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This round of Eurobarometer surveys queried respondents on standard Eurobarometer measures, such as how satisfied they were with their present life, whether they attempted to persuade others close to them to share their views on subjects they held strong opinions about, whether they discussed political matters, what their country's goals should be in the next 10 to 15 years, and how they viewed the need for societal change. Additional questions focused on the respondents' knowledge of and opinions on globalization and on the European Union (EU), including how well-informed they were about the EU, what sources of information about the EU they used, whether their country had benefited from being an EU member (or would benefit from being a future member), and the extent of their personal interest in EU matters. Other questions queried respondents about their country's public administration, the transparency of both their own government institutions and those of the EU, and how important they thought transparency was in their functioning. Respondents were asked which countries, specifically Turkey, Croatia, and the Former Yugoslav Republic of Macedonia, they would favor joining the EU. National and European identity is a major focus of the survey. Questions focused on to what extent respondents felt they were a citizen of their region, of their country, of Europe, and of the world, whether they were content with their identity, and their feelings on the importance of being European. The second major focus of the survey was European elections. Respondents were queried about their interest in the elections, whether or not they would vote, the main criteria in making these decisions, and what themes the electoral campaign should focus on. In addition, respondents were asked to name the party they voted for in the European Parliament (EP) elections in June 2004, May 2007, November 2007, and the latest parliamentary elections in their respective countries. For the third major focus, European values and value priorities, respondents were asked to identify their personal values, whether they thought EU member states shared common values, and how close or distant these shared values were. In addition, respondents were asked to select the most important values they associated with the idea of happiness. For the final major focus of the survey, climate change, respondents were queried about their knowledge of and views on climate change, including whether they thought climate change was a serious problem, whether enough is being done to fight it, and the reasons why individuals may or may not take action in fighting climate change. Respondents were also asked to identify the personal actions they have taken regarding climate change and to evaluate the objectives proposed by the EU to limit the impact of climate change. Demographic and other background information includes age, gender, nationality, origin of birth (personal and parental), marital status, left-right political self-placement, strength of party attachment, occupation, age when stopped full-time education, household composition, ownership of a fixed or a mobile telephone and other durable goods, type and size of locality, region of residence, and language of interview (in select countries). face-to-face interview The original data collection was carried out by TNS Opinion and Social on request of the European Commission Between March 25th 2008 and May 4, 2008. Data for this Eurobarometer study are being released in two parts. Part 1 includes original study materials supplied by GESIS: (1) SPSS portable file; (2) SPSS syntax file containing user-defined missing values; and (3) documentation files. These files are being released in a zipped package. A documentation file has been provided by ICPSR to describe the contents of this zipped package. Part 2 includes study materials supplied by ICPSR: (1) SPSS, SAS, and Stata setup files; (2) SPSS and Stata system files, and a SAS transport (CPORT) file; (3) ASCII column-delimited and tab-delimited data files; and (4) documentation files. The data in Part 1 and Part 2 are identical, except for the following: Variable V1 'ARCHIVE STUDY NUMBER - DISTRIBUTOR' in Part 2 has been recoded to the ICPSR study number. Documentation files have been provided both by GESIS and ICPSR; the ICPSR documentation files may contain additional information. Data on voting behavior in European (D53) and national parliamentary elections (D54), and on party attachment (D2) have now been provided by the data producer and are now available. The codebook and setup files for this collection contain characters with diacritical marks used in many European languages. A split ballot was used for one or more questions in this survey. The variable V892 defines the separate groups. The documentation and/or setup files may contain references to Norway, but Norway was not a participant in this wave of Eurobarometer surveys. This collection contains no data for Norway. The fieldwork dates in the data file for Malta and Croatia are not consistent with the fieldwork dates in the "Technical Specifications" section of the ICPSR codebook. No documentation was supplied by the data producer for responses to open specifications of "OTHER" items for questions QC4, QC6, QD7, QE1, QE6, QE7 and QE8. The basic sample design applied in all states is a multistage, random (probability) one. In each country, a number of sampling points were drawn with probability proportional to population size (for a total coverage of the country) and to population density. In order to do so, the sampling points were drawn systematically from each of the "administrative regional units", after stratification by individual unit and type of area. They thus represent the whole territory of the countries surveyed according to the EUROSTAT NUTS II (or equivalent) and according to the distribution of the resident population of the respective nationalities in terms of metropolitan, urban and rural areas. In each of the selected sampling points, a starting address was drawn, at random. Further addresses (every Nth address) were selected by standard "random route" procedures, from the initial address. In each household, the respondent was drawn, at random (following the "closest birthday rule"). All interviews were conducted face-to-face in people's homes and in the appropriate national language. As far as the data capture is concerned, CAPI (Computer Assisted Personal Interview) was used in those countries where this technique was available. Please refer to the GESIS documentation and Technical Specifications within the ICPSR Codebook for additional sampling information. For each country a comparison between the sample and the universe was carried out. The Universe description was derived from Eurostat population data or from national statistics offices. For all countries surveyed, a national weighting procedure, using marginal and intercellular weighting, was carried out based on this Universe description. In all countries, gender, age, region and size of locality were introduced in the iteration procedure. For international weighting (i.e. EU averages), TNS Opinion and Social applies the official population figures as provided by EUROSTAT or national statistic offices. Please refer to the GESIS Documentation and Technical Specifications within the ICPSR Codebook for additional weighting information. Citizens of the EU aged 15 and over residing in the 27 EU member countries: Austria, Belgium, Bulgaria, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom, and the national population of citizens and the population of citizens of all the EU member countries aged 15 and over residing in the three EU candidate countries: Croatia, Turkey, and the Former Yugoslav Republic of Macedonia, and in the Turkish Cypriot Community. Smallest Geographic Unit: country ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Datasets: DS0: Study-Level Files DS1: GESIS DS2: ICPSR Eurobarometer Survey Series

The SAFE model (Sustainability Assessment by Fuzzy Evaluation, http://www.sustainability.tuc.gr/) estimates the the overall sustainability of countries using relevant indicator data. The files (compressed, tab-delimited text) contain data on 69 sustainability indicators for 164 countries from 1990 to 2016. Three data sets are provided: 1) raw data as time series (810 out of a total of 69x164=11,316 time series are missing); 2) time series transformed into single values and normalized on a 0-1 scale from unsustainable to sustainable; 3) SAFE model inputs: a data set in which most of the missing values are imputed; for some countries certain indicators are intentionally omitted, estimated, or modified as explained in the article. Third-party sources of raw data are given in the article's supplementary Appendix E.

#### Note: #### An updated version of these data including data on biofuels and fuels from waste is available [here](https://pub.uni-bielefeld.de/record/2950291). The extended version also offers a package of R-scripts that have been used to reproduce the statistical analysis presented in [Hötte, Pichler, Lafond (2021): The rise of science in low-carbon energy technologies](https://doi.org/10.1016/j.rser.2020.110654). This data publication offers data about low-carbon energy technology (LCET) patents and citations links to the scientific literature. This data publication contains different data sets (in .RData and (long-term archivable) .tsv format). Further information about each data set is provided in more detail below. - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID, Paper year, cited (binary 0-1, indicates whether the paper is cited by a patent). - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number, Patent year (grant year), CPC class, Patent date, Patent title, citing_to_science (binary 0-1, indicates whether the patent is citing to science). - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number, Patent year (grant year), Technology type, CPC class, Patent date, Patent title. - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing, cited (Patent numbers). - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number, Paper ID, Patent year, Paper year, Technology type, WoS field, Patent title, Paper title, DOI, Confidence Score, Citation type, Reference type, Journal/ Conf. name, Journal ID, Conference ID, CPC class, Patent date, US patent. ### License and terms of use ### This data is licensed under the CC BY 4.0 license. See: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode) Please find the full license text below. If you want to use the data, do not forget to give appropriate credit by citing this data publication and the following paper. Kerstin Hötte, Anton Pichler, François Lafond: *The rise of science in low-carbon energy technologies*, Renewable and Sustainable Energy Reviews, Volume 139, 2021 [https://doi.org/10.1016/j.rser.2020.110654](https://doi.org/10.1016/j.rser.2020.110654) ### LCET definition and concepts ### LCET are defined by Cooperative Patent Classification (CPC) codes. CPC offers "tags" that are assigned to patents that are useful for the adaptation and mitigation of climate change. LCET are identified by YO2E codes, i.e. that are assigned to technologies that contribute to the "REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION". Only the subset of Y02E01 ("Energy generation through renewable energy sources") and Y02E03 ("Energy generation of nuclear origin") technologies are used. 8 different LCET are distinguished: Solar PV, Wind, Solar thermal, Ocean power, Hydroelectric, Geothermal, Nuclear fission and Nuclear fusion. More information about the Y02-tags can be found in: Veefkind, Victor, et al. "A new EPO classification scheme for climate change mitigation technologies." World Patent Information 34.2 (2012): 106-111. DOI: [https://doi.org/10.1016/j.wpi.2011.12.004](https://doi.org/10.1016/j.wpi.2011.12.004) ### Data sources and compilation ### The data was generated by the merge of different data sets. 1.) Patent data from USPTO was downloaded here: https://bulkdata.uspto.gov/ 2.) Complementary data on grant year and patent title was taken from: https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data 3.) Citations to science come from the Reliance on Science (RoS) data set https://zenodo.org/record/3685972 (v23, Feb. 24, 2020) DOI: [10.5281/zenodo.3685972](10.5281/zenodo.3685972) The directory ("code") offers the R-scripts that were used to process MAG data and to link it to patent data. The header of the R-scripts offer additional technical information about the subsetting procedures and data retrieval. For more information about the patent data, see: Pichler, A., Lafond, F. & J, F. D. (2020), Technological interdependencies predict innovation dynamics, Working paper pp. 1–33. URL: [https://arxiv.org/abs/2003.00580](https://arxiv.org/abs/2003.00580) For more information about MAG data, see: Marx, Matt, and Aaron Fuegi. "Reliance on science: Worldwide front‐page patent citations to scientific articles." Strategic Management Journal 41.9 (2020): 1572-1594. DOI: [https://doi.org/10.1002/smj.3145](https://doi.org/10.1002/smj.3145) Marx, Matt and Fuegi, Aaron, Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. Boston University Questrom School of Business Research Paper No. 3331686. DOI: [http://dx.doi.org/10.2139/ssrn.3331686 ](http://dx.doi.org/10.2139/ssrn.3331686 ) ### Detailed information about the data ### - "all_papers.RData" : Data on scientific papers from Microsoft Academic Graph (MAG), 3 columns: Paper ID: Unique paper-identifier used by MAG Paper year: Year of publication cited: binary 0-1, indicates whether the paper is cited by a patent, citation links are made in the text body and front-page of the patent, and added by examiners and applicants. - "all_patents.RData" : Data on USPTO utility patents, 6 columns: Patent number: Number given by USPTO. Can be used for manual patent search in http://patft.uspto.gov/netahtml/PTO/srchnum.htm (numeric) Patent year: Year when the patent was granted (numeric) CPC class: Detailed 8-digit CPC code (numeric) Patent date: Exact date of patent granting (numeric) Patent title: Short title (character) citing_to_science: binary 0-1, indicates whether the patent is citing to science as identified by citation links in RoS. (numeric) - "LCET_patents.RData" : Subset of LCET patents, 6 columns: Patent number: (numeric) Patent year: (numeric) Technology type: Short code used to tag 8 different types of LCET (pv, (nuclear) fission, (solar) thermal, (nuclear) fusion, wind, geo(termal), sea (ocean power), hydro) (character) CPC class: Detailed 8-digit CPC code (character) Patent date: (numeric) Patent title: (numeric) - "LCET_patent_citations.RData" : Citations from LCET patents to other patents, 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (numeric) - "LCET_subset_with_metainfo_final.RData" : Citations from LCET patents to scientific papers from MAG, complemented by meta-information on patents and papers, 18 columns: Patent number: see above (numeric) Paper ID: see above (numeric) Patent year: see above (numeric) Paper year: see above (numeric) Technology type: see above (character) WoS field: Web of Science field of research, WoS fiels were probabilistically assigned to papers and are used as given by RoS (character) Patent title: see above (character) Paper title: Title of scientific article (character) DOI: Paper DOI if available (character) Confidence Score: Reliability score of citation link (numeric). Links were probabilistically assiged. See Marx and Fuegi 2019 for further detail. Citation type: Indicates whether citation made in text body of patent document or its front page (character) Reference type: Examiner or applicant added citation link (or unknown). (character) Journal/ Conf. name: Name of journal or conference proceeding where the cited paper was published (character) Journal ID: Journal identifier in MAG (numeric) Conference ID: Conference identifier in MAG (numeric) CPC class: see above (character) Patent date: see above (numeric) US patent: binary US-patent indicator as provided by RoS (numeric) #### Note: #### The citation links were probabilistically retrieved. During the analysis, we identified manually some false-positives are removed them from the "LCET_subset_with_metainfo_final.RData" data set. The list is available, too: "list_of_false_positives.tsv" We do not claim to have a perfect coverage but expect a precision of >98% as described by Marx and Fuegi 2019. ### Statistics about the data ### Full data set: - Number of papers in MAG: 179,083,029 - Number of all patents: 10,160,667 - Number of citing patents: 2,058,233 - Number of cited papers: 4,404,088 - Number of citation links from patents to papers: 34,959,193 LCET subset: - Number of LCET patents: 57,530 - Number of citing LCET patents: 16,674 - Number of cited papers: 53,509 - Number of citation links from LCET patents to papers: 151,253 - Number of citation links from LCET patents to other patents: 567,274 Meta-information: Papers: - Publication year, 251 Web-of-Science (WoS) categories, Journal/ conference proceedings name, DOI, Paper title Patents: - Grant year, >250,000 hierarchical CPC classes, 8 LCET types Citation links: - Reference type, citation type, reliability score #### If you have further questions about the data or suggestions, please contact: kerstin.hotte@oxfordmartin.ox.ac.uk ### License issues ### Terms of use of the source data: - Reliance on Science data [https://zenodo.org/record/3685972](https://zenodo.org/record/3685972), Open Data Commons Attribution License (ODC-By) v1.0, https://opendatacommons.org/licenses/by/1.0/ - "Google Patents Public Data” by IFI CLAIMS Patent Services and Google (https://cloud.google.com/blog/products/gcp/google-patents-public-datasets-connecting-public-paid-and-private-patent-data), Creative Commons Attribution 4.0 International License (CC BY 4.0), https://console.cloud.google.com/marketplace/details/google_patents_public_datasets/google-patents-public-data - USPTO patent data (https://bulkdata.uspto.gov/), see: https://bulkdata.uspto.gov/data/2020TermsConditions.docx

The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN). Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method. In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station.Measurements- Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.- Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4).- Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O.- Auxiliary micrometeorology and soil climate measurements.Data availabilityNear real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset.Data policyICOS data license: [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)Detailed site info: [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)

PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NOx, NMVOCs, NH3 and SO2) from anthropogenic sources in South America, for the period 2014–2016. The dataset was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGARv4.3.2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. The inventories are presented as NetCDF4 files, one for each species and year, gridded with a spatial resolution of 0.1° x 0.1° covering the domain 32° W–120° W and 34° N–58° S. Each file contains 12 variables corresponding to the emissions in Tg/y from the following categories, which are organized and denominated using the nomenclature given by CAMS: thermal power plants (ENE); residential and commercial combustion (RES); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries (including fuel consumption in manufacturing industries and construction, refineries, industrial processes and solvent and other products use) (IND); agricultural soils (AGS); agriculture livestock (AGL); inland navigation (SHP); international navigation (SHP-INT); waste (including solid waste, wastewater and incineration) (SWD); and the sum of all sectors (SUM).

Article Abstract To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.

This dataset is a journal article that describes the use of a system dynamics model to explore the synergies between transit and development strategies as they give rise to outcomes of community concern: environmental impacts, economic development and equity. The dataset includes the publication itself, the publication figures, the input and calibration data, the data dictionary and the model output data for the base scenarios.

We monitored golden-cheeked warbler territories in 10 plots within an urban preserve to determine abundance, delineate territories, and document breeding success. We determined environmental conditions across the study period to examine temporal and landscape effects. We then used these data to estimate adult survival and productivity and relate these vital rates to environmental conditions experienced during our study period. We used supported covariates to predict potential effects on this population 25 years into the future. These data and code are associated with the publication in Ecosphere entitled "Urban land cover and El Nino events negatively impact population viability of an endangered North American songbird." We performed an integrated population model to evaluate the effect of climate patterns and urban land cover on the viability of an endangered wood-warbler breeding in central Texas. We used territory monitroing data from 2011–2019 to predict viability of the population 25 years into the future. We assembled and conducted the analysis in R.