEWS2000_gx1v6_shifted2m_climatology.nc v) an experiment in which the scaled river nutrient discharge (iii) was shifted earlier by a month and doubled in concentration. river_nutrients_GNEWS2000_gx1v6_shifted_climatology_x2.nc River nutrient fluxes are in units of nmol/cm2/s Only concentrations within the domain TLONG>=60 &TLONG <=340 & TLAT >=60 were modified in concentration/timing. Variables of interest: din_riv_flux: dissolved inorganic nitrogen river flux don_riv_flux: dissolved organic nitrogen river flux Each of the experiments is described in detail in Gibson et al (2022). --------------------------------------------- There are multiple versions of most output file types, corresponding to the river nutrient experiments that were conducted. Many variables in the output files are regional averages where model regions are indicated by a number *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering used in the accompanying manuscript. The numbers assigned in the analysis files aligns with the numbers assigned to regions within the region mask provided in the grid file. Grid File/region masks gx1v6_polar_mask_coast.5.22.20c.nc This file is an updated version of the standard grid file. It has been updated to include the addition of a coastal Arctic region variable ‘Arctic_Coast_Mask’ which indicates which grid cells are in the coastal regions used in the analysis and the Arctic_Region variable which indicates which grid cells are in the broader regions. Variables contained in this file are: Arctic_Coast_Mask: contains values 0-9 indicating which (if any) coastal region a grid cell is in Arctic_Region Mask: contains values 0-11 indicating which (if any) region a grid cell is in TLAT: latitude of grid cell TLONG: longitude of grid cell TAREA: Area of grid cell HT: Bathymetry of grid cell   <table> <tbody> <tr> <td> </td> <td> Arctic_Region (seas) </td> <td> Arctic_Coast_Mask (coast) </td> </tr> <tr> <td> Bering Sea </td> <td> 1 </td> <td> 1 </td> </tr> <tr> <td> Chukchi Sea </td> <td> 2 </td> <td> 2 </td> </tr> <tr> <td> East Siberian Sea </td> <td> 3 </td> <td> 3 </td> </tr> <tr> <td> Laptev Sea </td> <td> 4 </td> <td> 4 </td> </tr> <tr> <td> Beaufort Sea </td> <td> 5 </td> <td> 5 </td> </tr> <tr> <td> Barents Sea </td> <td> 6 </td> <td> 6 </td> </tr> <tr> <td> Canadian Basin </td> <td> 7 </td> <td> - </td> </tr> <tr> <td> Eurasian Basin </td> <td> 8 </td> <td> - </td> </tr> <tr> <td> Nordic Seas </td> <td> 9 </td> <td> 7 </td> </tr> <tr> <td> Labrador Sea </td> <td> 10 </td> <td> 8 </td> </tr> <tr> <td> Kara Sea </td> <td> 11 </td> <td> 9 </td> </tr> </tbody> </table> --------------   Model outputs that were analyzed in the manuscript are contained in three different kinds of output file. For each file type a file exists for each river nutrient experiment. The following series of files contains variables related to the particulate organic carbon flux to the sediment, demineralization and remineralization rates. bgc_T62_gx1GIF_nut-riv-BASELINE-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2xArcticN-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv_2XDC-coast_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-BASELINE-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2xArcticN-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-seas_region-sed-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-seas_region-sed-137-157.nc Variables contained in these are: POCTOSED_AVG* : Particulate organic carbon flux to sediment PONTOSED_AVG* : Particulate organic nitrogen flux to sediment SEDDENITRIF_AVG* : Sediment denitrification rate POC_PROD_AVG* : Production of Particulate organic carbon POC_FLUX_AVG* : Particulate organic carbon flux into layer/cell DON_REMIN_AVG* : Dissolved Organic Nitrogen remineralization rate DOC_REMIN_AVG* : Dissolved Organic Carbon remineralization rate DIAT_N_LIM_AVG* : Diatom nitrogen limitation DIAT_N_LIM_AVG* : Diatom nitrogen limitation DIAT_P_LIM_AVG* : Diatom phosphorous limitation DIAT_FE_LIM_AVG* : Diatom iron limitation DIAT_LIGHT_LIM_AVG*: Diatom light limitation SP_N_LIM_AVG* : Small phytoplankton nitrogen limitation SP_P_LIM_AVG* : Small phytoplankton phosphorous limitation SP_FE_LIM_AVG* : Small phytoplankton iron limitation SP_LIGHT_LIM_AVG* : Small phytoplankton light limitation Where * represents the coastal region number. ---------------------------------------- The following series of files contains primary production for the small and large phytoplankton groups and the zooplankton biomass. Coastal regional averages – based on regions marked in the Arctic_Coast_Mask variable bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_riv-BASELINE-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-ACM-137-157.nc bgc_T62_gx1GIF_nut-riv-2XDC-region-prod-ACM-137-157.nc Regional seas averages – based on regions marked in the Arctic_Region variable bgc_T62_gx1GIF_runoff-2xArcticN-region-prod-seas-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-region-prod-seas-137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-region-prod-seas-137-157.nc bgc_T62_gx1GIF_riv-BASELINE-region-prod-seas-137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-region-prod-seas-137-157.nc Variables contained in these files are: TAREA_SUM* – total area of the region PPSP_REGSUM* – sum of primary production by small phytoplankton in a region PPDIAT_REGSUM*– sum of primary production by diatoms in a region ZOOC_AVG*– sum of zooplankton biomass in a region ---------------------------------------- The following series of files contains ice associated variables and mixed layer nutrients bgc_T62_gx1GIF_nut_riv-2xArcticN-ice_coastal-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-BASELINE-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-2XDC-ice_coastal -137-157.nc bgc_T62_gx1GIF_nut-riv-2xArcticN-ice_seas-137-157.nc bgc_T62_gx1GIF_nut-riv-mon-clim-ice_seas -137-157.nc bgc_T62_gx1GIF_nut-riv-month-shiftx2-ice_seas -137-157.nc bgc_T62_gx1GIF_nut-riv-BASELINE-ice_seas -137-157.nc bgc_T62_gx1GIF_nut-riv-2mon-shift-ice_seas-137-157.nc HI_REGAVG* : Regional averaged ice depth HS_REGAVG* : Regional averaged snow depth ICEAREA_REGSUM* : Regional sum ice area ICEVOL_REGSUM*: Regional sum volume area MLAM_REGAVG* : Regional average ammonium concentration in mixed layer MLNIT_REGAVG* : Regional average nitrate concentration in mixed layer PP_REGAVG* : Regional average primary production (ice algae) PP_REGSUM* : Regional total primary production (ice algae) TAREA_SUM* : Total area of region TIME : time

Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST

The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat. The stored input enviormental and operational parameters are: <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline: <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> contact: nandar.hlaing@uliege.be

Supporting data for "Robust spin squeezing from the tower of states of U(1)-symmetric spin Hamiltonians"

Supporting data for "Robust spin squeezing from the tower of states of U(1)-symmetric spin Hamiltonians" (<a href="https://link.aps.org/doi/10.1103/PhysRevA.105.022625">https://link.aps.org/doi/10.1103/PhysRevA.105.022625</a>, <a href="https://arxiv.org/abs/2103.07354">https://arxiv.org/abs/2103.07354</a>), by Tommaso Comparin, Fabio Mezzacapo and Tommaso Roscilde. If you use these data in a scientific work, please cite the corresponding article. For additional details, please contact Tommaso Comparin (tommaso.comparin@ens-lyon.fr). This dataset includes tVMC results for several values of the coupling exponent alpha and of the system size N (see file names). Each file includes a set of relevant observables (see file header). These results are directly shown in Figures 1, 3, 7, 9. Further data processing leads to Fig. 4. Details on the variational Ansatz for tVMC - We employ the pair-product Ansatz defined in the main text. In general, there are N*(N-1)/2 independent spin pairs with 4 possible states for each pair, leading to 4*N complex coefficients. - Thanks to translational invariance, we can use coefficients which only depend on the distance between the two spins, reducing the number of independent pairs to N/2 (for a one-dimensional chain with periodic boundary conditions). - We also impose the spin-inversion symmetry for each spin pair, so that configurations like {up,down} and {down,up} have the same coefficient. - Therefore the total number of variational coefficients in our tVMC simulations is equal to N. NOTE: Data are provided without error bars. An analysis of the statistical/systematic errors is included in the folder Errors, for some representative cases.

Data and Material for 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'

Data and Material supporting the paper 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'.

opencc-by-4.0Dec 2021View details →

Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective

Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. Functional Ecology, 2022. We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.

Raw data supporting "mScarlet fluorescence lifetime reports lysosomal pH quantitatively"

Original dataset and processing code supporting the preprint (scientific publication) "mScarlet fluorescence lifetime reports lysosomal pH quantitatively." Publication Abstract: The lysosome maintains a highly acidic pH, which is critical for successful lysosomal catabolism. Lysosomal pH (pHlys) is difficult to measure because of the simultaneous need for a sensor with large dynamic range, genetic targetability, low pKa, and a quantitative readout. Here, we demonstrate that the fluorescence lifetime of the mScarlet-LAMP1 fusion protein quantitatively reports lysosomal pH, exhibiting a large dynamic range and a pKa well-tuned for the lysosome. Because fluorescence lifetime is an intrinsic property, pH measurements can be achieved in a single fluorescence channel. mScarlet-LAMP1 lifetime allows for individual lysosome-resolved recordings, a critical advance in describing and understanding pHlys heterogeneity. Using this biosensor, we quantify heterogeneity of pHlys in cultured cells at rest and over time during drug treatment. We anticipate that mScarlet-LAMP1 will enable new insights into the diversity of lysosomal physiology and ionic milieu.

Supporting Dataset for the Analysis on TSO-DSOs Cooperation and Stable Cost Allocation for the Joint Procurement of Flexibility (Network and Bid List)

The data provides supporting material for the two case studies in Chapter 5 of CoordiNet D6.2 (the deliverable is available at <a href="https://coordinet-project.eu/publications/deliverables">https://coordinet-project.eu/publications/deliverables</a>) and the two case studies in paper on TSO-DSO cooperation (available at <a href="https://arxiv.org/abs/2111.12830">https://arxiv.org/abs/2111.12830</a>). The dataset is cooresponding to two case studies. In the first case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). The interface flow limit is TPmax. In the second case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three Matpower systems 18-bus distribution networks, who are named as DN_1, DN_2, DN_3.   All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line. The interconnected system is fully represented in "Network_XXX.xlsx", in which: <ul> <li>System: transmission (TN) or distribution (DN_XXX);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected.. The generated orderbook is presented in "OrderbookTN_XXX.xlsx" (transmission system) and "OrderbookDN_XXX.xlsx" (distribution systems): <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_XXX) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> Source of the systems' topology: [1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010. Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.

Pacific salmon population time-series dataset to support Appendix S1: Data and additional information on declines of Pacific Salmon

Dataset used to support the main paper 'Protecting our coast for everyone’s future: Indigenous and scientific knowledge support marine spatial protections proposed by Central Coast First Nations in Pacific Canada' by Reid et al. 2022. Dataset cited in Appendix S1 regarding trends in adult salmon abundances in the Central Coast. The data were as compiled by Will Atlas from the <a href="https://wildsalmoncenter.org/">Wild Salmon Center</a> to describe trends in the abundance of adult salmon returning to the Central Coast, which is the sum of escapement and harvest, as derived from the following sources: <ol> <li>Escapement data from DFO: <a href="https://open.canada.ca/data/en/dataset/c48669a3-045b-400d-b730-48aafe8c5ee6">NuSEDS-New Salmon Escapement Database System - Open Government Portal (canada.ca)</a></li> <li>Harvest rates estimated by Karl English and colleagues and available at: <a href="https://data.salmonwatersheds.ca/data-library/">Salmon Watersheds Program - Data Library</a>.</li> <li>Information on total harvest that is reported in the DFO post season review (DFO 2020).</li> </ol>

Open database to support forensic investigation of disasters in South East Asia: FORINSEA v1.0

Forensic investigation of disasters (FORIN) is a conceptual framework and research guide that focuses on the investigation of root causes of disaster risk and occurrence. Underlying the FORIN conceptual framework is the understanding that historical processes, operating asynchronously at different spatial and temporal scales, configure the specific circumstances in which disasters occur. An objective of FORIN research is to accumulate lessons and experience in a systematic way that can lead to improved choices in the future. FORINSEA aims to support this objective by providing an open database suitable to FORIN enquiries in South East Asia region. FORINSEA1.0 provides a comprehensive and coherent historical record of disasters, from 1945 until 2020, socio-economic policies and development of key infrastructure at the hydrological catchments of the Red River Delta in Vietnam and the Marikina river basin in the Philippines. The FORINSEA1.0 dataset allows researchers, for the first time, to explore and make use of geocoded data on major disasters affecting the two large and rapidly expanding cities of Hanoi and Metro Manila and their catchment areas.

Documentation and digital files in support of "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938–2021" by Carl Tape and Anthony Lomax: Parts B, C, and D

These files support a manuscript to be submitted entitled "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938-2021," by Carl Tape and Anthony Lomax. This collection contains Parts B, C, and D. A separate collection contains Part A. This research was supported by the U.S. Geological Survey (USGS), Department of the Interior, under USGS award number G19AP00050.

opencc-by-4.0Oct 2021View details →

Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)

Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen. Contents of set 2:     - Data set and materials used for the human-robot interaction experiment and for data analysis Files:     - "Questionnaire.pdf": Questionnaire used for data collection.     - "Video links.txt": Weblinks to stimuli videos used.     - "Data set.xls": Collected raw data.     - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.     - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.     - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.     - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM. The experiment procedure is described in the paper. The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201

Supporting data set for: Simulations of the Electrochemical Oxidation of Shape-Selected Nanoparticle Catalysts

This dataset contains input and output files for simulations of the oxidation of a set of shape-selected, 3 nm platinum nanoparticles associated with the manuscript found at https://arxiv.org/abs/2201.07605. The simulations are performed using a grand-canonical Monte-Carlo algorithm[1,2] in combination with the ReaxFF reactive force field method as implemented in the Amsterdam Density Functional (ADF) software package version 2017.106 by Software for Chemistry and Materials (SCM). The Pt/O ReaxFF force field parameterized by Fantauzzi et al. was used for the simulations.[3] Simulations were performed at oxygen chemical potential conditions corresponding to 200-1000 K at ultra-high vacuum (UHV, pO2 = 10-10 mbar) and 400-1200 K at near-ambient pressure (NAP, pO2 = 1 mbar) conditions. The following nanoparticle shapes were used as input structures for the simulations: (111)-indexed octahedron, (100)-indexed cube, (110)-indexed dodecahedron, (111)- and (100)-indexed cuboctahedron, mixed-indexed sphere, and (730)-indexed tetrahexahedron. The folder structure is as follows: Particle shape -> pressure condition -> temperature condition -> simulation input and output files The simulation input and output files are of the following filetypes: control: Input parameters for the ReaxFF software. control_MC: Input parameters for the GCMC subroutine that interacts with the ReaxFF software. geo: Atomic input coordinates in BGF file format. geo_MCXXXXXX: Atomic output coordinates in BGF file format and ReaxFF total energy result for GCMC step XXXXXX. Simulations were performed for a total of 25,000 iterations. Only accepted GCMC steps result in the creation of a geo_XXXXXX output file. Therefore, the index XXXXXX is not continuous since output files are not written at every iteration. Other ReaxFF-specific output has been filtered in order to declutter the dataset. [1] T. P. Senftle, R. J. Meyer, M. J. Janik, A. C. T. van Duin, J. Chem. Phys. 2013, 139, 044109. [2] T. P. Senftle, M. J. Janik, A. C. T. van Duin, J. Phys. Chem. C 2014, 118, 4967–4981. [3] D. Fantauzzi, J. Bandlow, L. Sabo, J. E. Mueller, A. C. T. van Duin, T. Jacob, Phys. Chem. Chem. Phys. 2014, 16, 23118–23133.

opencc-by-4.0Jun 2021View details →

Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats - Datasets and supporting files

The supporting datasets, scripts, and supplementary information for the manuscript, "Habitat Protection Indexes - new monitoring measures for the conservation of threatened marine habitats," are available within this repository. We conduct an analysis on the coverage of protected areas that cover six threatened marine and coastal and developed two indexes, the Local Proportion of Habitat Protected Index and the Global Proportion of Habitat Protected Index, describing the protection of these habitats locally and globally. The habitats considered are the following: cold corals, warm water corals, knolls and seamounts, mangroves, saltmarshes, and seagrasses. The index scores of each jurisdiction are made available for download in the dataset: habitat_protection_indexes_average.csv The habitat specific index scores for each jurisdiction are made available for download in the dataset: habitat_protection_indexes.csv.  Column name descriptions are available in the text file: Column_name_descriptions_20220301 The scripts used to run the workflow to calculate the indexes, create figures, and calculate statistics for the manuscript are also included. The script 01_Workflow sources the first 9 scripts in the scripts folder to calculate the indexes which relies on the functions script within the functions folder. The rest of the scripts in the folder create the figures and calculate the statistics for the manuscript. A readme pdf file is included here to ease with reproducing the workflow, but we strongly suggest to please visit our github (<a href="https://github.com/jkumagai96/Marine_Habitat_protection">https://github.com/jkumagai96/Marine_Habitat_protection</a>) to reproduce the entire calculation where we provide detailed information on how to run the workflow and package management.

Dataset supporting the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures. ACS Nano 14, 6285 (2020)"

Dataset corresponding to theoretical calculations in the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures" ACS Nano 14, 6285 (2020), DOI: <a href="https://doi.org/10.1021/acsnano.0c02498">10.1021/acsnano.0c02498</a> List of files: Several folders corresponding to the figures of the paper. They contain the following files: <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).  </li> </ul>

Dataset supporting the paper "Power discontinuity and shift of the energy onset of a molecular de-bromination reaction induced by hot-electron tunneling. Nanoscale 13, 15215 (2021)"

Dataset corresponding to theoretical calculations in the paper "Power discontinuity and shift of the energy onset of a molecular de-bromination reaction induced by hot-electron tunneling. Nanoscale 13, 15215 (2021)". DOI: <a href="https://doi.org/10.1039/D1NR04229G">10.1039/D1NR04229G</a> List of files: Several folders corresponding to the figures of the paper. They contain: <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).  </li> </ul>

Supporting Online Toxicity Detection with Knowledge Graphs: Data

This data repository contains the output files from the analysis of the paper "Supporting Online Toxicity Detection with Knowledge Graphs" presented at the International Conference on Web and Social Media 2022 (ICWSM-2022).   The data contains annotations of gender and sexual orientation entities provided by the Gender and Sexual Orientation Ontology (https://bioportal.bioontology.org/ontologies/GSSO). We analyse demographic group samples from the Civil Comments Identities dataset (https://www.tensorflow.org/datasets/catalog/civil_comments).

Supporting Material for "Automatic Mapping of Small Lunar Impact Craters Using LROC NAC Images"

The supporting material for 'Automatic Mapping of Small Lunar Impact Craters Using LROC NAC'. This File contains: <ul> <li>Supporting Material (.pdf);</li> <li>List of True Positive detections (.csv);</li> <li>List of all ground truth and CDA detections (.csv);</li> <li>Folder (.zip) with images of the evaluation sites (.pdf); and</li> <li>Folder (.zip) with training image tiles (.png and .txt).</li> </ul> Refer to Supporting Material (.pdf) for file name and header information.

opencc-by-4.0May 2021View details →

Supporting information - A value creation model from science-society interconnections: Components and archetypes

Data protocol and datasets used for the study entitled 'A value creation model from science-society interconnections: Components and archetypes'.  Abstract of the paper: The interplay between science and society takes place through a wide range of intertwined relationships and mutual influences that shape each other and facilitate continuous knowledge flows. Stylised consequentialist perspectives on valuable knowledge moving from public science to society in linear and recursive pathways, whilst informative, cannot fully capture the broad spectrum of value creation possibilities. As an alternative we experiment with an approach that gathers together diverse science-society interconnections and reciprocal research-related knowledge processes that can generate valorisation. Our approach to value creation attempts to incorporate multiple facets, directions and dynamics in which constellations of scientific and societal actors generate value from research. The paper develops a conceptual model based on a set of nine value components derived from four key research-related knowledge processes: production, translation, communication, and utilization. The paper conducts an exploratory empirical study to investigate whether a set of archetypes can be discerned among these components that structure science-society interconnections. We explore how such archetypes vary between major scientific fields. Each archetype is overlaid on a research topic map, with our results showing that different archetypes correspond to distinctive topic areas. The paper finishes by discussing the significance and limitations of our results and the potential of both our model and our empirical approach for further research.