Eurofi 2022 Financial Forum While advances in financial technology that seek to enhance the efficiency, inclusiveness and quality of services should be welcomed, they will not replace the critical role of human judgment in banking and supervision. And they cannot substitute for the importance of ongoing cooperation among Basel Committee members with a view to safeguarding global financial stability.
Welcome and general introduction by Project Coordinator Jose Alfonso Gomez Calero, IAS-CSIC: -Actual potential of tree deficit irrigation, sensorization and differentiated spatial management for optimizing water use under droughts. Speaker: Juan Jose Alarcón. CEBAS-CSIC -Actual potential of conservation agriculture and green cover crops in the rotation for optimizing soil water retention in annual crops. Speaker: Tomas Dostal. CVTU -Cost Benefit Analysis and carbon/water footprint for specific agricultural systems across countries and farm typologies. Speaker: Gianni Quaranta. UNIBAS, MEDES. -Regional crop modelling for evaluating water use in agriculture. Speaker: Gabrielle de Lannoy. KU Leuven -Training and cooperation in large EU China projects, lessons learned. Speaker: Ian Dodd. ULANC -Key policy recommendations from SHui. Speaker: Rossana Salvia. UNIBAS, MEDES Round Table: Future of optimization of water use in agriculture. Drivers and identification of gaps in knowledge and implementation. Moderator: Jose Alfonso Gomez Calero. IAS-CSIC Participants: Miguel Barnuevo. Union de Pequeños Agricultores; Tim Hess. Cranfield University; Dirk Raes. KU Leuven and Mª Ferrer. FENACORE. SHui (Soil Hydrology research platform underpinning innovation) ran from September 2018 to August 2022 to address best use of soil and water in European and Chinese cropping systems via transdisciplinary research from plot to regional scales. Combining long-term experiments and modelling analysis at different scales evaluated the impact of Best Management Practices (BMPs) on water-limited crop productivity and soil retention, including socio-economic issues. The project also developed tools to facilitate implementation of soil and water saving technologies in specific farming situations. The objective of this in-person meeting in Brussels is to present the main project findings to stakeholders and policy makers, as well as to discuss in a round table the issues related to use of scarce water resources in agriculture. This project is co-funded by the European Commission within H2020 Framework Programme (Project: 773903). This project is co-funded by the Chinese Ministry of Science & Technology under CFM (China-EU Co-Funding Mechanism) Peer reviewed
The collated data correspond to the parameters to estimate the resources consumption and the emissions to the environment from the main farming activities (resources consumption and emissions from infrastructure building and management, fertiliser and pesticide production, on-field fertiliser and pesticide emissions, fuel consumption from machinery use, and irrigation), together with the environmental impacts of the unit processes (e.g. electricity mix, production of agricultural inputs) required to assess the environmental impacts from a panel of reference holdings at the main NUTS 2 in Spain. The authors gratefully acknowledge both the Universitat Politècnica de València for providing the funds for N.K. Sinisterra-Solís’s research contract through Subprogram 1 (PAID-01-18).
Unidad de excelencia María de Maeztu CEX2019-000940-M This dataset contains supporting information for "Quantitative link between sedimentary chlorin and sea-surface chlorophyll-a". The dataset consists of global oceanic biogeochemical data from sea-surface, water column and surface sediments. The dataset includes sedimentary chlorin and sea-surface chlorophyll concentration, total organic carbon content, oxygen concentration and mass accumulation rate, among other biogeochemical parameters.
Datasets accompanying the paper “Virtual Research Environments Ethnography: a Preliminary Study”, a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories. While for legal reasons we can not share the original datasets obtained by querying the databases, since they include copyrighted data, we can share the two datasets derived from the query results and the two topic modelling datasets. The dataset “main_dataset.csv” consists of the merged query results from ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink databases. It is structured into six columns: (i) doi; (ii) title; (iii) content_type; (iv) publication year; (v) keyword_search; (vi) DB. The ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘content_type’ label refers to the different and normalised typologies of resources: (a) Article; (b) Book, (c) Book Chapter; (d) Chapter; (e) Chapter ReferenceWorkEntry; (f) Conference Paper; (g) Conference Review; (h) Early Access Articles; (i) Editorial; (j) Erratum; (k) Letter; (l) Magazines; (m) Masters Thesis; (n) Note; (o) Ph.D. Thesis; (p) Retracted; (q) Review; (r) Short Survey; (s) Standards. (c) and (d) refer to the same type of entry (they are used in different databases), while in the case of (e) we observed that it is used in the Springer database to refer mainly to encyclopaedic entries. The ‘keyword_search’ label is used for identifying the keyword group used for formulating the query: (a) science gateway | scientific gateway; (b) virtual laboratory | Vlab; or (c) virtual research environment. The ‘DB’ label indicates the provenance of the entries from one of the five databases we selected for our study: (a) ACM; (b) IEEE; (c) ScienceDirect; (d) scopus; and (e) Springer, identifying the ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink respectively. The dataset “filtered_dataset.csv” consists of the deduplicated and filtered entries (journal articles and conference papers from 2010 onward, with a DOI assigned) from the “main_dataset.csv” we used as the final dataset for answering our research questions. It is structured into ten columns: (i) doi; (ii) title; (iii) venue; (iv) publication_year; (v) content_type; (vi) abstract; (vii) keywords; (viii) science gateway | scientific gateway; (ix) virtual laboratory | Vlab; and (x) virtual research environment. As for the previous dataset, the ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘venue’ label is used for indicating the conference or the journal the entries refer to. The values derive from the original query results. The ‘abstract’ and ‘keyword’ labels are used for the abstracts and the keywords associated with the entries. The values are mainly derived from the original query results, as we integrated the missing ones by querying OpenAIRE. The ‘science gateway | scientific gateway’, ‘virtual laboratory | Vlab’ and ‘virtual research environment’ labels indicate the connection between the entries and the keyword group used for denoting them. The values are binary (1 if the keywords belong to the group, 0 if they do not). The datasets “sg_vlab_vre_topics_datasets.csv” and “sgvlabvre_topics_dataset.csv” consist of the three datasets and of the unique dataset resulting from topic modelling, the first (corpus divided into three datasets) and the second analysis (corpus as a whole) respectively. They share the same structure: (i) Topic; (ii) #studies; (iii) Representative word; (iv) Representative word weight. The ‘Topic’ label is used for the topic denomination and the values consist of an alphanumeric string indicating the dataset and the progressive topic number: (a) SG, for the scientific gateway dataset; (b) VRE, for the virtual research environment dataset; (c) VLAB, for the virtual laboratory dataset; and (d) A, for the corpus as a whole. The ‘#studies’ label indicates the number of studies contributing to each topic. The ‘Representative word’ and ‘Representative word weight’ labels are used for denoting the keywords describing each topic and their weights respectively.
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