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The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

This data publication offers updated data about low-carbon energy technology (LCET) patents and citations links to the scientific literature. Compared to a [previous version](https://doi.org/10.4119/unibi/2941555), it also contains data on biofuels and fuels from waste technologies. The updated version also contains the code (R-scripts) that have been used to (1) compile the data and (2) to reproduce the statistical analysis including figures and tables presented in the final paper Hötte, Pichler, Lafond (2021): "The rise of science in low-carbon energy technologies", RSER. DOI: [10.1016/j.rser.2020.110654](10.1016/j.rser.2020.110654). 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. - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing, cited (Patent numbers). Moreover, this data publication contains a folder "code" with 2 subfolders: - "R_code_create_data" contains the R-scripts used to create the data sample. - "R_code_plots_and_figures" contains all R-scripts used to make the statistical analyses presented in the text (including figures and tables). Please check the read-me documents in the code folder for further detail. ### 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 Please find the full license text below. If you want to use the data, do not forget to give appropriate credit by citing this article: 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 ### 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 chagen. 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"), Y02E03 ("Energy generation of nuclear origin") and Y02E5 ("Technologies for the production of fuel of non-fossil origin") technologies are used. 10 different LCET are distinguished: Solar PV, Wind, Solar thermal, Ocean power, Hydroelectric, Geothermal, Biofuels, Fuels from waste, 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 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 10 different types of LCET (pv, (nuclear) fission, (solar) thermal, (nuclear) fusion, wind, geo(termal), sea (ocean power), hydro, biofuels, (fuels from) waste) (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 fields 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 assigned. 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) - "patent:citations.RData": Patent citations among all patents (not only LCET), 2 columns: citing: Number of citing patent (numeric) cited: Number of cited patent (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: - #papers in MAG: 179,083,029 - #all patents: 10,160,667 - #citing patents: 2,058,233 - #cited papers: 4,404,088 - #citation links from patents to papers: 34,959,193 LCET subset: - #LCET patents: 65,305 - #citing LCET patents: 22,017 - #cited papers: 103,645 - #citation links from LCET patents to papers: 396,504 Meta-information: Papers: - Publication year, 251 Web-of-Science (WoS) categories, Journal/ conference proceedings name, DOI, Paper title Patents: - Grant year, >240,000 hierarchical CPC classes, 10 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** ### Acknowledgements ### The authors want to thank the Center for Research Data Management of Bielefeld University and in particular Cord Wiljes for excellent support. ### 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 PV-Live dataset comprises data from a network of 40 solar irradiance measurement stations across the German state of Baden-Württemberg. All stations measure global horizontal irradiance and temperature with a pyranometer, and global tilted irradiance in east, south, and west direction with tilt angles of 25° with three photovoltaic reference cells in minute resolution. A quality control scheme has been developed specifically for this dataset and is applied to the measurements before publication. The minute resolution irradiance and temperature measurements are published with the derived quality flags. A description of the dataset and the quality control scheme is given in Lorenz et al. (2022) and Lorenz et al. (2020). The dataset contains data from September 2020 onwards. It will be continuously extended by adding data of the previous month on a monthly basis. QUALITY FLAGS Two types of flags are provided with the data. Each sensor is assigned a general quality flag, which is based on a combination of different tests. In addition, a shading flag is given, which is not sensor specific, i.e. it applies to all irradiance sensors simultaneously. The flags can have the levels 'passed, all tests complete', 'passed, not all tests complete', 'failed tests, likely erroneous' or 'failed tests, most likely erroneous'. If the flag level of the general quality flags is 'failed tests, most likely erroneous' the corresponding measurement value is set to NaN. DATA FORMAT The data is published as monthly .zip archives. Each archive contains the following files: 1. Tab separated data files (tng000XX_YYYY-MM.tsv) for each station, containing measurements and quality flags of one month 2. Tab separated station location metadata (metadata_stations_YYYY-MM.tsv) 3. Metadata of the dataset describing variable names and quality flag levels (metadata_measurements.json) 4. General comments on data availability and quality for the month (comments_quality_control_YYYY-MM.txt) 5. Log file of changes (change_log_YYYY-MM.txt) Station location metadata is given on a monthly basis because stations can be relocated. Therefore, we recommend to use the metadata valid for its corresponding month. VERSION UPDATES Version 7: - Recalculation of height information for all stations - One station has been moved to a new, nearby location on 16th September 2021 ACKNOWLEDGEMENTS The data have been collected and processed by Fraunhofer ISE in the framework of PV-Live, a project in cooperation with TransnetBW. We thank our station partners for cooperation in installing and maintaining our measurement stations: EnBW Solar, Badenova, Pohlen Solar, Oekogeno Solar7, Ecovision, Hochschule Ulm, Hofgemeinschaft Heggelbach, Soltechnics-solution and the Stadtwerke Karlsruhe, Grünstadt, Buchen, Crailsheim, Schwäbisch Hall, Pforzheim, Konstanz, Waldshut-Tiengen, Schwäbisch Gmünd, Ravensburg, Eberbach, Baden-Baden.

Complete replication data and code for article "A Meta-Analysis of Country Level Studies on Environmental Change and Migration". The rdata file contains both the meta and country level data. The data is also saved separately as xlsx files.

A total of 256 surveys were analyzed for Caquetá and 227 surveys forYurimaguas. These surveys belong to the project "Sustainable Amazonian Landcapes" ( see below Related Datasets). All the households that participated in the survey did it freely and under prior informed consent. Households were asked about their farms’ agrobiodiversity (crops, cattle ranching, and pastoral diversity), farming activities, agroecological management practices and livelihood assets. These surveys provided the information required for defining the household typologies based on farming livelihood diversification and to obtain indicators of vulnerability to climate change. People surveyed usually were household heads or their spouses. The supervisors were the direct link with CIAT. The data was collected through Android Devices using the software CsPro 6.2 and 6.3. (Reporte Interno)

Tree cores were sampled using increment borers. At each site three trees were chosen for coring, with two or three cores taken per tree. Cores were sanded and ring widths measured based on high-resolution images of the sanded cores. Cores were cross-dated and summary statistics used to compare cross-dating accuracy. The dataset contains the resulting dated ring width series. This dataset includes tree ring width data, derived from tree cores, that were sampled from sites across the Rhön Biosphere Reserve (Germany). At each chosen site three trees were cored, with two or three cores taken per cored tree. Data was collected in August 2021.

Shapefile data set estimating trends of mean annual terrestrial surface air temperature (°C) and mean annual total precipitation (mm) for urban land, characterised by clusters of local spatial autocorrelation in regard to the age of urban area and the coefficient of variation of urban area extent over time.

A lack of adequate high resolution climate proxy records for the Last Glacial Maximum (LGM) has prevented the extrapolation of climate–solar linkages on centennial time scales prior of the Holocene. Therefore, it is still unknown whether centennial climate variations of the last ten thousand years convey a universal climate change or merely represent a characteristic of the Holocene. Recently published high resolution climate proxy records for the LGM allowed us to extrapolate climate–solar linkages on centennial time scales ahead of the Holocene. Here we present the analysis of a high resolution pollen concentration record from Lake Kotokel in southern Siberia, Russia, during the LGM. The record reflects the dynamics of vegetation zones and temperature change with a resolution of ~ 40 years in the continental climate of north-eastern Asia. We demonstrate that our pollen concentration record, the oxygen isotope δ18O record from the Greenland ice core project NGRIP (NorthGRIP), the dust-fall contributions in Lake Qinghai, China, grain size in the Gulang and Jingyuan loess deposits, China, and the composite oxygen isotope δ18O record from the Alpine cave system 7H reveal cooler to warmer climate fluctuations between ~ 20.6 and 26 ka. Such fluctuations correspond to the ~ 1000-yr, 500-600-yr and 210-250-yr cycles possibly linked to the solar activity variations and recognized in high resolution Holocene proxies all over the world. We further show that climate fluctuations in the LGM and Holocene are spectrally similar suggesting that linkages between climate proxies and solar activity at the centennial time scale in the Holocene can be extended to the LGM. {"references": ["Vadim A. Kravchinsky, Rui Zhang, Ryan Borowiecki, Pavel E. Tarasov, Mirko van der Baan, Taslima Anwar, Avto Goguitchaichvili, Stefanie M\u00fcller, 2021. Centennial scale climate oscillations from southern Siberia in the Last Glacial Maximum. Quaternary Science Reviews, in press."]}