Institution
Technical University of Madrid
Education•Madrid, Spain•
About: Technical University of Madrid is a education organization based out in Madrid, Spain. It is known for research contribution in the topics: Population & Ontology (information science). The organization has 16613 authors who have published 34759 publications receiving 634043 citations. The organization is also known as: UPM & Polytechnical University of Madrid.
Topics: Population, Ontology (information science), Finite element method, European union, Solar cell
Papers published on a yearly basis
Papers
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TL;DR: The latest version of STRING more than doubles the number of organisms it covers, and offers an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input.
Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
10,584 citations
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Technical University of Madrid1, Stanford University2, Elsevier3, VU University Amsterdam4, National Institutes of Health5, University of Leicester6, Harvard University7, Beijing Genomics Institute8, Maastricht University9, Wageningen University and Research Centre10, University of Oxford11, Heriot-Watt University12, University of Manchester13, University of California, San Diego14, Leiden University Medical Center15, Leiden University16, Federal University of São Paulo17, Science for Life Laboratory18, Bayer19, Swiss Institute of Bioinformatics20, Cray21, University Medical Center Groningen22, Erasmus University Rotterdam23
TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
Abstract: There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
7,602 citations
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T. Prusti1, J. H. J. de Bruijne1, Anthony G. A. Brown2, Antonella Vallenari3 +621 more•Institutions (93)
TL;DR: Gaia as discussed by the authors is a cornerstone mission in the science programme of the European Space Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach.
Abstract: Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page.
5,164 citations
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TL;DR: In this article, the authors used a Bayesian hierarchical model to estimate trends in diabetes prevalence, defined as fasting plasma glucose of 7.0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs in 200 countries and territories in 21 regions, by sex and from 1980 to 2014.
2,782 citations
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TL;DR: This work offers a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.
2,669 citations
Authors
Showing all 16935 results
Name | H-index | Papers | Citations |
---|---|---|---|
Stefano Boccaletti | 60 | 348 | 25776 |
Xin Chen | 60 | 955 | 22412 |
Marcela González-Gross | 59 | 356 | 15681 |
Manuel Doblaré | 59 | 313 | 11048 |
De-Yi Wang | 58 | 269 | 9799 |
Antonio Luque | 56 | 332 | 16740 |
Luis Gil | 56 | 321 | 10089 |
Philipp Cimiano | 56 | 334 | 12612 |
Antonio Hernando | 55 | 599 | 15614 |
Manuel Elices | 55 | 197 | 7688 |
Andres Cuevas | 54 | 307 | 14127 |
Asunción Gómez-Pérez | 54 | 312 | 15547 |
Pedro Larrañaga | 53 | 339 | 17737 |
David J. Connor | 53 | 218 | 9139 |
Javier Ortega-Garcia | 53 | 257 | 10210 |