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2,487 results for “Country”
Biomass Domestic Material Consumption by Country and over Time
<p>Biomass domestic material consumption in thousand tons, and tons per capita for European countries.</p> <p>Our dataset has a 10.4% larger congruent dataset (to be used in various supervised or unsupervised learning models, such as machine learning) than the original Eurostat dataset after imputation, backcasting, forecasting. It has overall 18% more observations after processing than the dataset at source. </p>
Biomass Exports in Europe by Country
<p>Biomass exports in thousand tons, and tons per capita for European countries.</p> <p>Our dataset has a 10.4% larger congruent dataset (to be used in various supervised or unsupervised learning models, such as machine learning) than the original Eurostat dataset after imputation, backcasting, forecasting. It has overall 18% more observations after processing than the dataset at source. </p>
Safe Country Policies Dataset (SACOP)
<p>The Safe Country Policies Dataset (SACOP) (Version 1.1) provides original information on the adoption and characteristics of Safe Country Policies and National Asylum Frameworks in 195 countries from 1951 until 2021.</p> <p>An interactive visualization by Andreas Perret (nccr <em>–</em> on the move) can be accessed <a href="https://tabsoft.co/3AUKrRu">here</a>.</p>
Meta-analysis and gender classification of 914 national and international surveys in six European countries (2000-2023)
<p><span>This data frame presents the results of a quan</span><span>ti</span><span>ta</span><span>ti</span><span>ve content analysis of the occurrence of gender‐based concepts, themes, issues, and solu</span><span>ti</span><span>ons within large‐scale poli</span><span>ti</span><span>cal and sociological survey ques</span><span>ti</span><span>onnaires fielded cross‐na</span><span>ti</span><span>onally in Europe and in six European countries: Denmark, Germany, Hungary, Switzerland and the UK, spanning 2000‐2023. Data was collected by teams from each country between September 2023‐January 2024. Teams collected ques</span><span>ti</span><span>ons in the original language and provided a transla</span><span>ti</span><span>on into English. Analysis was conducted using the translated text. The unit of analysis (‘CODING_UNIT_TEXT’) was the individual 'gender‐related argument' within a survey ques</span><span>ti</span><span>on. This could be the en</span><span>ti</span><span>re survey ques</span><span>ti</span><span>on, a sub‐ques</span><span>ti</span><span>on (in the case of matrix ques</span><span>ti</span><span>ons), or a singular response op</span><span>ti</span><span>on (for mul</span><span>ti</span><span>ple choice ques</span><span>ti</span><span>ons). Coding units were coded in three key domains:(1) Gender concepts, (2) Themes/issues, and (3) Solu</span><span>ti</span><span>ons. Up to two Themes/Issues and Solu</span><span>ti</span><span>ons could be coded per coding unit. Several coding categories within the Themes/Issues and Solu</span><span>ti</span><span>ons domains func</span><span>ti</span><span>on hierarchically, where a coder first assigned a higher‐level category and then as many subcategories as applicable. For example, a ques</span><span>ti</span><span>on concerning government‐funded childcare is coded as B1_Economy ‐> B1_4_LabourMarket ‐> B1_4_1_CareWork ‐> B1_4_1_3_Childcare. The corresponding codebook presents the uni</span><span>ti</span><span>sa</span><span>ti</span><span>on process and coding categories in full detail.</span></p>
Gender codification of 412 national (general) and European Parliament elections in six European countries (2003-2021)
<p>This dataset has been produced by applying the Manifesto Gender Analysis (MGA) codebook to 412 national (general) and European Parliament elections in the six countries participating in the UNTWIST project (Denmark, Germany, Hungary, Spain, Switzerland, and the UK) from 2003 to 2021.</p> <p> The Manifesto Gender Analysis coding procedure, developed by WP4 of the UNTWIST consortium, aims to analyse gender-related content in party manifestos. It relies on existing manifestos collected by MARPOR and EM projects from 2003-2021 in six national contexts: Denmark, Germany, Hungary, Spain, Switzerland, and the United Kingdom. The process involves splitting manifestos into quasi-sentences, coding them based on a scheme inspired by previous projects and feminist typology, and completing an expert survey. This method ensures comprehensive analysis and potential scalability through computational methods. </p> <p>The coding procedure involves a series of essential steps, divided in two main activities: the classification of manifestos’ quasi-sentences, and the completion of a survey dedicated to more general concepts which can be gauged by evaluating the content of the entire documents. In the latter case, then, the unit of measure of each coder consists in the manifesto document, whereas in the former the units of measure are quasi-sentences - i.e., arguments denoting a verbal expression of a political idea or issue. Coders are instructed to split sentences containing multiple arguments into quasi-sentences and ensure that each quasi-sentence encapsulates a single political idea or issue. </p> <p>Once the manifestos are split into said units, coders classify the arguments following the MGA coding scheme. The coding scheme (MGA) consists of 5 domains and 25 coding categories, covering various aspects of gender-related issues. Each domain includes an "other" category for relevant statements that do not fit precisely into the defined categories. Apart from coding categories related to specific themes, the coding scheme then includes additional dimensions. The classification process consists of seven steps: (1) assessing whether the quasi-sentence addresses gender-related issues, (2) defining both the domain and coding category, (3) determining whether the quasi-sentence refers to a specific recipient or group based on gender and/or sexual orientation, (4) evaluating intersectionality, (5)<strong> </strong>assigning the sentiment or connotation, (6) determining if it's related to a goal, issue, or policy, and (7) characterising the policy if applicable.</p> <p>After completing the classification of the quasi-sentences in a given manifesto, coders fill in a survey for each manifesto document. The surveys provide information that cannot be directly inferred from the quasi-sentences, focusing on the gender ontology of a manifesto, the degree to which a manifesto entails a binary conception of sexes, the extent to which a manifesto promotes a patriarchal conception of the society, and how much a manifesto promotes heterosexuality as the only normal and socially acceptable sexual orientation of individuals. While the last four characteristics are gauged relying on quasi-interval measures (scales ranging from 0 to 10), the first one, gender ontology, consists in a categorical variable which distinguishes between manifestos with an essentialist ontology – gender and sex are the same and inseparable –, a constructivist ontology – biological sex is mediated through social construction of femininity and masculinity –, and other or undefined ontologies.</p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
UNFCCC country-submitted greenhouse gas emissions data until 2024-07-05
<p>Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2024-07-05.</p>
Survey data on climate policy in three countries (Peru, Ghana, Philippines) within the project "Sustainable Middle Classes in Middle Income Countries: Transforming Carbon Consumption Patterns (SMMICC)"
<p>The unprecedented growth of the new middle classes in middle income developing countries implies a strong growth in both consumption and carbon emissions. The research project Sustainable Middle Classes in Middle Income Countries (SMMICC) investigates the drivers of carbon consumption choices of the new middle classes and policy options to decrease their carbon footprints, including the implementation of carbon taxes</p> <p>The research of the authors generated quantitative data on the acceptability of carbon taxes in three countries (Peru, Ghana, Philippines).</p> <p> </p> <p><strong>The data is provided in the following formats:</strong></p> <p>- 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.csv<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_peru.csv<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.csv</p> <p>- 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.dta<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_peru.dta<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.dta</p> <p>Additionally, the codebooks on variables of questionnaire and political parties in each country are attached in a csv format.</p>
CoHERE Work Package 3 Survey of Inhabitants of Baltic Countries on Song and Dance Celebrations
<p>Part of Work Package 3 for the 'Critical Heritages' research project ( https://research.ncl.ac.uk/cohere/researchstrands/#WP3%20Cultural%20forms%20and%20expressions%20of%20identity%20in%20Europe ).</p> <p>One of the key case studies in CoHERE Work Package 3 has been the Song and Dance Celebration tradition in the Baltic states (included in the UNESCO list as a masterpiece of the oral and intangible heritage of humanity in 2003). The case study reveals several aspects of this festival: cultural, economic, social dimensions and governance. Through examining different aspects of this festival tradition and everyday practices it responds to several objectives of the WP3. Being a key social and cultural event in three Baltic countries, it provides a ground for debates on how performative practices and festivals can contribute to identity construction and transformation, developing sense of belonging, serve as platform for where heritage practices of different social groups can meet.</p>
Data for: The circular economy potential of urban organic waste streams in low- and middle-income countries
<p>This dataset includes the research data and supporting information for the publication "The circular economy potential of urban organic waste streams in low- and middle-income countries" which was published in the Journal of Environment, Development and Sustainability (DOI: 10.1007/s10668-021-01487-w).</p> <p>This dataset and the associated publication are the basis upon which the REVAMP (Resource Value Mapping) tool has been developed. See more info about the REVAMP tool here: https://www.sei.org/revamp</p> <p> </p>
Database on vacancies in selected non-EU countries
<p>This database is a revised version of the deliverable D3.1 of the Horizon Europe project 'Global Strategy for Skills, Migration and Development' (GS4S). For more information, please see the associated working paper: Locating Shortages in Migrants’ Origin Countries: A Big Data Approach, authored by Friedrich Poeschel. </p>
Occurrence cubes at species level for European countries
<p>This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the <a href="https://www.gbif.org/">Global Biodiversity Information Facility (GBIF)</a> by year (year), 1x1km spatial <a href="https://www.eea.europa.eu/en/datahub/datahubitem-view/3c362237-daa4-45e2-8c16-aaadfb1a003b">EEA reference grid</a> cell (eea_cell_code) and species (speciesKey). For each grouping, the number of occurrences found in GBIF (n) and the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (min_coord_uncertainty) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see <a href="https://github.com/trias-project/occ-cube/blob/master/src/3_assign_grid.Rmd#L198-L234">this code</a>). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling.</p> <p>The occurrence cubes are built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package.</li> <li>The code to process the data to cubes is publicly available on GitHub at <a href="https://github.com/trias-project/occ-cube-alien">https://github.com/trias-project/occ-cube</a> (version <a href="https://github.com/trias-project/occ-cube/tree/20240124">20240124</a>).</li> </ul> <h2>Files</h2> <ul> <li><strong>Occurrence cubes at species level per country</strong>: filename format countrycode_species_cube.csv.</li> <li><strong>Taxonomic information for species in a cube</strong>: filename format countrycode_species_info.csv.</li> </ul> <h2>Included countries</h2> <ul> <li><strong>Belgium</strong> (BE): based on <a href="https://doi.org/10.15468/dl.9qx3ba">https://doi.org/10.15468/dl.9qx3ba</a></li> <li><strong>Italy</strong> (IT): based on <a href="https://doi.org/10.15468/dl.jghpm5">https://doi.org/10.15468/dl.jghpm5</a></li> <li><strong>Lithuania</strong> (LT): based on <a href="https://doi.org/10.15468/dl.duegx2">https://doi.org/10.15468/dl.duegx2</a></li> <li><strong>Slovenia</strong> (SI): based on <a href="https://doi.org/10.15468/dl.9eky98">https://doi.org/10.15468/dl.9eky98</a></li> <li><strong>Romania</strong> (RO): based on <a href="https://doi.org/10.15468/dl.b7z5vw">https://doi.org/10.15468/dl.b7z5vw</a></li> <li><strong>Portugal</strong> (PT): based on <a href="https://doi.org/10.15468/dl.b89nr4">https://doi.org/10.15468/dl.b89nr4</a></li> </ul> <p>Occurrence cubes are added on demand. To include occurrence cubes for other European countries, <a href="https://github.com/trias-project/occ-cube/issues">leave an issue</a> or contact the main author, or generate your own cube using the code in <a href="https://github.com/trias-project/occ-cube">this repository</a>.</p>
Global Human Settlement Layer per zoom-level 18 Quadtree tile for selected countries as Spatialite database with OpenStreetMap building completeness assessment
<p>This Spatialite database contains the built-up area of the Global Human Settlement Layer (GHSL) per zoom-level 18 Quadtree tile. Additionally, it provides a comparison of the GHSL with buildings in OpenStreetMap: For each tile the built-up ratio between the building footprints and the GHSL is given and a binary completeness assessment (buildings complete, not complete) is provided for easy use. This dataset was created using the obmgapanalysis tool: https://git.gfz-potsdam.de/dynamicexposure/openbuildingmap/obmgapanalysis</p>
Country Compendium of the Global Register of Introduced and Invasive Species. Dataset.
<p>The Country Compendium of the Global Register of Introduced and Invasive Species (GRIIS) is a collation of data across 196 individual country checklists of alien species, along with a designation of those species associated with evidence of impact at a country level. </p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
Inclusive Green Growth Dataset for African Countries
<p><span>Tracking the progress of countries in inclusive green growth (IGG) is crucial for shaping effective sustainable development policies. However, comprehensive IGG data is often inaccessible. Accordingly, rigorous empirical contributions in this direction in the context of Africa remain sparse. To address this, we computed IGG scores for 22 African countries from 2000-2020. Our data reveal that only nine of these countries are achieving green and inclusive growth. This dataset equips researchers and institutions to assess IGG progress and identify pathways that African governments can leverage to promote sustainable development.</span></p>
Beyond the Digital Divide: Sharing Research Data across Developing and Developed Countries
<p>The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government. </p> <p>Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized. </p> <p>Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers. </p> <p>Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.</p> <p>Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis. </p>
Ramularia detection dates by country
<p>Dark and light themed maps showing the detection rate of ramularia by country. Detection rates are taken from the following two publications.</p> <pre>Walters, D. R., Havis, N. D., and Oxley, S. J. P. <strong>2008</strong>. Ramularia collo-cygni: the biology of an emerging pathogen of barley. FEMS Microbiology Letters 279:1–7. https://doi.org/10.1111/j.1574-6968.2007.00986.x </pre> <pre>Havis N. D., Brown J. K. M., Clemente G., Frei P., Jedryczka M., Kaczmarek J., Kaczmarek M., Matusinsky P., McGrann GRD., Pereyra S., Piotrowska M., Sghyer H., Tellier A., and Hess M., <strong>2015</strong>. Ramularia collo-cygni - an emerging pathogen of barley crops. Phytopathology 105: 895-904. https://doi.org/10.1094/PHYTO-11-14-0337-FI </pre> <p>Country outlines were taken from Natural Earth: http://www.naturalearthdata.com/downloads/50m-cultural-vectors/</p>
Open Access in developing countries – attitudes and experiences of researchers Dataset
<p>A survey was conducted of 507 researchers from the developing world and connected to INASP’s AuthorAID project to ascertain experiences and attitudes to Open Access publishing. This file is the raw output from the survey, with names and email addresses removed to preserve anonymity. </p>
Post-trial access practice in Malaria, Tuberculosis, and NTDs Clinical Trial studies in Sub-Saharan African countries, quantitative study
<p>This is the data set used <span>to evaluate post trial access plan and implementation practice on TB, Malaria and NTD clinical trial studies conducted in the sub-Saharan African countries. </span></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.