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249 results for “Synergy”
Data for Publication - Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo
<p>Data used for the publication:</p> <p>"Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo" - Ieben Broeckhoven, Jonas Depecker, Trésor Kasereka Muliwambene, Olivier Honnay, Roel Merckx and Bruno Verbist</p>
Synergy database dump
<p>An SQL dump of the Synergy database. Synergy was first published in 2014, but the associated application has now reached the end of its life. This database contains the data that was presented in the publication, and that all tools in the web application were using.</p>
The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training
<p>This is the minimal dataset underlying the paper:</p> <p>"The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training"</p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
SUPER-G synthesis report & associated data - D3.6 Synergies & Tradeoffs
<p>A series of co-innovation workshops within 23 farm networks helped to identify PG management <br>options, innovations, and technologies to trial on commercial and experimental farms before being <br>road tested and demonstrated on a selection of pilot farms. A series of over 40 experiments were <br>conducted on commercial and research farms across the biogeographic regions on a wide range of <br>topics. </p> <p>The field trials and experiments have involved detailed integrated assessments on farms or <br>permanent grassland (PG) areas to investigate the synergies and trade-offs between productivity, <br>biodiversity, and delivery of other selected ecosystem services (ES). </p> <p>The investigations were placed into five overarching themes:<br>1. New Grassland Species / Diverse Species Swards<br>2. Precision Grassland Management<br>3. Nutrient Management<br>4. Agri-Environment Options<br>5. Miscellaneous</p>
PERCEIVE: WP4: Spatial determinants of policy performance and synergies: Task4.4: Cohesion Policy vs Urban and Rural policies to address spatial discrepancies in EU territorial policy
<p>This dataset consists of data addressing the relationship between territorial cohesion objectives and the problems perceived by citizens. In particular a comparative analysis between the case study regions will generate data useful for identifying best practices in mixing the EU policy instruments for a better achievement of regional needs. Data that will be generated via focus groups interviews among representatives of LMA (local management authorities) in 2 Polish regions: Dolnośląskie and Warmińsko-Mazurskie . Interview transcripts and report was used to address how territorial cohesion objectives match the “real problems” of regions. The focus groups were built around the following main topics: 1) governance of the Cohesion Policy projects, in order to understand how different authorities at different levels cooperate and share the responsibilities for the implementation of the Cohesion Policy; 2) level of citizen engagement, in order to understand whether a bottom-up approach is used; 3) how the media inform on the Cohesion Policy programmes, in order to appreciate the discrepancies (if any) about the aims of Cohesion Policy and its construction on the public discourse. Comparing current and past programming periods, we investigate how the policy performs in reducing the gap between territorial cohesion objectives and “real problems” defined by LMAs and citizens. Because it is project-specific data and reflects the concept and methodology of the study under PERCEIVE they are perceived as unique - similar data does not exist. Potential users are be Regional Policy’s European/National/Local policy makers and practitioners, European networks and associations looking to data on LMA opinions on cohesion policy implementation in Poland to be used in policy recommendation, studies and policy making process; next group of potential data users are researchers working on assessment of Cohesion Policy, data may be used as a source for topic-related studies, case studies, comparisons.</p> <p>The dataset is made up of 5 files: 2 files consist of reports from the workshops, 2 files consist in transcripts of interviews to practitioners, beneficiaries and targets of the Cohesion Policy projects in the Polish selected case‐study regions. Interviewees are asked to provide their views and perceptions on the multilevel governance system, on the communication activities of the Operational Programmes and on the effectiveness of Cohesion Policy. 1 readme file is included.</p>
Policies, systemic factors, trade-offs and synergies for agroecological transitions
<table> <tbody> <tr> <td>In order to identify the existing policies, systemic factors, trade-offs and synergies for agroecological transition we collected data via desk research, interviews with policy-makers and focus groups. This dataset presents the interview outcomes for 39 policy-makers from our focal countries</td> </tr> </tbody> </table>
Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture
<p>Data: Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture</p>
Chiroptical properties of streptorubin B – the synergy between theory and experiment.
<p>Computational analysis of the calculated and measured optical rotation (OR) together with other calculated chiroptical properties such as electronic circular dichroism (ECD) and vibrational circular dichroism (VCD) of the prodigiosin alkaloid streptorubin B</p>
The impact of market integration on the digital divide: An analysis based on the heterogeneous effect of innovation resource synergy
<p>Resource market reforms have provided opportunities for reducing the digital divide (DD). In this study, we analyzed the theoretical mechanism of the impact of market integration (MI) on DD and the intermediary role of innovation resource (IR) synergy. Data from 271 cities in China were collected, and the DEA–Malmquist index and Thiel index were utilized to quantify DD among these cities during 2011–2019. The intermediary effect and SDM models were adopted for an empirical analysis. The results showed that (1) MI had a negative effect on DD in addition to significant spatial spillover effects. MI widened DD in the local city but narrowed the same in the adjacent cities. (2) IR synergy exerted positive intermediary effects, which were heterogeneous. Through human resource synergy, MI narrowed DD, through knowledge synergy, MI widened DD, and through the R&D capital synergy, MI did not significantly influence DD. (3) There was no significant spatial spillover effect of the intermediary role of IR synergy.</p>
Dataset for "Understanding Wavelength-dependent Synergies between Morphology and Photonic Design in TiO2-based Solar Powered Redox Cells"
Open the record for dataset details and reuse information.
Unwrapping reworked crust at the Columbia supercontinent margin within Amazonian Craton using satellite potential field data synergy
<p>This file contains the geochronology data inventory (Supplementary Material Table 1) as a MS Excel (*.xls) table used for interpretation in the the original publication of 'Unwrapping reworked crust at the Columbia supercontinent margin within Amazonian Craton using satellite potential field data synergy'. Paper published in Geoscience Frontiers in early 2022.</p>
Metabolomics-based phenotypic screens for evaluation of drug synergy via DIMS
<p>Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery. Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.</p>
Dataset for mansucript: Deeper Insight into Photopolymerization: The Synergy of Time-Resolved Non-Uniform Sampling and Diffusion NMR
<p>Dataset and processing scripts used in Manuscript:</p> <p><a href="https://pubs.acs.org/doi/10.1021/jacs.2c05944"><strong>Deeper Insight into Photopolymerization: The Synergy of Time-Resolved Non-Uniform Sampling and Diffusion NMR</strong></a></p> <p><a href="https://pubs.acs.org/doi/10.1021/jacs.2c05944"><strong>Journal of the American Chemical Society, DOI: 10.1021/jacs.2c05944</strong></a></p> <p>The main folder contains two Jupyter Notebooks:</p> <ol> <li>Analyze_DiffusionOnlyData.ipynb</li> <li>Analyze_InterleavedData.ipynb</li> </ol> <p>The first is used to analyze Diffusion-only analysis of photopolymerization of N,N-bis(anthracen-9-ylmethyl)butane-1,4-diamine and dimerization of anthracene. It is used to generate Figure SI.1.</p> <p>The second is used to analyze the first system using TR-NUS and TR-Diffusion dataset acquired in interleaved mode. It is used to generate Figures: 2,3,4 in main manuscript and Figure SI.3</p>
Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling
<p>The datasets contain input data and data emulated by Neural networks (NN) used in the study 'Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling'</p> <p>- <strong>NN2018_CMEMS_ARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) along ARGO float paths in 2018 with vertical resolution taken from CMEMS (13 depth levels in the depth range studied here); NN was applied either on CMEMS physics ('C') or on ARGO physics ('A'); additionally, CMEMS BGC model data (Chlorophyll-a and Oxygen) along these paths are included; (filenames follow the names of floats given in Table 1:<em> floatname</em>_2018_NNARGOCMEMS.mat)</p> <p>- <strong>NNalongARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700 nm) along ARGO float paths together with input ARGO data (time, latitude, longitude, salinity, temperature, sigma_T and BGC variables); all variables are mapped with 1m vertical resolution; depth range is [1m 150m] ; additionally, float ogs7 data include NO3, float hzg1 data does not contain Chlorophyll-a and backscatter at 700 nm; (filenames follow the names of floats given in Table 1:<em> floatname</em>_euph_1mRes_150mALLINCLNN.mat)</p> <p>- <strong>NNReconBlackSea.tar.gz</strong>: archive contains Matlab binary files consisting of basin wide NN derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) for the years 2015-19 and 2010/11 (weekly mean data); additionally CMEMS data (time, latitude, longitude, salinity, temperature, sea surface height) are provided for the photic zone; (filenames are reconNNCMEMS_2015_2019.mat and reconNNCMEMS_2010_2011.mat, respectively)</p>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"
<div> <div> <div>This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the paper titled "Unraveling Secondary Ice Production in Winter Orographic Clouds through a Synergy of In-Situ Observations, Remote Sensing, and Modeling." The in-situ and remote sensing measurements were conducted at Mount Helmos in Peloponnese as part of the CALISHTO campaign (https://calishto.panacea-ri.gr/).</div> </div> </div> <div>Preprint accessible at: https://doi.org/10.21203/rs.3.rs-3502790/v1</div>
Data on the Blackthorn fruit peel: phenolic compounds and antimicrobial synergy with blue light against Listeria monocytogenes
<p>The dataset contains results obtained during a project funded by the National Science Centre, Poland [Grant number 2022/45/B/NZ9/00299 (OPUS-23)].</p>
Optimizing protein quality: synergies and comparisons of single and combined alternative proteins from diverse sources
<p>This dataset includes (1) the AAS of alternative protein ingredients, calculated with the references set by PDCAAS and DIAAS (adult age group); (2) the AAS of the limiting AA for combinations made with these protein ingredients (AAS calculated based on PDCAAS reference); and (3) the AAS of the limiting AA for combinations made with these protein ingredients (AAS calculated based on DIAAS reference)</p>
PERCEIVE: WP4: Spatial determinants of policy performance and synergies: City smartness
<p>The dataset contains data on both smart cities projects and smart cities characteristics to be used for the computation of the composite indicators using the Stochastic Multi-criteria Acceptability Analysis (SMAA) methodology. The dataset reports the data used to analyze the concept of a ‘smart city’ along two main dimensions. First, it contains the data to operationalize the concept of smart city along the dimensions elaborated in the ongoing literature including proxies for networked infrastructure to improve economic and political efficiency and enable social and cultural development, the extent of business-led development, the social inclusion of various urban residents in public services, extent of high-tech and creative industries, social and relational capital, and social and environmental sustainability. Second, it reports the results of the analysis using the above dimensions to compute a new index of smartness and quality of life based on SMAA. </p> <p>PLEASE NOTE that the file ' PERCEIVE_WP4_T4-1_SmarCities_20190704_v01.csv' contains data from Eurostat. For those data re-use involves normalisation of the raw data according to the max-min procedure.</p>
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