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1,855 results for “framework”
Analytical Framework for Precise Relative Motion in Low Earth Orbits
<p>The data sets provided here can be used to recreate the plots of the paper “Analytical Framework for Precise Relative Motion in Low Earth Orbits” available at this <a href="https://arc.aiaa.org/doi/10.2514/1.G004716">link</a>.</p> <p>That paper presents a practical and efficient analytical framework for the precise modelling of the relative motion in low Earth orbits.</p>
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2008 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2009 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Trees and alignments for: A robust phylogenomic framework for the calamoid palms
<p>Target file, alignments, gene trees and species trees from phylogenomic analyses in Kuhnhäuser et al. (2021), A robust phylogenomic framework for the calamoid palms, Molecular Phylogenetics and Evolution. <a href="https://doi.org/10.1016/j.ympev.2020.107067">https://doi.org/10.1016/j.ympev.2020.107067</a>.</p> <p>Raw sequence data are deposited in the European Nucleotide Archive of the European Bioinformatics Institute (<a href="https://www.ebi.ac.uk/ena">https://www.ebi.ac.uk/ena</a>) under project number PRJEB40689. Scripts for all phylogenetic analyses are available at <a href="https://github.com/BenKuhnhaeuser/PhyloFrame">https://github.com/BenKuhnhaeuser/PhyloFrame</a>.</p>
Dataset of "Tuning the morphology and energy levels in organic solar cells with metal- organic framework nanosheets"
<p>Metal-organic framework nanosheets (MONs) have proved themselves to be useful<br>additives for enhancing the performance of a variety of thin film solar cell devices. However,<br>to date only isolated examples have been reported. In this work we take advantage of the<br>modular structure of MONs in order to resolve the effect of their different structural and<br>optoelectronic features on the performance of organic photovoltaic (OPV) devices. Three<br>different MONs were synthesized using different combinations of two porphyrin-based ligands<br>meso-tetracarboxyphenyl porphyrin (TCPP) or tetrapyridyl-porphyrin (TPyP) with either zinc<br>and/or copper ions and the effect of their addition to polythiophene-fullerene (P3HT-PCBM)<br>OPV devices was investigated. The power conversion efficiency (PCE) of devices was found to<br>approximately double with the addition of MONs of Zn2(ZnTCPP), but was unchanged with<br>the addition of Cu2(ZnTPyP) and halved upon the addition of Cu2(CuTCPP) compared to<br>devices without nanosheets. Our analysis indicates that there are three different mechanisms<br>by which MONs can influence the photoactive layer – light absorption, energy level alignment,<br>and morphological changes. Analysis of external quantum efficiency, UV-vis photoelectron<br>spectroscopy data found that MONs have similar effects on light absorption and energy level<br>alignment. However, atomic force and Raman microscopy studies revealed that the nanosheet<br>thickness and lateral size are crucial parameters in enabling the MONs to act as beneficial<br>additives resulting in an improvement of the OPV device performance. We anticipate this<br>study will aid in the design of MONs and other 2D materials for future use in other light<br>harvesting and emitting devices.</p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
Dataset related to the manuscript: "An open-source integrated framework for the automation of citation collection and screening in systematic reviews"
<p>Dataset related to the manuscript: “An open-source integrated framework for the automation of citation collection and screening in systematic reviews”, to be used together with the code stored at https://github.com/AD-Papers-Material/BART_SystReviewClassifier to reproduce the results.</p> <p>There are three datasets:<br> - The Record data collected from the online scientific databases;<br> - The session journal which describes the search session, i.e., how many records were collected and from which source, for each query/session pairs.<br> - The session data which is the outcome of the classification and review tasks;</p>
Sample data for "A weakly supervised framework for high resolution crop yield forecasts"
<p>This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled <em>A weakly supervised framework for high resolution crop yield forecasts</em>, accessible at </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2205.09016">https://doi.org/10.48550/arXiv.2205.09016</a></td> </tr> </tbody> </table> <p> </p> <p>The updated paper (including results from the US) is published in Environmental Research Letters:</p> <p><a href="https://doi.org/10.1088/1748-9326/acf50e">https://doi.org/10.1088/1748-9326/acf50e</a></p> <p> </p> <p>The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup.</p> <p> </p> <p>Data</p> <p>1. County data (county-data.zip) for county-level strongly supervised models:</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p> <p>* CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha<sup>-1</sup>), total weight of storage organs (kg ha<sup>-1</sup>), development stage (0-2). Source: de Wit et al. (2022).</p> <p>* METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022).</p> <p>* REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020).</p> <p>* SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p> </p> <p>2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above).</p> <p>* METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above).</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above).</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above).</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021), Lobell et al. (2020).</p> <p> </p> <p>3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level.</p> <p>* METEO_GRIDs_US.csv: Meteo indicators at 10km grid level.</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level.</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level.</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p>
Robust framework and software implementation for fast speciation mapping
<p>R script and raw data to test the sparse excitation energy XAS procedure.</p>
Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset
<p>This dataset contains data used in the research published by MLabs Optronics in the paper:</p> <p>Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018.</p> <p><br> The dataset is classified into 3 folders:</p> <p>- IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.<br> <br> - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS".</p> <p>- VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).</p> <p> </p> <p>Mlabs Optronics<br> PTA<br> Calle Pierre Laffitte, 8<br> 29590 Málaga (Spain)</p> <p>www.mlabsoptronics.com<br> info@mlabsoptronics.com</p>
Data and R code for Tansley review New Phytologist 2021: "An integrated framework of plant form and function: The belowground perspective"
<p>The files in this archive are related to the paper of Weigelt, Mommer, Andraczek et al. (2021) An integrated framework of plant form and function: The belowground perspective. Tansley Review New Phytologist. The paper developed and tested a new conceptual framework of plant form and function linking above and belowground traits of 2510 species. We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth.</p> <p>This archives contains four files:</p> <ol> <li><strong>Weigelt et al.2021RCode.DataCleaning.txt</strong> - RCode for the complete data processing starting with the downloaded database files from the Plant Trait Database version 5.0 (TRY, Kattge et al. 2020), the Global Root Trait database (GRooT, Guerrero-Ramirez et al. 2020) and a small number of additional data files listed in Table S2 of the original paper. Additional information was later incorporated using FungalRoot Database (Soudzilovkaia et al. 2020), nodDB Database (Tedersoo et al. 2018) and a compiled dataset on rooting depth (Fan et al. 2017). The code processes, cleans and merges the data and produces a final table for PCA analysis of species specific mean traits. This final table is provided as a second file in this archive (Weigelt_et_al_2021_Main.PCA.Matrix.xlsx). A second part of the RCode.DataCleaning extracts species-specific individual trait data where root and shoot traits were measured on the same plant individual or plot. This data was compiled from 43 studies identified in Table S2 of the original publication. The final table for individual trait data is the third file in this archive (Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx).</li> <li><strong>Weigelt_et_al_2021_Main.PCA.Matrix.xlsx</strong> – Datafile with species-specific global mean trait data for 17 traits of 2510 species with at least one root and one shoot trait available. Meta-data is provided in the data file.</li> <li><strong>Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx</strong> – Datafile with species-specific trait data where root and shoot traits were measured on the same individual or plot for 6 traits of 455 species. Meta-data is provided in the data file.</li> <li><strong>Weigelt et al.2021RCode.Analysis.txt – </strong>RCode for all analyses and figures provided in the paper for both the species mean and individual based dataset. The Code is annotated to help reproducibility of the analysis.</li> </ol>
Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework
<p>This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play. </p> <p>In this data set, <strong>team level multi-modal behavioral data</strong> is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below: </p> <ul> <li><em>condition:</em> This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li><em>Error: </em>This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li><em>Learning Gain: </em>It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li><em>Usefulness Score: </em>The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li><em>PE Score: </em>It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li><em>Right_Suggestions: </em>This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li><em>Right_Time: </em>This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li><em>Exploration:</em> This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li><em>Reflection: </em>This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li><em>Communication: </em>This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li><em>LG_status: </em>This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> <p>This dataset corresponds to the publication <em><strong>"Social robots as skilled ignorant peers for supporting learning"</strong></em>: <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a></p> <p> </p>
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p><p>This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Potentials (NNP and ΔNNP model), scripts, and all energy and force data used in this work at the (Δ)NNP, ReaxFF, and DFT (SCAN+D3(BJ) and ωB97X-D3(BJ)) level. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOH.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.</p><ol><li>"aimd_simulations.zip" - VASP INCAR file, XDATCAR and traj file for 10 ps AIMD run (Supplementary Figure 6) and NNP level (re-)calculated energies/forces ("aimd_nnp_recalc.traj")</li><li>"biased_dynamics.zip" - VASP/Plumed input and output files for DFT (SCAN+D3(BJ)) and NNP level biased dynamics including traj files (Supplementary Figure 12)</li><li>"database_input.zip" - structure (cif) files of the initial structures used for database generation (Supplementary Table 1)</li><li>"delta_nnp.zip" - (pytorch) ΔNNP model (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>) together with example scripts </li><li>"error_stats.zip" - traj files of all generalization tests (Figure 1 and Supplementary Figure 4) storing energies/forces at the SCAN+D3(BJ), ReaxFF, and NNP level as well as traj files with ΔNNP and ωB97X-D3(BJ) energies/forces for a subset taken from biased dynamics runs (Supplementary Figure 11)</li><li>"md_simulations.zip" - NNP level MD trajectories of all generalization test (Figure 1 and Supplementary Figure 4) runs including an example script for an MD run</li><li>"neb_calculations.zip" - traj files and example scripts for NEB calculations at the (Δ)NNP along with the corresponding DFT energy/force data (SCAN+D3(BJ) and ωB97X-D3(BJ))</li><li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li><li>"silica_database.zip" - output files of the single-point (SP) and optimization test runs (Supplementary Figure 1) of pure silica structures together with an example structure optimization script </li><li>"SiAlOH.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li></ol>
Sound-Frameworks: Kresten Bjorn Krab-Bjerre (Bang & Olufsen) - Interview
<p><span>This is a transcription of an interview that took place within the project Sound-Frameworks: Collaborative Frameworks for Integrating Sound Within Urban Design and Planning Processes.</span></p> <p><span>Sound-Frameworks is an action-led research project that explores the role of sound in urban design and city planning. The project is led by the artist and researcher Dr. Sven Anderson and hosted by Theatrum Mundi, a non-profit organisation that expands the craft of city-making through collaboration with artists.</span></p> <p><span>Central to this project is a focus on how urban sonic experience can serve as a driver for design in the public realm. The public realm constitutes the integral connective tissue that defines the contemporary cityscape, within which different individuals, communities and institutions engage with each other through cooperation and conflict. Sound has remained a neglected dimension of this domain, generally coming into consideration only within late stages of design through efforts to ameliorate the impact of environmental noise. As the densification of urban territories accelerates, the role of sonic experience as an essential factor to be addressed by urban designers must be reassessed.</span></p> <p><span>Sound-Frameworks explores new methodologies for integrating sound in urban design through the production of three inter-related resources:</span></p> <p><span>1. A sound in practice survey<br>2. A publication on best practice guidelines in this field<br>3. An online tool to guide the integration of sound in the design of the public realm</span></p> <p><span>Drawing from environmental acoustics, spatial planning, contemporary sound studies, the project initiates a framework to extend regional, national and international objectives for integrated city planning and contribute to public realm initiatives in Europe and beyond.</span></p> <p><span>Sound-Frameworks has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101032632.</span></p> <p><span>Sound-Frameworks is hosted by Theatrum Mundi (UK) and supported by partnerships with Arup (UK), UrbanIdentity (CH), Struer Kommune (DK), the University of Oxford Faculty of Music (UK) and the Sound Studies Lab at the University of Copenhagen (DK).<span> </span></span></p> <p><span>Sound-Frameworks: <a href="https://www.soundframeworks.org/"><span>https://www.soundframeworks.org</span></a><br>Theatrum Mundi: <a href="https://www.theatrum-mundi.org/"><span>https://www.theatrum-mundi.org</span></a><br>Sven Anderson: <a href="https://www.svenanderson.net/"><span>https://www.svenanderson.net</span></a></span></p>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Results of Survey on Playertypes by Gamification User Types Hexad Framework in Higher Education
<p>Survey on playertypes via the validated quesitonaire published in Krath, J., von Korflesch, H.F.O. (2021). Player Types and Game Element Preferences: Investigating the Relationship with the Gamification User Types HEXAD Scale. In: Fang, X. (eds) HCI in Games: Experience Design and Game Mechanics. HCII 2021. Lecture Notes in Computer Science(), vol 12789. Springer, Cham. https://doi.org/10.1007/978-3-030-77277-2_18</p> <p>Between 25.01.23 and 08.02.23 students of the University of Lübeck, Germany were invited to fill out an online questionnaire. The acquisition was done by sending an email to the students. No incentive was offered for participation, except to find out at one's own expression at the end of the survey. In addition to the validated questions, this also included questions about gender and study area. All participants agreed to anonymous data collection and publication. Participants were also asked to confirm that they were completing the survey for the first time, otherwise the return was removed from the result set. </p> <p>The result set is formatted as CSV. Questions and identifiers of the data are shown in the first line.</p>
A Bayesian Machine Learning Framework for Animal Telemetry Data
<p>The data and tutorial in this repository are intended to be used in conjunction with the tutorial with our manuscript titled "A Bayesian Machine Learning Framework for Animal Telemetry Data." Telemetry data for three lesser prairie-chickens are provided here as .csv files. For more information about the data, please refer to our manuscript or contact Andrew Whetten or David Haukos for more information.</p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
Analysis of two Methods for Aircraft Fuel Requirement Calculations in the Context of a novel Methodological Framework for LCA of Sustainable Aviation
<p>This Microsoft Excel file contains equations to compare different approaches to calculate fuel efficiency ("energy use" in [MJ/t*km]) of aircraft over a specific distance at a specific payload. </p> <p>Two approaches are compared: A novel approach by <a href="10.1016/j.scitotenv.2023.163881" target="_blank" rel="noopener">Su-ungkavatin et al.</a> and the more established approach well documented by eg. <a href="https://www.fzt.haw-hamburg.de/pers/Scholz/arbeiten/TextBurzlaff.pdf" target="_blank" rel="noopener">Burzlaff</a> or <a href="http://www.aircraftmonitor.com/uploads/1/5/9/9/15993320/aircraft_payload_range_analysis_for_financiers___v2.pdf" target="_blank" rel="noopener">Ackert</a>.</p> <p>This work augments a Letter to the Editor we submitted to the journal <a href="https://www.sciencedirect.com/journal/science-of-the-total-environment" target="_blank" rel="noopener">Science of the Total Environment</a>.</p>
Quality-Assurance Package for the "Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework" Manuscript
<h2>Background</h2> <p>Neuroimaging research requires consistent image quality and temporal signal stability, especially for functional magnetic resonance imaging (MRI) studies that rely on detecting subtle blood-oxygen-level-dependent (BOLD) signal changes. Regular MR system performance monitoring is essential, especially for longitudinal and multi-site studies. This study aims to establish a robust quality assurance (QA) protocol to promote data comparability across scanner models, vendors, and sites, as well as over a prolonged period.</p> <p>The manuscript titled "<em>Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework</em>" was submitted to the Special Issue <a href="https://link.springer.com/journal/10334/updates/26638300">Reproducibility and Quality Assurance</a> of the Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA) journal.</p> <p>This QA package proposed by the manuscript hosts materials for</p> <ul> <li>all reconstructed images,</li> <li>instruction for data acquisition,</li> <li>instruction for image reconstruction,</li> <li>instruction for post-processing,</li> <li>example raw data and DICOM images, and</li> <li>images and scripts for T1/T2 fitting.</li> </ul> <p>The detailed information is listed below.</p> <h2>All reconstructed images</h2> <p>This directory contains all reconstructed images from the fBIRN phantom on three Siemens 3T scanners (Trio, Prisma.Fit, and Cima.X) and one GE (UHP) 3T scanner. It contains four sub-folders for each scanner. And each sub-folder contains (some of) the following sub-folders:</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product spin-echo (SE) images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h2>Instruction for data acquisition</h2> <p>This directory includes the following documents:</p> <ul> <li><code>write_QA_Tran_EPIrs.m</code> to generate the <code>QA_epi.seq</code> file for EPI scans.</li> <li><code>write_QA_Tran_T1.m</code>: to generate the <code>QA_T1.seq</code> file for SE scans.</li> <li><code>20241122_QA_protocol_instruction_siemens.docx</code>: standard operating procedure for QA measurements.</li> <li><code>QA_record.xlsx</code>: Excel sheet for the record of QA measurements.</li> </ul> <h2>Instruction for image reconstruction</h2> <h3><em>Documents</em></h3> <ul> <li><code>pulseq2mrd_epi.m</code>: convert GE Pulseq EPI raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_epi.seq</code> file.</li> <li><code>pulseq2mrd_se.m</code>: convert GE Pulseq SE raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_T1.seq</code> file.</li> <li><code>siemens2mrd_epi.m</code>: convert Siemens Pulseq EPI raw data (<code>.dat</code>) to MRD raw data (<code>.h5</code>) using the information in the <code>.dat</code> raw data.</li> </ul> <ul> <li><code>default.xml</code>: Gadgetron configuration file for SE image reconstruction. This document is already in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/default.xml</code>.</li> <li><code>qc_epi.xml</code>: Gadgetron configuration file for EPI image reconstruction, which is modified from the <code>default epi.xml</code> located in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/</code>.</li> </ul> <ul> <li><code>specialCard_ICE.png</code>: Special card setting for ICE online reconstruction.</li> </ul> <h3><em>Procedures for Gadgetron offline reconstruction</em></h3> <p><strong>Step 1: Gadgetron installation (for more details, visit <a href="https://gadgetron.github.io/tutorial/">here</a>)</strong></p> <ul> <li>Download and install <a href="https://www.docker.com/">Docker</a> software. You may need to install/update the Windows Sub Linux (WSL) system for the Docker installation.</li> <li>Open your terminal (Power shell with administrative privilege in Windows) and navigate to the folder you would like to map to the Gadgetron Docker container.</li> <li>Run: <code>docker run -t --name gt_latest --detach --volume ${pwd}:/opt/data ghcr.io/gadgetron/gadgetron/gadgetron_ubuntu_rt_nocuda:latest</code>. If docker is not recognized, set <code>docker</code> to connect to <code>C:\Program Files\Docker\Docker\resources\bin</code> in the Environment Path in Windows. This will download and then launch the <a href="https://gadgetron.readthedocs.io/en/latest/building.html">latest Gadgetron version</a> in a Docker container. It will also mount your current folder as a data folder inside the container.</li> <li>Run this command: <code>docker exec -ti gt_latest /bin/bash</code>. This will execute your Gadgetron container.</li> </ul> <p><strong>Step 2: Data preparation</strong></p> <ul> <li>Place your SE/EPI <code>.dat</code>/<code>.h5</code> data in the mounted folder.</li> <li>Run the command in Terminal: <code>cd /opt/data</code> to enter the mounted folder.</li> </ul> <p><strong>Step 3: MRD conversion</strong></p> <ul> <li>For Siemens data, you can convert the <code>.dat</code> data to MRD data by using Gadgetron. If Gsdgetron doesn't work (e.g. for XA EPI data), you can then use the Matlab script <code>siemens2mrd_epi.m</code>.</li> <li>The command for Siemens SE data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -o se_data.h5</code>.</li> <li>The command for Siemens EPI data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -m IsmrmrdParameterMap_Siemens.xml -x IsmrmrdParameterMap_Siemens_EPI.xsl -o epi_data.h5</code>.</li> <li>For GE data, you can convert the <code>.mat</code> raw data to MRD data by using the Matlab scripts with the corresponding <code>.seq</code> files. For SE conversion: use <code>pulseq2mrd_se.m</code> with <code>QA_T1.seq</code>. For EPI conversion: use <code>pulseq2mrd_epi.m</code> with <code>QA_epi.seq</code>.</li> </ul> <p><strong>Step 4: Gadgetron reconstruction</strong></p> <ul> <li>SE reconstruction: <code>gadgetron_ismrmrd_client -f se_data.h5 -c default.xml -o se_out.h5</code>.</li> <li>EPI reconstruction: first, put <code>qc_epi.xml</code> to the mounted folder and then copy it to the Gadgetron container: <code>cp /opt/data/qc_epi.xml /opt/conda/envs/gadgetron/share/gadgetron/config/</code>. Then, run the reconstruction: <code>gadgetron_ismrmrd_client -f epi_data.h5 -c qc_epi.xml -o epi_out.h5</code>.</li> </ul> <p><strong>Step 5: Load Gadgetron-reconstructed images (<code>.h5</code>)</strong></p> <ul> <li>Load SE <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_se_out.h5' ;</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_se_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_se_im = reshape(pulseq_se_im, [256, 256, 11, 2]) ;</p> </blockquote> <ul> <li>Load EPI <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_epi_out.h5';</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_epi_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_epi_im = reshape(pulseq_epi_im, [64, 64, 27, 200]) ;</p> </blockquote> <h3><em>Procedures for ICE online reconstruction</em></h3> <p>Before executing the Pulseq-based sequences, you can enable ICE online Reconstruction following the procedures below:</p> <ul> <li>Navigate to the Special Card (<code>specialCard_ICE.png</code>), set <code>Data handling</code> to <code>ICE STD</code> for NUMARIS/X (e.g. XA60A and XA61A), and <code>ICE 2D</code> for NUMARIS/4 (e.g. VB, VD, and VE).</li> <li>Select <code>Sum-of-Square</code> for coil combination.</li> <li>Be sure that the maximal pixel intensity does not violate the intensity threshold of <strong>4096</strong>.</li> </ul> <h2>Instruction for post-processing</h2> <p>The example post-processing is based on the reconstructed images from Cima.X over five days.</p> <h3><em>Reconstructed images from Cima.X</em></h3> <p><strong>Note</strong>: All <code>se</code> folders contain a <code>structuralQuality_main.m</code> to call the <code>structuralQuality.m</code> function for structural quality analysis. All <code>epi</code> folders contain a <code>temporalQuality_main.m</code> to call the <code>temporalQuality.m</code> function for temporal quality analysis.</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product SE images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h3><em>QA analysis Matlab package: </em><code><em>QA_functions</em></code></h3> <ul> <li><code>circfit.m</code>: to find the center point and radius of the phantom.</li> <li><code>makeCircleMask.m</code>: to make a circular mask based on the center point and radius.</li> <li><code>structuralQuality.m</code>: to analyze the structural quality of the SE images.</li> <li><code>temporalQuality.m</code>: to analyze the temporal quality of the EPI images.</li> </ul> <h3><em>Post-processing procedures</em></h3> <ul> <li>Step 1: Add the <code>QA_functions</code> folder to your Matlab Path.</li> <li>Step 2: Run the <code>temporalQuality_main.m</code> or <code>structuralQuality_main.m</code> script in each folder to produce the QA results of all reconstructed images inside the folder.</li> <li>Step 3: Run the <code>make_figure_epi.m</code> and <code>make_figure_se.m</code> to produce some of the tables and figures used in the manuscript.</li> </ul> <h2>Example raw data and DICOM images</h2> <p>The data and DICOM images were acquired from Cima.X on the fBIRN phantom on 06.08.2024.</p> <ul> <li>DICOM folder: contains the DICOM images for four EPI scans (the first two scans for warm-up) and two SE scans.</li> <li><code>meas*.dat</code>: Siemens raw data of two EPI scans for temporal quality analysis and two SE scans for structural quality analysis.</li> <li><code>*data.h5</code> files: the ISMRMRD data of the four raw datasets.</li> <li><code>*out.h5</code> files: the images reconstructed by Gadgetron.</li> <li><code>*.nii</code>: the NIFTI-format reconstructed images.</li> <li><code>siemens2mrd_epi.m</code>: to convert the Siemens EPI raw data to ISMRMRD data.</li> <li><code>read_image.m</code>: to convert the Gadgetron-reconstructed h5-format images to NIFTI-format images.</li> </ul> <h2>Images and scripts for T1/T2 fitting</h2> <p>This package includes DICOM images and T1/T2 fitting scripts for the fBIRN phantom. Images for T1 fitting were acquired using a product turbo spin echo sequence with an inversion recovery pulse (repetition time = 4000 ms, echo train length = 4). Images for T2 fitting were obtained using a product SE sequence (repetition time = 3500 ms). Both measurements were conducted on the Siemens Prisma.Fit 3T scanner on 05.06.2024.</p> <ul> <li><code>T1 sub-folder</code>: contains all DICOM images for T1 fitting with inversion recovery times of {50, 150, 300, 450, 600, 750, 900, 1050, 1200, 1350, 1500, 2200, 3000} ms.</li> <li><code>T2 sub-folder</code>: contains all DICOM images for T2 fitting with echo times of {7.5, 15, 30, 45, 60, 75, 90, 130, 200, 250} ms.</li> <li><code>Do_T1fit.m</code>: Matlab script for T1 fitting.</li> <li><code>Do_T2fit.m</code>: Matlab script for T2 fitting.</li> </ul> <p>For more information regarding Pulseq and the workflow for data acquisition and image reconstruction, please visit our GitHub repositories: <a href="https://github.com/pulseq/pulseq">Pulseq Matlab software</a>, <a href="https://github.com/pulseq/tutorials">Pulseq Tutorials</a>, and <a href="https://github.com/pulseq/Pulseq-Rocks-2023-24-ISMRM-Reproducibility-Challenge">Pulseq Rocks for the 2024 ISMRM Reproducibility Team Challenge</a>.</p> <p>If you need any further information or have any questions, please feel free to contact our Pulseq email address: pulseq.mr@uniklinik-freiburg.de.</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.