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6,170 results for “european”
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 for article: Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018
<p>The data consists of the indicators for spatio-temporal analysis of wood Ecosystem Service (ES) potential, supply, and demand across Europe between 2008 and 2018. This dataset was used for the analysis of temporal trends of wood ES in the study "Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018".</p> <p>The data are collected and compiled from open access statistical databases. They consist of three parts:</p> <p>1. The PDF file with a detailed description of the data and all the input sources from which it was derived.</p> <p>2. The Zip file with 3 separate Excel files containing the short description of the indicators for mapping spatio-temporal changes, namely wood ES potential, wood ES supply and wood ES demand and their values between 2008 and 2018 at three different levels: continental, national and regional. Note that for the regional level only supply and demand indicators are available.</p> <p>3. The tiff file representing the spatial resolution for visualisation and analysis of the data.</p> <p> </p> <p> </p> <p>Resolution:</p> <p>Data are available for 3 spatial levels, in the temporal dimension between 2008 and 2018 (annually). All 3 indicators are available at continental (European) and national scales. The study area covers 24 countries of the European Union (EU) and Switzerland. Supply and demand are also available at regional level. At the regional scale, we assessed supply and demand using the nomenclature of territorial units for statistics (NUTS 3; n = 1061) and local administrative units (LAU; n = 957) from the year 2016 (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units.">Eurostat, 2016</a>). The visualisation of the resolution is available in the tiff file attached to the data.</p> <p>The data are visualised and analysed in the ETRS 1989 LAEA projection.</p> <p> </p> <p>For more information on the indicators used, data collection and processing, see the supplementary files of the published article.</p> <p> </p> <p> </p> <p><strong>DEFINITIONS of ES mapping indicators used in the study: </strong></p> <p><em>ES potential</em> - the hypothetical maximum yield of services potentially available for supply.</p> <p><em>ES supply</em> – the amount of the mobilized service within the ecosystem capable to provide a service at a given location in a certain time (frequently referred in literature as ES flow).</p> <p><em>ES demand </em>- the need for the ecosystem-based service by the end users. In this study demand is analysed from the perspective of service end-user.</p>
Native and non-native insect herbivores associated with native and non-native European trees
<p>We compiled a list of all native and non-native insect species known to feed on 77 tree species in Europe. For each tree species, a list of insects known to utilize that species as a host was compiled using a variety of sources. The resulting list consists of 7,598 tree species -insect species pairs. Each insect was researched to determine its taxonomic groupings, feeding guild (gall-maker, folivore, reproductive plant feeder, sap-feeder, or phloem/wood-borer), and whether it was native to Europe or non-native.</p>
Replication package for the article 'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.
<p>The documents in this replication package serve to reproduce the analysis for the article <br>'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in<br>the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.<br>This replication package is created by Pierre Benz (pierre.benz@unil.ch).</p>
European ancestry: 72 traits spanning multiple clinical domains (GRCh37)
<div> </div> <div> <p><span><span>This collection of 72 traits </span><span>contains</span> <span>a wide variety of traits including </span><span>metabolic traits (</span><span>e.g.</span> <span>lipid level</span><span>s</span><span>, glycemic traits</span><span>…</span><span>), </span><span>immune system</span><span> diseases (</span><span>e.g.</span> <span>inflammatory bowel </span><span>disease</span><span>,</span><span> celiac disease</span><span>)</span><span>, </span><span>cardiovascular outcomes and more. </span><span>This collection allows </span><span>experimenting </span><span>with combination of traits from </span><span>different clinical domains and search</span><span>ing</span><span> for highly pleiotropic variants. </span> <span>More details </span><span>on</span><span> GWAS summary statistics and their curation are available </span></span><a href="https://www.biorxiv.org/content/10.1101/2023.10.27.564319v1" target="_blank" rel="noreferrer noopener"><span><span>here</span></span></a><span><span>.</span></span><span> </span></p> </div>
European CFP EGMS Vertical Land Motion
<p>This dataset contains the estimates of EGMS-derived vertical land motion in European Coastal Flood Plain.</p>
European Investment Bank: Lending to Sub-Saharan Africa (2000-2022)
<p>The provided dataset traces the financial trajectory of the European Investment Bank (EIB) from 2000 to 2022, and has been derived from the EIB's annual financial reports. These reports offer a comprehensive account of the EIB's lending activities globally and specifically to countries outside of the European Union (non-EU or extra-EU), including and excluding the UK in the aftermath of Brexit. All numbers are in 2015 euros.</p> <p>However, the EIB's annual reports do not explicitly present an aggregate view of its lending activities in Sub-Saharan Africa (SSA). Therefore, this dataset is particularly valuable as it combines the country-level data from the reports to provide an overview of the EIB's total lending to SSA over this period.</p> <p> </p>
Scientific Webinar on Sustainable Public Food Procurement (SPFP) in the European Union
<p>On 23 April 2024, <a href="https://sapiensnetwork.eu/">SAPIENS Network</a> <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/sustainability-to-collective-table/">Early Stage Researcher Chiara Falvo</a> held a scientific webinar focusing on Sustainable Public Food Procurement (SPFP) in the European Union. The event took place in hybrid form and was hosted within the Master’s Course in Food Systems Law at the Department of Law of the University of Turin, also in collaboration with the Department of Agricultural, Forestry and Food Sciences (DISAFA). After a brief introduction on public procurement law and practice, Chiara delved into the legal strategies and mechanisms for integrating social and environmental considerations into the procurement of food and catering services. She also highlighted some national and local experiences that are leading the way in the field. During the event, <a href="https://www.giurisprudenza.unito.it/do/docenti.pl/Alias?silvia.mirate#tab-profilo">Professor Silvia Mirate</a>, who also acted as a discussant, provided an overview on the new EU Deforestation Regulation (EUDR), followed by Chiara's exploration of its relevance for public procurement. Contributing to bridging the gap between the scientific domains of law and agricultural and forestry sciences, this webinar may be relevant for students and newcomers to public procurement, especially in the food sector, as well as for anyone interested in understanding deforestation issues and the latest legal mechanisms to combat them. </p>
European eVALuation sites (EVAL): A European sampling for regional satellite product intercomparison
<p>For the quality assessment of satellite-based products it is necessary to perform product intercomparisons at global and continental scales. In the context of the Copernicus Land Monitoring Service High-Resolution Vegetation and Productivity Parameters (HR-VPP) project, a network of European eVALuation (EVAL) sites was defined to perform product intercomparisons over a representative sampling in terms of land cover and geographical area. EVAL is thus a spatial sampling of 3800 evaluation sites over the 39 countries of the European Economic Area (EEA39) [1]. The main selection criterium is the homogeneity, in terms of land cover, over areas of 1 km x 1 km (more than 70% of the area corresponds to the same land cover class) to be useful for the evaluation of decametric and hectometric (300 – 500 m) resolution products. </p> <p>The selection was based on surface albedo validation sites (SAVS1.0) [2], and European long-term ecosystem research (eLTER) sites [3]. Those sites were first screened to be homogeneous in land cover type, and then visually inspected using Google Earth to select homogeneous sites. Initially, only 597 sites (158 SAVS1.0 and 439 eLTER) were selected. Then, the network was complemented with Land use and land cover survey (LUCAS) samples [4] to get a better representativeness of European land cover types and geographical distribution. 10 additional LUCAS sites were selected for each 1 x 1 degree according to the same homogeneity criteria. Thereafter, random rejection of sites was conducted to keep the distribution of EVAL sites similar to that of the EEA-39 area (see distribution in <em>Figure_1</em>). In the last step, under sampled regions (e.g., Turkey or Iceland) were filled through visually identification of additional homogeneous sites.</p> <p>A total of 3800 sites were finally selected. The CORINE Land Cover 2018 (CLC2018) [5] was used to assign the land cover class to each site to allow the analysis per cover type. An aggregation of the CLC land cover classes was performed to reduce the number of classes to 8 main classes: Broad-leaved forest (BLF), Needle-leaf forest (NLF), Mixed Forest (MF), Crops, Grass, Shrubs, Agroforestry and Sparse. <em>Figure_2</em> shows the distribution of the 3800 EVAL sites per aggregated land cover type as compared to the actual distribution over the whole EEA-39. </p> <p> The xlsx file provided in this dataset contains two spreadsheets with the following information:</p> <ul> <li>‘EVAL_sites’: ‘Site label’, ‘Latitude’, ‘Longitude’, ‘CLC 2018’,’ Aggregated Land Cover’, and ‘Environmental zone’ for each location. </li> <li> ‘Legend’: information about the legends used in the dataset (CLC2018, aggregated land cover and environmental zone) and the number of EVAL samples for each class.</li> </ul> <p> </p> <p><strong><u>References:</u></strong></p> <p>[1] <a href="https://sdi.eea.europa.eu/catalogue/srv/api/records/8526ff78-b000-42e1-8360-a2fb3a51e4ac">https://sdi.eea.europa.eu/catalogue/srv/api/records/8526ff78-b000-42e1-8360-a2fb3a51e4ac</a></p> <p>[2] Loew, A., Bennartz, R., Fell, F., Lattanzio, A., Doutriaux-Boucher, M., Schulz, J., 2016. A database of global reference sites to support validation of satellite surface albedo datasets (SAVS 1.0). Earth Syst. Sci. Data 8, 425–438. https://doi.org/10.5194/essd-8-425-2016</p> <p>[3] <a href="https://www.lter-europe.net/">https://www.lter-europe.net/</a></p> <p>[4] <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=LUCAS_-_Land_use_and_land_cover_survey">https://ec.europa.eu/eurostat/statistics-explained/index.php?title=LUCAS_-_Land_use_and_land_cover_survey</a></p> <p>[5] <a href="https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac" target="_blank" rel="noopener">https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac</a></p> <p> </p>
Figure 2a in A procedure for taxon assessment based on morphological variation in European water frogs (Pelophylax esculentus complex)
Figure 2a. Correlations of each morphological character with the first 2 dimensions of the FAMD.
Data from: Genome-wide scans reveal selection signatures and cross-population variation in South African and European beef cattle breeds
<p>In genetics and evolutionary biology, the concept of selection signatures is used to describe specific patterns in the genome that are associated with the process of natural selection. These selection signatures provide insights into how evolutionary forces have shaped a population over time.In this study, a total of 96 samples were collected in several farms from four different cattle breeds, namely South African indigenous Nguni (n = 28) and Bonsmara (n = 21), Scottish Angus (n = 22), and Swedish Simmental (n = 25). Genotyped samples were subjected to quality control, and a total of 105,675 SNPs from 78 individuals remained for further analysis. Genomic signatures of positive selection within each breed were identified using the Integrated Haplotype Score (iHS) method, and cross-population comparison analysis using cross-population extended haplotype homozygosity ( XP-EHH), relative extended haplotype homozygosity (Rsb), and fixation index (Fst) methods, to assess the genetic differences between breeds. The results from the iHS method revealed selection signatures in two genomic regions for Bonsmara, six for Simmental, four for Nguni, and one for Angus cattle. Ten regions were found to be under selection, with BTA 12 being shared between Nguni and Bonsmara. Comparisons across populations using Rsb, and Fst methods performed better and revealed the most specific genomic regions that varied in selection between breeds. Gene annotation analyses linked candidate genes to several Quantitative Trait Loci (QTL). For example, in Simmental cattle's FAM110B gene was linked to carcass weight and body confirmation score. Bonsmara showed fewer candidate genes, such as CDK8 and FLT1, whereas Angus had none on BTA 18. Nguni identified potential genes such as CRB1, PLAG2GA, and VASH2, with CDK8 shared by Bonsmara and Nguni on BTA 12. Further cross-population studies revealed candidate genes associated with certain traits, genes including as PLCXD3, FAM149B1, and GRIK2 for Bonsmara versus Nguni, and SLIT2 and TSPAN9 for Simmental vs Angus. The study also emphasised gene related to meat quality, reproduction, health, illnesses, fertility, and body conformation score. Gene interaction study with the STRING database revealed a network of 63 candidate genes, demonstrating the structure of genetic connections, some biological processes. The study found that iHS performed well in population analysis with Nguni cattle, having exhibited the highest number of signatures across the genome, and significant signatures were also seen in comparisons between Nguni and Bonsmara using the Fst and Rsb methods. Furthermore, the study discovered that a bigger number of genes were connected with various traits, including sperm count and insemination per conception, sensitivity to bovine respiratory disease, and ease of calving. This genomic analysis underlined the relevance of the genetic relying which distinguishes distinct breeds. This understanding has the potential to significantly enhance selective breeding and increase desirable traits in cattle herds. This genomic analysis underlined the significance of the genetic basis for breed-specific traits. This understanding has the potential to drastically improve selective breeding and increase desirable traits in cattle herds.</p>
Leadership Styles in International Conflict Management. Action by the European Union Against Radicalization, Terrorism and Violent Extremism – Risks and Threats
<p>The article focuses on the antecedents of the emergence and management of international conflicts related to radicalization and terrorism in a European context and the interaction between leader and team. The objective of the desk research is to examine the correlation between the leadership style and the team, the leadership competencies, and to develop strategies for conflict resolution and management. It is achieved by analysing the European approach to the prevention of radicalization, terrorism and violent extremism, and exploring their positive and negative effects on a specific individual or group. The focus is on the role of leadership styles and competences at different hierarchical levels in order to achieve results and find solutions to problems. This paper presents the desk research conducted on countering radicalization and extremism.</p>
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - North Macedonia
<p>This dataset contains ASF laboratory analytical results in domestic pigs and wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2023_MK - Food and Veterinary Agency (FVA)</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)*</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)</li> </ul> <p> </p> <p> </p> <p>*This version of the ASF laboratory data has been republished with the subunit identification code (sampUnitIds.subUnitId) column empty due to data protection reasons</p>
Long-term changes in taxonomic and functional composition of European marine fish communities
<p>Evidence of large-scale biodiversity degradation in marine ecosystems has been reported worldwide, yet most research has focused on few species of interest or on limited spatiotemporal scales. Here we assessed the spatial and temporal changes in the taxonomic and functional composition of fish communities in European seas over the last 25 years (1994-2019). We then explored how these community changes were linked to environmental gradients and fishing pressure. We show that the spatial variation in fish species composition is more than two times higher than the temporal variation, with a marked spatial continuum in taxonomic composition and a more homogenous pattern in functional composition. The regions warming the fastest are experiencing an increasing dominance and total abundance of r-strategy fish species (lower age of maturity). Conversely, regions warming more slowly show an increasing dominance and total abundance of K-strategy species (high trophic level and late reproduction). Among the considered environmental variables, sea surface temperature, surface salinity, and chlorophyll-a most consistently influenced communities' spatial patterns, while bottom temperature and oxygen had the most consistent influence on temporal patterns. Changes in communities' functional composition were more closely related to environmental conditions than taxonomic changes. Our study demonstrates the importance of integrating community-level species traits across multi-decadal scales and across a large region to better capture and understand ecosystem-wide responses and provides a different lens on community dynamics that could be used to support sustainable fisheries management.</p>
Phase picker models and training data for paper "Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic"
<p>This ZIP file includes tensorflow models for seismic phase detection. Please see how to use these models here: https://github.com/NorwegianSeismicArray/tphasenet</p> <p>The HDF5 files includes waveforms and labels which are part of the training data set (only NORSAR event catalogue and station ARA0).</p>
Data from: Mast seeding in European beech (Fagus sylvatica L.) is associated with reduced fungal sporocarp production and community diversity
<p>A time series of seed production data from European beech (<em>Fagus sylvatica</em>) was combined with a fungal census (1977 to 2006) from La Chanéaz Fungus Reserve to evaluate the relationship between mast seeding and fungal resource availability. Annual fungal species' counts, traits, contemporaneous weather and seed production data are available here, alongside the code used to complete the analyses.</p>
Per capita sectoral emissions for European countries 1990 - 2022
<p>The data set contains per capita emissions (t CO2eq/capita) for European countries from 1990 to 2022 (for some countries values starting in 1985 are contained). Emission data is taken from the <a href="https://doi.org/10.2909/6331f651-8863-4656-a911-669f2a332a1e" target="_blank" rel="noopener">European Environment Agency</a> [1]. Population data is taken from <a href="https://doi.org/10.2908/DEMO_GIND" target="_blank" rel="noopener">Eurostat </a>(population at January 1th in each year) [2]. Emissions are reported per sector, with the sectoral categories according to <a href="https://www.bmuv.de/fileadmin/Daten_BMU/Download_PDF/Gesetze/191118_ksg_lesefassung_bf.pdf" target="_blank" rel="noopener">Annex I in the German Federal Climate Change Act</a> [3]:</p> <p>Energy: 1.A.1, 1.A.3.e, 1.B<br>Industry: 1.A.2, 2, 1.C<br>Buildings: 1.A.4.a, 1.A.4.b, 1.A.5<br>Transport: 1.A.3.a, 1.A.3.b, 1.A.3.c, 1.A.3.d<br>Agriculture: 5,6<br>Land use, land-use change and forestry: 4</p> <p>The data set contains the data as a .csv and as a .xlsx file. Additional, the code for processing the raw data (available from the European Environment Agency and from Eurostat) is contained as a jupyter notebook.</p> <p>Please communicate any questions, corrections or comments to mirko.schaefer [at] inatech.uni-freiburg.de</p> <p>[1] National emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanism, April 2024. European Environment Agency (2024)<br>[2] Demographische Veränderung - absoluter und relativer Bevölkerungsstand auf nationaler Ebene. Eurostat (2024)<br>[3] German Federal Climate Change Act (2021)</p> <p> </p> <p> </p>
Survey Data for Multicriteria Satisfaction Analysis of Cargo Bike Last-Mile Delivery in European Cities
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. <br>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.<br>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.<br>The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility. <br>As part of writing a chapter for the SSH CENTRE book on ‘Strengthening European mobility policy - Governance recommendations from innovative interdisciplinary collaborations’, we elicit the opinions of citizens in urban logistics policymaking through a series of surveys in different European cities. The files attached to this Zenodo webpage are therefore the dataset contains raw survey data from a study utilizing Multicriteria Satisfaction Analysis (MUSA) to evaluate public perceptions of cargo bike last-mile delivery in London, Paris, Rome, Dublin, and Warsaw. The data encompasses over 2,000 responses, detailing participants' satisfaction levels with various aspects of cargo bike delivery services, including CO2 emissions, noise, traffic, safety, and shipping costs. This dataset supports comprehensive analyses of urban logistics policies aimed at sustainable mobility solutions in these specific cities.</p>
Data and Script used in "Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest"
<p>The R code and data provided in this repository allow to reproduce the data carpentry, analysis and visualization of “Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest” (https://doi.org/10.1016/j.scitotenv.2024.173572).</p> <p> </p> <p>Folder structure</p> <p> </p> <p>Data abstracts:</p> <p>Data_abstract_climate.pdf</p> <p>Data_abstract_soil_biotics.pdf</p> <p>Data_abstract_soil_abiotics_openness.pdf</p> <p> </p> <p>Data:</p> <p>Climate_data.xlsx</p> <p>Soil_biotics.xlsx</p> <p>Soil_abiotics_openness.xlsx</p> <p> </p> <p>R Scripts:</p> <p>00-preamble.R loads all required packages</p> <p>01-data-carpentry.R loads all datasets and prepares the analysis of all experimental periods.</p> <p>02-data-analyses-microclimate.R compares understorey air and soil microclimate between forest types and treatments, presents diurnal and seasonal variations and tests the relationship of under- and overstorey openness on microclimate.</p> <p>03-data-analyses-decomposition.R compares decomposition rates and feeding activity between forest types and treatments and models the dependencies of soil biological activity on microclimate and soil abiotic factors.</p> <p> </p> <p>Information of related software and package versions used in the script:<br>R version 4.3.2 (2023-10-31 ucrt)<br>Platform: x86_64-w64-mingw32/x64 (64-bit)<br>Running under: Windows 10 x64 (build 19045)<br>Matrix products: default</p> <p> </p> <p>Contact</p> <p>Please contact me at annalena.lenk@uni-leipzig.de if you have further questions.</p>
Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps
<p>This data set contains model output presented in the following paper: I. Brangers, H. Lievens, A. Getirana, and G. J. M. De Lannoy. (2024). Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps. Water Resources Research. Under review.</p> <div>The model simulations were carried out in NASA's Land Information System (LIS), using the NoahMP v3.6 land surface model, forced with ERA5. The land surface model was coupled to the HyMAP routing algorithm to produce streamflow estimates. The data contains model results for the western European Alps for the period of 2015-2021 for two seperate cases. 1) The OL run: model run without assimilation of external observations; and 2) DA run: model run with the assimilation of Sentinel-1 snow depth observations.</div> <div> </div> <div>The zip-folders contain netcdf files for each day of the simulation period, for 1) the river discharge (_ROUTING), </div> <div>2) land surface model variables such as snow depth and SWE (_SURFACEMODEL_yyyy) grouped per year, and 3) variables related to the data assimilation such as the spread and innovations (_EnKF).</div>
ScienceDex guides
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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.