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31,887 results for “three”
Three-Enzyme Phosphorylase Cascade for Integrated Production of Short-chain Cellodextrins
<p>Cellodextrins are linear β-1,4-gluco-oligosaccharides that are well soluble in water up to a degree of polymerization (DP) of ~6. Soluble cellodextrins have promising applications as nutritional ingredients. A DP-controlled, bottom-up synthesis from expedient substrates is desired for their bulk production. Here, we developed a three-enzyme glycoside phosphorylase cascade for the conversion of sucrose and glucose into the short-chain cellodextrins (DP range 3 – 6). The cascade reaction involves iterative β-1,4-glucosylation of glucose from α-glucose 1-phosphate (αGlc1-P) donor that is formed in situ from sucrose and phosphate. With final concentration and yield of soluble cellodextrins set as targets for the biocatalytic synthesis, we identified, and partly optimized, three major factors of reaction efficiency: the ratio of enzyme activities; the ratio of sucrose and glucose; and the phosphate concentration used. We demonstrate efficient use of the phosphate/αGlc1-P shuttle for cellodextrins production and obtain the soluble product at 40 g/L under near complete utilization of the donor substrate offered (88 mol.% from 200 mM sucrose). The productivity was 16 g/(L h). Through a simple two-step route, the soluble cellodextrins were recovered from the reaction mixtures in ≥ 95% purity and ~92% yield. Overall, this study provides the basis for their integrated production. </p>
Official GO FAIR Foundation icons for the Three-Point FAIRification Framework
<p>The official icons for the Three-Point FAIRification Framework (3PFF): Metadata for Machines Workshops, FAIR Implementation Profiles and FAIR Orchestration, created by the GO FAIR Foundation.</p>
JOINT WEBINAR: SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels
<p>On May 21, 2024, an informative webinar titled “SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels” was held from 12:00 to 13:00 CET. This online event was a collaborative effort among three key projects—ALFAFUELS, COCPIT, and FUELGAE—aimed at advancing renewable fuel technologies. Attendees were introduced to the main concepts, ambitions, and methodologies behind these innovative European initiatives. The event was structured in six parts, including presentations on non-biological algal renewable fuels, detailed discussions on each project, and a Q&A session.</p> <p>The webinar was moderated by Pablo Morales Moya from Sustainable Innovations (SIE), and featured a presentation from Javier Sánchez López of CINEA, who discussed the agency’s role in supporting climate, infrastructure, and environmental initiatives. Following the introductory segments, the spotlight shifted to the project coordinators. Charis Xiros from RISE Research Institutes of Sweden presented the ALFAFUELS project, Sary Awad from IMT Atlantique showcased the COCPIT project, and Silvia Morales de la Rosa from CSIC presented the FUELGAE project. Each coordinator provided insights into their project’s objectives, impacts, and collaborative efforts.</p> <p>Participants had the opportunity to learn about groundbreaking renewable fuel solutions and their potential for carbon capture. The event underscored the importance of European collaboration in tackling environmental challenges through innovative research and development. Recordings of the session will be used for dissemination purposes, ensuring that the knowledge shared continues to benefit a wider audience interested in sustainable fuel technologies.</p>
Stellar Mass Black Hole Formation and Multimessenger Signals from Three-dimensional Rotating Core-collapse Supernova Simulations
<p>Gravitational waveforms from <a href="https://ui.adsabs.harvard.edu/abs/2021ApJ...914..140P/abstract">Pan et al. (2021) .</a></p> <p>They are the 40 solar mass model from Woosley & Heger 2007 with different<br>initial rotational speeds:</p> <p>Model NR: Omega_0 = 0.0 rad/sec<br>Model SR: Omega_0 = 0.5 rad/sec<br>Model FR: Omega_0 = 1.0 rad/sec</p> <p>/* File content */</p> <p>They are 5 files for each simulation.</p> <p>Files "data_s40_[model]_d3_[Cross/Plus][Equator/Pole].d" are GW strains for different<br>mode of polarization [h_plus or h_cross] and viewing angles [equator or pole].</p> <p>1st column is time [s] in postbounce. <br>2nd column s the GW strain, assuming d=10 kpc.<br> <br>Files "data_s40_[model]_d3_Idotdot.d" are the second time derivative of the<br>quadruple moments.</p> <p>1st: postbounce time [s] <br>2nd: Idd_xx [cgs]<br>3rd: Idd_xy = Idd_yx [cgs]<br>4th: Idd_yy = Idd_yy [cgs]<br>5th: Idd_zx = Idd_zx [cgs]<br>6th: Idd_zy = Idd_zy [cgs]<br>7th: Idd_zz = Idd_zz [cgs]</p> <p> </p>
Cows, Pigs and People: Example data of cubic insulin from three different species recorded on Diamond Light Source I24
<p>Data collected at 100K on 10th May 2024 at I24 (Diamond Light Source) to investigate automatic grouping of datasets containing very subtle differences. Crystals grown by Cicely Tam following standard techniques with coordination from Felicity Bertram. For each of bovine, porcine, and human insulin, 10 degree wedges are included. Insulin from these three sources differ by 1-3 amino acids, but are otherwise structurally isomorphous. </p> <p>The purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p> <p>Key:</p> <p>CIX - bovine insulin</p> <p>PIX - porcine insulin</p> <p>X - human insulin</p>
Data from: Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing data
<p>Data from the paper:</p> <p><em>Amos, S., Mengistu, S., Kleinschroth, F. (2021): Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing.</em></p> <p>Based on Landsat 5, 7, 8, RapidEye Ortho, and Sentinel-2 satellite imagery, we manually mapped the settlements of the Dasanech people in the most populated parts of the Omo River Delta in Ethiopia from 1992 to 2019 using QGIS. We used the data to answer the following questions: (1) How have pastoralist settlements in the delta changed in extent and persistence over the past three decades? And (2) how have the settlements changed structurally during the construction, filling, and operation of Gibe III Dam?</p> <p>We conducted two independent remote sensing analyses. Firstly, we used Landsat data from 1992 to 2019 to track land that is inhabited by pastoralists people within the evergreen part of the Delta. Secondly, the higher spatial resolution of the RapidEye Ortho (5m) and Sentinel-2 (10m) images allowed the detailed identification of settlements as well as infrastructure (tin-roof houses and road) in the Delta during a shorter period from 2009 to 2019. <strong>For more information on the data, please refer to the README.txt or the paper.</strong></p>
A Nu Supersymmetric Anomaly-free Atlas: anomaly-free, flavour-dependent U(1) charge assignments for the Minimally Supersymmetric Standard Model plus three Standard Model-singlet superfields
<p>We present lists of anomaly-free charge assignments up to a maximum magnitude charge Qmax=10 for the chiral fermionic content of the MSSM plus 3 right-handed neutrinos. </p> <p>Due to the large number of solutions, we compress the list into the file MSSMnuRcharges_Qmax10.gz. Please note that the unzipped file is approximately 130GB in size. We additionally include a smaller file, MSSMnuRcharges_Qmax4, containing the subset of anomaly-free charge assignments up to a maximum magnitude charge Qmax=4.</p> <p>The files searchU1MSSM.cpp and searchU1MSSM.h contain C++ files (in the 2014 standard) to produce the solutions. runsearch.sh is a bash script that compiles the programs and then runs it for a sample set of inputs.</p> <p>We provide Mathematica notebooks Analytic_solution_generator.nb and Analytic_Checks.nb which respectively provide the parametrisation of the analytic solution and checks thereof.</p> <p>The files beginning 'filter' contain example programs that read in each line in the solution list, apply a filter and print only the solutions satisfying the conditions of that filter. runfilter.sh is a bash script that compiles the filters and then runs a single filter as an example.</p> <p>These data and programs are based on this paper: https://arxiv.org/abs/2107.07926.</p>
Results of the Web-Delphi process to INAMI stakeholders (three rounds)
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 3 (Testing the framework with empirical applications), Results of the Web-Delphi process to INAMI stakeholders (three rounds), about the views of stakeholders regarding “The relevance of the following value aspects for the evaluation of new medicines in this disease context is” (2020)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 3, Deliverable 7.3 (Testing the framework with empirical applications), Testing the IMPACT-HTA Value Framework in collaboration with HTA agencies: Case studies on Non-Small Cell Lung Cancer and Spinal Muscular Atrophy (2021), Aris Angelis (LSHTM, LSE), Mónica Oliveira (IST), Teresa Rodrigues (IST), Liliana Freitas (IST), Carlos Bana e Costa (IST), Panos Kanavos (LSE)</p>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>Those are movies of numerical supernova models, which appear in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
Data for Rasch analysis of the three Field Practice Experiences Scales
<p>Data for Rasch analysis of the three Field Practice Experiences Scales. Data rare from Danish teacher education program students. Contains the following variables:</p> <p>Variables O1 to O12 are the items for the Observed scale</p> <p>Variables P1 to P12 are the items for the Practised scale</p> <p>Variables F1 to F12 are the items to the Received feedback scale</p> <p>Gender: 1 = female, 2 = male</p> <p>Campus: 1 = campus A, 2 = campus B</p> <p>T_Progr (teacher education program): 1 = regular, 2 = other</p> <p>P_level (level of latest field practice placement): 1 = 3rd level, 2 = 2nd level, 3 = 1st level</p> <p>Age: 1 = 24 years and younger, 2 = 25 years and older</p>
Data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler.
<p>This file system package contains data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler. It presents three quantum chemically calculated data sets ("MP2", "DMol", and "ZBL pair-specific") for diatomic interatomic potentials in the repulsive region, where the separation of the atoms is so short that the potential energy is >> 10 eV. The set also contains the fitted parameters for analytical NLH repulsive potentials that consist of a Coulomb term multiplied by a three-exponential screening function.</p> <p>The version from Oct 9, 2025 has updated NLH parameters for the pair Na-O (Z1=8, Z2=11). Otherwise the data is identical to before.</p> <p>The MP2 data sets contain potential data for all elements pairs Z1+Z2<=36, and the DMol, ZBL pair-specific and NLH potentials are proved for all elements pairs Z1, Z2 <=92.</p> <p>The data sets are arranged in the following directories:</p> <p>mp2/ : Data sets for the Hartree-Fock Moller-Plesset2 level calculations</p> <p>dmol/ : Data sets for the Density Functional Theory calculations with the DMOL code</p> <p>nlh/ : Coefficients for the fits of the Nordlund-Lehtola-Hobler potential to the DMol data sets</p> <p>zbl/ : Data sets for the pair-specific Ziegler-Biersack-Littmark potential calculations</p> <p>zbluniv/ : A directory with a Linux bash/awk script that generates the ZBL universal potential.</p> <p>zblspec/ : Coefficients for the pair-specific ZBL screening functions as contained in SRIM-2013.</p> <p>Each subdirectory has its own README.txt file giving additional details on the content of the directory and its subdirectories.</p>
Database of Planar and Three-Dimensional Periodic Orbits and Families Near the Moon
<p>The lunarPOdatabase.zip is the digital database accompanying the paper:<br> <br> C. Franz and R. P. Russell, “Database of planar and three-dimensional periodic orbits and families near the Moon,” The Journal of the Astronautical Sciences, DOI 10.1007/s40295-022-00361-9 (accepted Nov. 2022).</p> <p>Please see the paper for details, and cite the paper as appropriate. The database is accessible and permanently archived with the following DOI <a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.6411980&data=05%7C01%7C%7C1770e232817c4c99fc1b08dad0a0e3ad%7C31d7e2a5bdd8414e9e97bea998ebdfe1%7C0%7C0%7C638051686701542157%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Spb%2FFj5YL8QKaBoojr09MCCIdloAkzLiGKHoUo3uVGE%3D&reserved=0">https://doi.org/10.5281/zenodo.6411980</a>. See accompanying license.txt and gpl-3.0.txt for license information, applying to all files included in the .zip distribution.</p> <p>The database contains over 13 million planar and three-dimensional solutions in the Earth-Moon circular restricted three body problem, grouped into 34,000 family and sub-family clusters. The database exists as human readable text files with periodic orbits organized by clusters and other dynamical characteristics. The database contains the clustered data, a README file describing the output format, an interactive GUI, and a simple MATLAB script as a basic interface with the database. The data are split into five files, one for each of the planar prograde, planar retrograde, axial prograde, axial retrograde, and x-z cases. The results (i.e. initial conditions and relevant dynamical parameters of each converged periodic orbit) are contained in a human-readable text file where each row is a new solution. The data are sorted by cluster and ordered inside the cluster to form a smooth curve. Summary files are included for both the grid search and the clustering for each run. The input parameters to the grid search software are also included with each case for reproducibility. File sizes range from approximately 1.1GB to 2.6GB, with a total uncompressed file size of 5.4GB and a total compressed file size of 1.2GB.</p> <p>It is emphasized that the GUI and other MATLAB interface files are only a preliminary capability to ease interaction with the database. They may not be stable under future releases of MATLAB. On the initial use of the GUI, we recommend to restrict the data to a single value of N (say N=1 or N=16), otherwise the number of solutions may overwhelm the system memory. If a user has difficulties using the GUI, the user is encouraged to use the MATLAB code interfaces or interface with the text files directly. The text files containing the database are the primary product provided here, with the GUI and test scripts provided as a courtesy to help ease the database's use.</p> <p>Please send questions to <a href="mailto:cfranz21@gmail.com">cfranz21@gmail.com</a> and/or <a href="mailto:ryan.russell@utexas.edu">ryan.russell@utexas.edu</a>.</p>
Three-way model differencing and conflict detection
<p>We have further enhanced our practice, DSMCompare, to detect semantic differences and conflicts based on a three-way domain-specific comparison approach.</p> <p>To evaluate our practice, using a novel technique; we first used a reverse engineering method to transform the minimal collection of conflicting files of source codes involved in each commit into a three model for 95 conflicting commits over 13 open source GitHub projects and then calculated a high precision and recall compared to RefConfMiner's result.<br> The repository contains the results of the evaluation including the common ancestor model, left version mode, right version model, and the results of semantic comparison and conflict detection for each commit.</p> <p>The source code and relevant artifacts such as meta-models are available at:<br> https://github.com/geodes-sms/DSMCompare/</p>
Doppler spectra collected by a transect of three MRR-PRO during the POPE 2020 campaign at Princess Elisabeth Antarctica
<p>This repository contain the datasets of Doppler spectra collected by three K-band Doppler profiling radars (MRR-PRO) deployed in a transect across the Sør Rondane Mountains, in the vicinity of the Belgian research base Princess Elisabeth Antarctica (PEA).</p> <p>The measurement campaign has been conducted by the Environmental Remote Sensing Laboratory (LTE) of the École Polytechnique Fédérale de Lausanne (EPFL), with the logistical support of the International Polar Foundation (IPF).</p> <p>The datasets are described in the article “Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica”, by Alfonso Ferrone and Alexis Berne. The article was submitted to Earth System Science Data in August 2022, and is available at the following URL: <a href="https://doi.org/10.5194/essd-2022-295">https://doi.org/10.5194/essd-2022-295</a> .</p> <p>This repository complements "Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica", uploaded on Zenodo at: <a href="https://doi.org/10.5281/zenodo.7428690">https://doi.org/10.5281/zenodo.7428690</a> . The radar variables in the MRR-PRO data files contained in the latter have been computed from the raw spectra stored in the current repository.</p> <p> </p> <p><strong>Content of the archives</strong></p> <p>- <em>MRR_PRO_23_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 23, deployed at the lowest altitude in the transect, at1543 m above mean sea level (a.m.s.l.), at the following coordinates: latitude 72° 6’ 50.4” S, longitude 23° 30’ 50.4” E.</p> <p>- <em>MRR_PRO_06_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 06, deployed at approximately 2000 m a.m.s.l. of altitude, at the following coordinates: latitude 72° 7’ 4.8” S, longitude 23° 20’ 49.2” E.</p> <p>- <em>MRR_PRO_22_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 22, deployed at the highest location in the transect (2360 m a.m.s.l.), at the following coordinates: latitude 72° 13’ 37.2” S, longitude 23° 11’ 27.6” E.</p> <p> </p> <p><strong>Content of the NetCDF4 files</strong></p> <p>A “short name” is associated to each variables in the NetCDF4 files stored in the three archives.</p> <p>The relevant variables in each data file are:<br> – the raw spectral power, identified in the files by the short name “spectrum_raw”;<br> – the transfer function, used to convert the raw spectral power to spectral reflectivity, as described in Ferrone et al. (2022), and identified in the files by the short name “transfer_function”.</p> <p> </p> <p><strong>References</strong></p> <p>Ferrone, A., Billault-Roux, A.-C., and Berne, A.: ERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO), Atmospheric Measurement Techniques, 15, 3569–3592, https://doi.org/10.5194/amt-15-3569-2022, 2022</p> <p>Ferrone, A., and Berne, A., Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7428690, 2023</p>
Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes
<p><strong>Background</strong></p> <p>Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.</p> <p>A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).</p> <p><a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a></p> <p><strong>Methodology</strong></p> <p>This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.</p> <p>This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).</p> <p><strong>Data description</strong></p> <p>The current data product covers seven data files.</p> <p><strong>(1) Agents' experienced activity plans</strong></p> <p>File name: 1_activity_plans.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>deso</p> </td> <td> <p>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X</p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work, home, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>dep_time</p> </td> <td> <p>Departure time in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>trav_time</p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:second</p> </td> </tr> <tr> <td> <p>trav_time_min</p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>act_start</p> </td> <td> <p>Start time of activity in minute (0-1439)</p> </td> <td> <p>Integer</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time</p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_end</p> </td> <td> <p>End time of activity in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day given by MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <p> </p> <p><strong>(2) Travel trajectories</strong></p> <p>File name: 2_input_zip</p> <p>Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time in second in a simulation day (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation<sup>2</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Nearest road link consistent with (5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>vehicle</p> </td> <td> <p>Vehicle ID identical to person</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><sup>2 </sup>One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)</p> <p> </p> <p><strong>(3) Overnight charger access</strong></p> <p>File name: 3_home_charger_access.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(4) Car fleet composition</strong></p> <p>File name: 4_car_fleet.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>income_class</p> </td> <td> <p>Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p>(<strong>5) Road network with slope information</strong></p> <p>File name: 5_road_network_with_slope.shp (5 files in total)</p> <table> <tbody> <tr> <td> <p>Column</p> </td> <td> <p>Description</p> </td> <td> <p>Data type</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>length</p> </td> <td> <p>The length of road link</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>freespeed</p> </td> <td> <p>Free speed</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>capacity</p> </td> <td> <p>Number of vehicles</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>permlanes</p> </td> <td> <p>Number of lanes</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>oneway</p> </td> <td> <p>Whether the segment is one-way (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>modes</p> </td> <td> <p>Transport mode (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link_id</p> </td> <td> <p>Link ID</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>from_node</p> </td> <td> <p>Start node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>to_node</p> </td> <td> <p>End node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>count</p> </td> <td> <p>Aggregated traffic (number of cars travelled per day)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope in percent from -6% to 6%</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>LINESTRING (SWEREF99TM)</p> </td> <td> <p>geometry</p> </td> <td> <p>meter</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(6) Simulation scenarios specifying the parameter sets</strong></p> <p>File name: 6_scenarios.txt</p> <table> <tbody> <tr> <td> <p><strong>Parameter set</strong></p> <p><strong>(paraset)</strong></p> </td> <td> <p><strong>Strategy 1</strong></p> </td> <td> <p><strong>Strategy 2</strong></p> </td> <td> <p><strong>Strategy 3</strong></p> </td> <td> <p><strong>Fast charging power (kW)</strong></p> </td> <td> <p><strong>Minimum parking time for charging (min)</strong></p> </td> <td> <p><strong>Intermediate charging power (kW)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(7) Time history of travel trajectories and charging of the simulated BEVs</strong></p> <p>File name: 7_output.zip</p> <p>Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>seq</p> </td> <td> <p>Sequence ID of time history by agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>purpose</p> </td> <td> <p>Valid for activities (home, work, school, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Link ID (link_id in File 5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>distance_driven</p> </td> <td> <p>Cumulative driven distance in the simulation day</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>energy_1</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_2</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_3</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>charger_1</p> </td> <td> <p>Power rating of the charger (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_2</p> </td> <td> <p>Power rating of the charger (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_3</p> </td> <td> <p>Power rating of the charger (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>soc_1</p> </td> <td> <p>State of charge (0-1, Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_2</p> </td> <td> <p>State of charge (0-1, Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_3</p> </td> <td> <p>State of charge (0-1, Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p>
Data from: Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation
<p>This dataset contains the original data used in the article:</p> <p>Harásek M., Klinkovská K. & Chytrý M. (2023) Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation. <em>Applied Vegetation Science</em>, 26, e12726. https://doi.org/10.1111/avsc.12726</p> <p>The data contain plant species composition data from resurveyed vegetation plots in southwestern and central Moravia (Czech Republic). The plots were first surveyed by Milan Chytrý in 1986–1991 (“old plots”) and resurveyed by Martin Harásek, under the supervision of Milan Chytrý, in 2018–2019 (“new plots”).</p> <p>Of the old plots, 86 were sampled between 26 June and 16 September and 8 in May. Their size ranged from 5 to 49 m<sup>2</sup> (mean 33 m<sup>2</sup>). These plots were subjectively selected at different sites to document maximum variation in species composition and environmental conditions of the grasslands and heathlands studied. In each plot, all vascular plant species were recorded, and their covers were estimated using the nine-grade Braun-Blanquet scale (van der Maarel 1979). Plot locations were recorded in the form of text descriptions. Geographic coordinates of approximate location were added for each plot prior to the resurvey by the original surveyor using georeferenced aerial photographs and various information recorded in the field during the first survey, including slope, aspect and elevation. Location uncertainty (mean = 139 m) was indicated as the possible distance of the actual location from the given coordinates.</p> <p>The resurvey was conducted between 4 June and 16 August. Care was taken to select the most likely location of the original plot based on the coordinates of the approximate location, the original site description, and the occurrence of the species recorded during the first survey. New plots always had the same plot size as in the original sampling. Each old plot was resurveyed using 1–3 new plots depending on the uncertainty of the location of the old plot. A total of 94 old plots were resurveyed at 47 sites with 153 new plots. Of these, 71 old plots at 32 sites were in current protected areas, while 23 old plots at 15 sites were outside protected areas. All new plots were located using GPS with a location uncertainty of approximately 5 m.</p> <p>For each old plot resurveyed with more than one new plot, the most similar new plot (based on Bray-Curtis dissimilarity in species composition) was selected, resulting in a dataset of 94 old and 94 new plots (“best-fit dataset”). To test the robustness of the results, we created another dataset (“validation dataset”) that included the least similar of the corresponding new plots for each old plot. This dataset also included the 94 old and 94 new plots. If the old plot was resurveyed using a single new plot, that new plot was included in both the best-fit and validation datasets.</p> <p>The header data structure follows that of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, fields are added to indicate whether the new plot was used in the best-fit dataset (Best_fit) or the validation dataset (Validation). The information about location within or outside the protected area is given in the field Protection.</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_SW_moravia_acidgrass.zip</strong>. For using this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and the species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) must be installed.</li> <li>Three TXT files with columns separated by tabs: <ul> <li><strong>SW_moravia_acidgrass_species.txt</strong> contains the percentage covers of plant species in the plots, which are mid-values for cover-abundance categories of the Braun-Blanquet scale. Plant nomenclature was harmonised according to Danihelka et al. (2012).</li> <li><strong>SW_moravia_acidgrass _head.txt</strong> contains information on the number of species in each plot (number_species), the number, proportion and relative cover of threatened species (IUCN categories CR, EN, VU, NT, columns CR_NT_number, CR_NT_perc_number and CR_NT_perc_cover), alien species (alien_number, alien_perc_number, alien_perc_cover), species characteristic of dry grasslands (TH_number, TH_perc_number, TH_perc_cover), sand and rock-outcrop grasslands (TF_number, TF_perc_number, TF_perc_cover), mesotrophic grasslands (TD_number, TD_perc_number, TD_perc_cover) and herbaceous ruderal vegetation (XA_XC_number, XA_XC _perc_number, XA_XC _perc_cover) and unweighted means of Ellenberg-type indicator values for light (light), temperature (temperature), moisture (moisture), soil reaction (reaction) nutrients (nutrients) and salinity (salinity) used to test changes in these variables through time.</li> <li><strong>SW_moravia_life_forms.txt </strong>contains information about the assignment of individual species to the life form, which was used to analyse changes in frequency and cover of the life forms.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (Knollová et al. 2023; <a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
Transcriptome assemblies of three diatom and three prymnesiophyte isolates from Station ALOHA and Kaneohe Bay
<p><strong>Culture ID/name</strong></p> <p>AT125A – Pseudo-nitzschia sp.</p> <p>AT125C – Pseudo-nitzschia sp.</p> <p>ATCH2 – Chaetoceros sp.</p> <p>Pn B2 – Pseudo-nitzschia sp.</p> <p>KB-HA01 – Chrysochromulina sp. (also called </p> <p>AL-TEMP-12 – Chrysochromulina sp. (also called </p> <p>NF-H275 – Chrysochromulina sp.<br> <br> </p> <p><strong>Growth Conditions</strong></p> <p>All cultures were grown at 27°C, 12:12 light:dark cycle, and with a light intensity of 100 µmol photons m<sup>-2</sup> sec<sup>-1</sup>. AT125C, AT125A, and Pn B2 were grown with Aquil media. ATCH2 was grown with F/20 media with the phosphate concentration modified to a final concentration of 0.5µM. KB-HA01 was grown with F/2 media and AL-TEMP-12 and NF-H275 were grown with K media. None of the cultures were axenic. All cultures were filtered in “light” and “dark” conditions and were in exponential phase when filtered. (Filter types and volumes filtered listed below.) After filtration, all filters were placed into 2mL screwcap tubes, flash frozen with liquid nitrogen, and stored at -80°C.</p> <p><strong>Growth Conditions</strong></p> <p>All cultures were grown at 27°C, 12:12 light:dark cycle, and with a light intensity of 100 µmol photons m<sup>-2</sup> sec<sup>-1</sup>. AT125C, AT125A, and Pn B2 were grown with Aquil media. ATCH2 was grown with F/20 media with the phosphate concentration modified to a final concentration of 0.5µM. KB-HA01 was grown with F/2 media and AL-TEMP-12 and NF-H275 were grown with K media. None of the cultures were axenic. All cultures were filtered in “light” and “dark” conditions and were in exponential phase when filtered. (Filter types and volumes filtered listed below.) After filtration, all filters were placed into 2mL screwcap tubes, flash frozen with liquid nitrogen, and stored at -80°C.<br> <br> [TRANSCRIPTOME SEQUENCING]<br> <br> [QC AND ASSEMBLY]<br> <br> [POST-ASSEMBLY PROCESSING]<br> Diamond v2.0.5.143 was used to blast (e-value: 1e-5) to a cross-kingdom reference sequence database (as described in Coesel et al., 2021). Diamond v2.0.5.143 was used to find the least common ancestor of each contig based upon the blast results. Contigs that were identified as bacteria, archaea, or viruses were excluded from the assemblies.<br> <br> </p>
Data and graphics from: "Three invariants of strange attractors derived through hypergeometric entropy"
<p>This package contains data and graphics related to the publication:</p><p>Keisuke Okamura, "Three invariants of chaotic attractors derived through hypergeometric entropy", Chaos, Solitons & Fractals 170 (2023) 113392; <a href="https://doi.org/10.1016/j.chaos.2023.113392">https://doi.org/10.1016/j.chaos.2023.113392</a></p><p> </p><p><strong>Abstract of the original article</strong></p><p>A new description of strange attractor systems through three geometrical and dynamical invariants is provided. They are the correlation dimension (\(\mathcal{D}\)) and the correlation entropy (\(\mathcal{K}\)), both having attracted attention over the past decades, and a new invariant called the correlation concentration (\(\mathcal{A}\)) introduced in the present study. The correlation concentration is defined as the normalised mean distance between the reconstruction vectors, evaluated by the underlying probability measure on the infinite-dimensional embedding space. These three invariants determine the scaling behaviour of the system's Rényi-type extended entropy, modelled by Kummer's confluent hypergeometric function, with respect to the gauge parameter (\(\rho\)) coupled to the distance between the reconstruction vectors. The entropy function reproduces the known scaling behaviours of \(\mathcal{D}\) and \(\mathcal{K}\) in the 'microscopic' limit \(\rho\to\infty\) while exhibiting a new scaling behaviour of \(\mathcal{A}\) in the other, 'macroscopic' limit \(\rho\to 0\). The three invariants are estimated simultaneously via nonlinear regression analysis without needing separate estimations for each invariant. The proposed method is verified through simulations in both discrete and continuous systems.</p><p> </p><p><strong>Data files</strong></p><p>The following files are provided (see the original article for the notation):</p><ul><li>The comma-separated values (CSV) file named 'est_sum_all.csv' summarises the results of the correlation dimension (\(\mathcal{D}\)), correlation entropy (\(\mathcal{K}\)) and correlation concentration (\(\mathcal{A}\)) estimates for two discrete (logistic and Hénon maps) and four continuous (Lorenz, Rössler, Duffing-Ueda and Langford attractors) chaotic systems in tabular form.</li><li>The six CSV files whose file names begin with 'H_' record the extended Rényi entropy values calculated for each \(\sigma\) value (in increments of 0.1) for the chaotic system whose name follows the prefix, for each embedding dimension (\(m\)). The column 'mN' shows the results for the embedding dimension \(N\).</li><li>The six PDF files whose file names begin with 'gr_' illustrate, for the named chaotic systems following their prefixes, their i) appearance (left), ii) graph of the extended Rényi entropy as a function of \(\sigma\) (centre) and iii) the chaotic invariants (\(\mathcal{D}, \mathcal{K}, \mathcal{A}\)) estimation results (right).</li></ul>
Three-component modelling of O-rich AGB star winds I. Effects of drift using forsterite – dataset
<p>The data provided here include all parameter files, log files, and a set of the<br> binary output files that are the basis for the publication in A&A.</p> <p>The file 'file_listing.txt' contains a complete list of files and directories<br> in all gzipped tar files. Each individual gzipped tar file is formatted as<br> follows:</p> <p> Mm.m_Ll.ll_Ttttt.tar.gz</p> <p>where<br> m.m :: the assumed mass of the model, in solar masses<br> l.ll :: The assumed luminosity, in log10(solar luminosities)<br> tttt :: The effective temperature of the star, in Kelvin.</p> <p><br> The contents of the tar files vary according to the model, but here is the<br> general directory structure:</p> <p> nodr/ :: non-drift / PC models<br> drift/ :: drift models</p> <p> nodr/init<br> drift/init :: Initial model files created using John Connor.</p> <p><br> File suffixes are the following:</p> <p> .par :: Plain-text parameter file that contains all parameters that are<br> different from the respective default value in the model.<br> Consequently, to see what parameters were actually used, it is<br> necessary to look in the log file (see below).</p> <p> .bin :: Binary file that contains output of converged models. Each model is<br> stored in two versions, first the previous time step and then the<br> current time step (having access to the model code T-800, data of both<br> time steps are needed to restart model calculations at that time<br> step).</p> <p> The initial model file only contains one model; where the previous<br> time step data are the same as the current time step data.</p> <p> We provide a tool to read this file, see below.</p> <p> Note! These files can get pretty large and are therefore only<br> available for a smaller number of the models in the Zenodo dataset.<br> Please ask the corresponding author for the missing files is the<br> need should appear.</p> <p> .log :: Plain-text log file that shows the used model parameters and a number<br> of key properties for each converged model.<br> The encoding of this file is UTF-8.</p> <p> .inf :: Plain-text secondary log file that contains the header of the<br> [primary] log file as well as timing information.<br> The encoding of this file is UTF-8.</p> <p> .tpb :: Secondary binary file that contains a number of properties specified<br> at the outer boundary, typically for each consecutive time step.</p> <p> We provide a tool to read this file, see below.</p> <p> .lis :: Plain-text file with the iteration history. Unavailable here.</p> <p> .liv :: Plain-text file with values specified for a number of properties at<br> each gridpoint. Unavailable here.</p> <p> .inp :: Plain-text file that is used to launch a model; some are present.<br> This file is automatically generated by the tool that launches T-800<br> and is typically removed when T-800 launches. Unavailable here.</p> <p> .eps :: Encapsulated PostScript file created by John Connor when calculating<br> the initial model.</p> <p><br> Model evolution structure - file endings before the suffix:</p> <p> _rlx :: Files related to relaxing the T-800 calculations on the initial model<br> created by John Connor.</p> <p> _exp :: Files related to expanding the initially compact model to using the<br> full radial domain.</p> <p> _fix :: Files related to the intermediate stage where calculations are changed<br> from expansion to outflow.<br> <br> _out :: Files related to the outflow stage of the calculations; this is what<br> you want to look at to see the wind evolution. Results in the paper<br> are calculated using these data.</p> <p> Note! Some outflow stage calculations continue the evolution of the previous<br> set of files. The underlying reason for continued calculations is typically<br> that the calculated time interval is too short. Such files are typically<br> given the extension '_cont.lin_out', '_cont2.lin_out', etc.</p> <p><br> Stored data in the binary files:</p> <p> The binary files (suffix '.bin') contain the full radial structure in the<br> following 10 (PC models) or 11 (drift models) primary variables:</p> <p> mr: radius<br> mm: integrated [gas] mass<br> md: gas density<br> mu: gas velocity<br> me: internal energy<br> mj: radiative energy<br> mh: radiative flux<br> n0: dust moment, forsterite (Fo)<br> nm: number density of magnesium atoms<br> ns: number density of silicon atoms<br> v0: dust velocity, forsterite (only drift models)</p> <p> Other properties are derived from these primary variables using auxiliary code<br> that isn't part of this dataset.</p> <p><br> Load files:</p> <p> Two tools are provided here that can load the binary data files using the<br> Interactive Data Language (IDL):</p> <p> sc_load_bin (for files with the suffix '.bin'):</p> <p> Loads the full content of a T-800 binary file and returns a structure<br> with the data.</p> <p><br> sc_load_tpb (for files with the suffix '.tpb'):</p> <p> Loads the full content of a T-800 'tpb' binary file and returns a<br> structure with the data.</p> <p> Note! Due to the way models run on clusters, this file is sometimes<br> incomplete; this happens when the model code T-800 is stopped as the<br> cluster-specific walltime is reached. If this is the case, it is<br> necessary to use the binary file instead, where data are saved<br> typically every 20:th time step.</p> <p> Alternative tools for use with Python and Julia could be considered for<br> writing, but where not yet available when this dataset was made public.<br> Please contact the corresponding author for a current status on this issue.</p> <p> </p>
Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.</p> <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul> <p><br> </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.