Resultados totales (Incluyendo duplicados): 33743
Encontrada(s) 3375 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285761
Dataset. 2019

FLOOD MASKS DOÑANA 1984/2019

  • Díaz-Delgado, Ricardo
  • Afán, Isabel
  • Aragonés, David
  • García, Diego
  • Bustamante, Javier
Time Series of flooded areas derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, these products and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/ Methodology is described in this paper: Remote Sensing 8(9):775 · September 2016. DOI: 10.3390/rs8090775, European Commission: ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762), Peer reviewed

Proyecto: EC/H2020/641762
DOI: http://hdl.handle.net/10261/285761
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285761
HANDLE: http://hdl.handle.net/10261/285761
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285761
PMID: http://hdl.handle.net/10261/285761
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285761
Ver en: http://hdl.handle.net/10261/285761
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285761

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285763
Dataset. 2021

DATASETS AND SUPPORTING MATERIALS FOR THE IPIN 2021 COMPETITION TRACK 3 (SMARTPHONE-BASED, OFF-SITE)

  • Torres-Sospedra, Joaquín
  • Aranda Polo, Fernando J.
  • Parralejo, Felipe
  • Bellavista Parent, Vladimir
  • Álvarez, Fernando J.
  • Pérez-Navarro, Antoni
  • Jiménez Ruiz, Antonio R.
  • Seco Granja, Fernando
This package contains the datasets and supplementary materials used in the IPIN 2021 Competition. Contents: IPIN2021_Track03_TechnicalAnnex_V1-02.pdf: Technical annex describing the competition 01-Logfiles: This folder contains a subfolder with the 105 training logfiles, 80 of them single floor indoors, 10 in outdoor areas, 10 of them in the indoor auditorium with floor-trasitio and 5 of them in floor-transition zones, a subfolder with the 20 validation logfiles, and a subfolder with the 3 blind evaluation logfile as provided to competitors. 02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 82 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results., We would like to thank GISS Research team at University of Extremadura for their support in colllecting the datasets and the ISTI-CNR for managing the competition and find sponsors for the winner's award. We are also grateful to Francesco Potortì, Sangjoon Park and the ISTI-CNR team for their invaluable committment in organizing and promoting the IPIN competition and conference. This work has been done within the framework of the IPIN 2021 Conference in Lloret de Mar, organised by A. Pérez-Navarro, R. Montoliu and J. Torres-Sospedra. Parts of this work were carried out with the financial support received from projects and grants: - A-WEAR (H2020-MSCA-ITN-2018, Grant Agreement 813278) - INSIGNIA (PTQ2018-009981) - REPNIN+ network (TEC2017-90808-REDT) - LORIS (TIN2012-38080-C04-04) - SmartLoc(CSIC-PIE Ref.201450E011) - TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER) - MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), Peer reviewed

DOI: http://hdl.handle.net/10261/285763
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285763
HANDLE: http://hdl.handle.net/10261/285763
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285763
PMID: http://hdl.handle.net/10261/285763
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285763
Ver en: http://hdl.handle.net/10261/285763
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285763

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285766
Dataset. 2022

UROLITHINS: AN UPDATE ON THEIR METABOLISM, BIOACTIVITY AND ASSOCIATED GUT MICROBIOTA

  • García-Villalba, Rocío
  • Giménez-Bastida, J. A.
  • Cortés-Martín, Adrián
  • Ávila-Gálvez, María Ángeles
  • Tomás Barberán, Francisco
  • Selma, María Victoria
  • Espín de Gea, Juan Carlos
  • González-Sarrías, Antonio
Urolithins, metabolites produced by the gut microbiota from the polyphenols ellagitannins and ellagic acid, are discovered by the research group in humans almost 20 years ago. Pioneering research suggests urolithins as pleiotropic bioactive contributors to explain the health benefits after consuming ellagitannin-rich sources (pomegranates, walnuts, strawberries, etc.). Here, this study comprehensively updates the knowledge on urolithins, emphasizing the review of the literature published during the last 5 years. To date, 13 urolithins and their corresponding conjugated metabolites (glucuronides, sulfates, etc.) have been described and, depending on the urolithin, detected in different human fluids and tissues (urine, blood, feces, breastmilk, prostate, colon, and breast tissues). There has been a substantial advance in the research on microorganisms involved in urolithin production, along with the compositional and functional characterization of the gut microbiota associated with urolithins metabolism that gives rise to the so-called urolithin metabotypes (UM-A, UM-B, and UM-0), relevant in human health. The design of in vitro studies using physiologically relevant assay conditions (molecular forms and concentrations) is still a pending subject, making some reported urolithin activities questionable. In contrast, remarkable progress has been made in the research on the safety, bioactivity, and associated mechanisms of urolithin A, including the first human interventions., European Commission: PolyBiota - Polyphenols and Gut Microbiota interaction in Cardiovascular Health (838991), Peer reviewed

Proyecto: EC/H2020/838991
DOI: http://hdl.handle.net/10261/285766
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285766
HANDLE: http://hdl.handle.net/10261/285766
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285766
PMID: http://hdl.handle.net/10261/285766
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285766
Ver en: http://hdl.handle.net/10261/285766
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285766

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285767
Dataset. 2022

DATA FOR: REPRESENTATION IN SEA TURTLE SCIENCE: SLOW PROGRESS TOWARDS GENDER EQUITY AND GLOBALIZATION REVEALED FROM THIRTY YEARS OF SYMPOSIUM ABSTRACTS

  • Robinson, Nathan J.
  • Mills, Sophie
  • St.Andrews, Laura
  • Sundstrom, Allegra
  • Thibodeau, Jadyn
  • Yaney-Keller, Adam
  • Gatto, Christopher R.
Methods Data Source The first ISTS occurred in 1980 and has been repeated annually until 2020, when subsequent events were put on hold due to the COVID-19 pandemic. The first seven meetings were based around informal discussions and meetings. Since 1988, however, the event has followed a more structured format based around several days of short presentations (8-20 mins each). The “Book of Abstracts” from these presentations are published after each symposium and made available at https://www.internationalseaturtlesociety.org/publications/proceedings/ or https://repository.library.noaa.gov/. We gleaned data from every abstract presented in the annual Book of Abstracts between the 8th ISTS in 1988 and the 30th in 2018 (for full references see Supplementary Materials 1). However, we did not compile any data from the 32nd, 35th, 37th, and 39th ISTS as the official Book of Abstracts for these events were not publicly available at the time of writing this manuscript. We only report on data that was shared within these Books of Abstracts and did not access any additional personal information. Furthermore, no specific individuals or institutions will be named in this manuscript. From each manuscript, we extracted the following information: (1) whether the abstract was for an oral or a poster presentation, (2) the number of authors per abstract, (3) the inferred gender of the first author and last author using their first name, (4) the category (defined below) of the first author’s affiliated institution, and (5) the location of both the author’s affiliated institution as well as the location where the study took place. We will expand on each of these topics in the sub-headers below. Oral or poster In the database, we recorded the location of the event, the total number of presentations per year, and whether each abstract was given as either an oral or a poster presentation. Presentations are occasionally made in other formats, such as video presentations, but for consistency these were not included. We also did not include any key-note presentations. Number of authors per abstract We recorded the total number of authors, including the first author, on each abstract. When authors listed a collective of individuals under a single heading (e.g. J.J. Jamieson and volunteers), we counted the collective as a single individual. Inferred gender of first and last author We inferred the gender of the first and last author by consulting the Oxford Dictionary of First Names (Hanks et al. 2006), which indicated whether these names were predominantly used for male or females. For example, Alexander would be listed as male, Alexandra would be listed female, and Alex would not have a categorized gender. If the author had initialized their first name, we used their next listed name as long as it was not their final name (i.e. J. Jonah Jameson would be shortened to Jonah Jameson but J. J. Jameson would not be categorized) or if the author’s full name appeared elsewhere in our dataset. For those names that either did not appear in the Oxford Dictionary of First Names or were categorized as being unisex names, we used the platform Gender-API (https://gender-api.com/) to determine the most likely gender. Names that could not be categorized by Gender-API or were given a 50 % probability of being either gender were left as non-categorized. We acknowledge that gender is not binary, and that the gender inferred via author’s name may not match the author’s self-identified gender. Consequently, this study’s representation of gender lacks complexity and will not represent the holistic array of genders attending each ISTS. Nevertheless, we believe this simplification can still provide insightful details about gender representation at the ISTS. Affiliated institution of the first author We recorded and categorized the affiliated institution for all first authors. When the author listed more than one affiliation, we only used the first one that was listed. We assigned each affiliated institution to one of the following five categories. (1) Academic – this included all traditional education institutions, both public and private, such as high schools, colleges, and universities (e.g., Oxford University, Duke University). (2) Governmental – this included any local, federal, or national governmental entities. This also included governmental run initiatives such as National Park services and government funded research centers (e.g., National Oceanographic and Atmospheric Administration run institutes in the USA or Consejo Superior de Investigaciones Científicias in Spain). (3) Non-profit – this included all non-profits, charities, and non-commercial organizations as well as research centers/institutes that are not directly affiliated with universities or colleges (e.g., the Sea Turtle Conservancy). The decision was made to include non-academic research centers in this category because they are frequently part of a broader non-profit (e.g., the Cape Eleuthera Institute in The Bahamas is part of the Cape Eleuthera Foundation). (4) Industry – including all for-profit institutions such as consulting agencies, aquariums, and private museums (e.g., the New England Aquarium). Museums associated with academic institutions were listed under the associated academic institution (e.g., the Peabody Museum of Natural History was considered part of Yale University and thus included in the academic category). (5) Unknown / Not Listed – This was used when no affiliation was listed, or it was not possible to assign the affiliation to one of the previously listed categories with certainty. Institutional and study site We determined the geographic location where the study took place (hereafter referred to as the study site) as well as the location of the author’s institutional affiliation (hereafter referred to as the institutional site). As each abstract could only have a single affiliation (see previous section), this meant that it also could only have a single institutional site. In contrast, it was possible that the research was conducted in multiple locations and a single abstract could have several study sites. We defined the study site as the area where the sampling took place and not where the analyses were conducted. For example, if samples were collected from turtles in Costa Rica but then exported to the U.S.A. for analysis, the study site would remain listed as Costa Rica and not the U.S.A. Similarly, if samples were collected / bio-logging devices were placed on turtles in one country but the turtles migrated into the waters of another country, only the country where the samples were collected / devices were deployed was listed. We only recorded the location of the study if it could be deduced with certainty. When possible, we defined the study and institutional site to the level of both country and continent. When determining countries, we used political borders. Thus, all territories, islands, or dependencies were listed as part of their broader country. For example, the U.S. Virgin Islands were part of the U.S.A and French Guyana was part of France. In contrast, when determining continents, we used geographic borders. This meant that territories, islands, or dependencies may be listed as being in a separate continent to the principal country (for guidance see Supplementary Material 2). For example, the U.S. Virgin Islands were part of Central America and the Caribbean and French Guyana was part of South America. The definition of continent is not fixed and varies worldwide and so we delineated the continents, following the United Nations global geoscheme (UNSD 2022), as follows: Africa, Antarctica, Asia, Central America and the Caribbean, Europe, North America, Oceania, and South America. Each country was only considered to be part of a single continent following designations in Supplementary Materials 3. As a single abstract could have study sites in several countries, when calculating the representation of each continent we considered that an abstract with a single study site was counted as 1. However, if the abstract had study sites in multiple continents, then representation was divided between those continents. E.g., a study in both Asia and Europe would be counted at 0.5 for each continent. When studies stated that they occurred in a particular region, we still assigned them a continent even if they did not state specific countries. However, when studies stated that they were global, we did not assign continents or countries if neither nor any specific region was stated specifically in the abstract., Sea turtles are a circumglobal taxon that receive considerable research attention, yet there is little information about the demographics of sea turtle researchers. To assess long-term trends in demographic, geographic, and institutional representation within the sea turtle community, we quantified information from 7041 abstracts presented at the International Sea Turtle Symposium (ISTS) between 1988–2018. The dataset from this study is presented here., Severo Ochoa Centre of Excellence, Award: CEX2019-000928-S, Peer reviewed

DOI: http://hdl.handle.net/10261/285767
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285767
HANDLE: http://hdl.handle.net/10261/285767
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285767
PMID: http://hdl.handle.net/10261/285767
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285767
Ver en: http://hdl.handle.net/10261/285767
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285767

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285768
Dataset. 2017

DAILY GRIDDED DATASETS OF SNOW DEPTH AND SNOW WATER EQUIVALENT FOR THE IBERIAN PENINSULA FROM 1980 TO 2014

  • Alonso-González, Esteban
  • López-Moreno, Juan I.
  • Gascoin, Simon
  • García-Valdecasas Ojeda, Matilde
  • Sanmiguel-Vallelado, Alba
  • Navarro‐Serrano, Francisco
  • Revuelto, Jesús
  • Ceballos-Barbancho, Antonio
  • Esteban-Parra, María Jesús
  • Essery, Richard
We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 ×10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management ., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285768
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285768
HANDLE: http://hdl.handle.net/10261/285768
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285768
PMID: http://hdl.handle.net/10261/285768
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285768
Ver en: http://hdl.handle.net/10261/285768
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285768

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285769
Dataset. 2019

METEOROLOGICAL & OCEANOGRAPHIC DATA FROM NW GALICIAN COAST

  • Álvarez-Salgado, Xosé Antón
  • Fuentes-Santos, I.
  • Otero, Jaime
File containing time series of daily values of coastal winds, upwelling index (offshore Ekman transport), continental runoff, solar irradiance and sea surface temperature for selected sites in the NW Galician coast (NW Spain). These data can be freely downloaded from the web sites of the Galician Meteorological Agency MeteoGalicia (http://www.meteogalicia.es), the Instituto Español de Oceanografía (http://www.indicedeafloramiento.ieo.es) and ICOADS (http://icoads.noaa.gov). We just put them together in a single file. The file includes metadata indicating units, the position of the selected stations and the origin of the data, European Commission: ClimeFish - Co-creating a decision support framework to ensure sustainable fish production in Europe under climate change (677039), Peer reviewed

Proyecto: EC/H2020/677039
DOI: http://hdl.handle.net/10261/285769
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285769
HANDLE: http://hdl.handle.net/10261/285769
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285769
PMID: http://hdl.handle.net/10261/285769
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285769
Ver en: http://hdl.handle.net/10261/285769
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285769

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285773
Dataset. 2022

DATA FROM PATI CIENTIFIC 2022-04-27 TD-5

  • Carrasco, Oriol
  • Bardají, Raúl
  • Vallès Casanova, Ignasi Berenguer
  • Pelegrí, Josep Lluís
  • Hoareau, Nina
  • Salvador, Joaquín
  • Simon, Carine
  • Rodero García, Carlos
  • Piera, Jaume
  • Ortigosa Barragán, Inma
  • Mateu, Jordi
  • Castells-Sanabra, Marcel·la
  • Barberan, Victor
  • González Fernández, Óscar
  • Puigdefàbregas, Joan
  • Yannoukakou, Iphygenia
  • Carretero, Igor
  • Verger, Elisabet
Sea temperature vs depth measured by the Pati Cientific at Somorrostro, Barcelona, on 2022-04-27., BIT Habitat, Barcelona Science Plan 2019, MONOCLE and EMSO - Laboratorios Submarinos Profundo, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285773
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285773
HANDLE: http://hdl.handle.net/10261/285773
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285773
PMID: http://hdl.handle.net/10261/285773
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285773
Ver en: http://hdl.handle.net/10261/285773
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285773

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285779
Dataset. 2022

DATA FROM PATI CIENTIFIC 2022-03-10 TD-3

  • Carrasco, Oriol
  • Bardají, Raúl
  • Vallès Casanova, Ignasi Berenguer
  • Pelegrí, Josep Lluís
  • Hoareau, Nina
  • Salvador, Joaquín
  • Simon, Carine
  • Rodero García, Carlos
  • Piera, Jaume
  • Ortigosa Barragán, Inma
  • Mateu, Jordi
  • Castells-Sanabra, Marcel·la
  • Barberan, Victor
  • González Fernández, Óscar
  • Puigdefàbregas, Joan
  • Yannoukakou, Iphygenia
  • Carretero, Igor
  • Verger, Elisabet
Sea temperature vs depth measured by the Pati Cientific at Somorrostro, Barcelona, on 2022-03-10., BIT Habitat, Barcelona Science Plan 2019, MONOCLE and EMSO - Laboratorios Submarinos Profundo, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285779
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285779
HANDLE: http://hdl.handle.net/10261/285779
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285779
PMID: http://hdl.handle.net/10261/285779
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285779
Ver en: http://hdl.handle.net/10261/285779
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285779

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285785
Dataset. 2021

HUMAN STUDIES CARRIED OUT WITH POMEGRANATE-BASED PRODUCTS (JUICE AND EXTRACT)

  • Giménez-Bastida, J. A.
  • Ávila-Gálvez, María Ángeles
  • Espín de Gea, Juan Carlos
  • González-Sarrías, Antonio
The consumption of pomegranate juices and extracts has long been linked to many health benefits beyond nutrition, described mainly by innumerable preclinical studies. However, the European Food Safety Authority (EFSA) concluded in 2010 that a cause and effect relationship could not be established between the consumption of pomegranate-derived products and all the health claims presented. There are no additional EFSA opinions on health claims specifically addressed to pomegranate in the last decade. Scope and approach This review comprehensively compiles all human studies conducted on pomegranate. The aim is to discuss these studies critically to identify possible flaws and propose guidelines that might help establish a cause and effect relationship between pomegranate-derived product consumption and health. Key findings and conclusions To date, 86 human studies have evaluated the health benefits of pomegranate juices and extracts. The most promising, albeit scarce, evidence is related to blood pressure improvement. Less evidence deals with inflammation, cancer, cognitive function, physical activity, and gut microbiota modulation (prebiotic effects). After a decade since EFSA's opinion, human evidence remains inconsistent, making it difficult to support most claimed health effects. The lack of effects and(or) data discrepancy might be attributable to design limitations, including insufficient product characterization and interindividual variability that influence pomegranate polyphenols' bioefficacy. New coordinated strategies between policy makers, research/academic institutions, and industry are needed to move forward. Therefore, this review presents a roadmap to conduct well-designed trials and cover existing gaps, which could establish a cause-effect relation between pomegranate consumption and health benefits beyond nutrition., European Commission: PolyBiota - Polyphenols and Gut Microbiota interaction in Cardiovascular Health (838991), Peer reviewed

Proyecto: EC/H2020/838991
DOI: http://hdl.handle.net/10261/285785
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285785
HANDLE: http://hdl.handle.net/10261/285785
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285785
PMID: http://hdl.handle.net/10261/285785
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285785
Ver en: http://hdl.handle.net/10261/285785
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285785

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285789
Dataset. 2022

ANNUAL CROP YIELDS OF THE CS1 STUDY CASE

  • Almagro, María
  • Díaz-Pereira, Elvira
  • Martínez-Mena García, M. Dolores
  • Boix-Fayos, Carolina
Database on main crop and secondary crop yields of the CS1 Study case for the period 2019-2021, European Commission: Diverfarming - Crop diversification and low-input farming across Europe: from practitioners engagement and ecosystems services to increased revenues and chain organisation (728003), Peer reviewed

Proyecto: EC/H2020/728003
DOI: http://hdl.handle.net/10261/285789
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285789
HANDLE: http://hdl.handle.net/10261/285789
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285789
PMID: http://hdl.handle.net/10261/285789
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285789
Ver en: http://hdl.handle.net/10261/285789
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285789

Buscador avanzado