Resultados totales (Incluyendo duplicados): 45603
Encontrada(s) 4561 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217049
Dataset. 2019

DATA FOR: SHORT-TERM EFFECTS OF PARTICULATE MATTER AND DESERT DUST EPISODES ON DAILY MORTALITY DURING IN IRAN

  • Querol, Xavier
  • Yarahmadi, Maryam
  • Hadei, Mostafa
  • Namvar, Zahra
  • Hashemi Nazari, Seyed Saeed
  • Shahsavani, Abbas
  • Stafoggia, Massimo
  • Emam, Baharan
  • Khosravi, Ardeshir
  • Tobías, Aurelio
Daily PM10 and PM2.5 concentrations in µg/m3 used in this study for Ahvaz and Tehran, mortality data can not be supplied because there is a confidentiality agreement for the use, Daily PM10 and PM2.5 concentrations in µg/m3 used in this study for Ahvaz and Tehran, mortality data can not be supplied because there is a confidentiality agreement for the use, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/217049
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217049
HANDLE: http://hdl.handle.net/10261/217049
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217049
PMID: http://hdl.handle.net/10261/217049
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217049
Ver en: http://hdl.handle.net/10261/217049
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217049

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106
Dataset. 2020

CALIBRATION SOFTWARE AND DATA SETS USED IN: "MULTI-SENSOR DATA FUSION CALIBRATION IN IOT AIR POLLUTION PLATFORMS" PAPER

  • Ferrer-Cid, Pau
  • Barceló-Ordinas, José María
  • García Vidal, Jorge
  • Ripoll, Anna
  • Viana, Mar
The data folder is includes the five different data sets used in the paper along with a metadata file, where the different features are explained., This dataset contains python scripts to calibrate tropospheric ozone sensor data obtained in the H2020 Captor project using sensor fusion techniques. Four different models are implemented; Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN),Random Forest(RF) and Support Vector Regression (SVR). The methodology consists of first applying the PLS procedure to derive orthogonal components (to avoid multicollinearity problems). Afterwards, the components are used as features in the machine learning algorithms, so the models are trained. The scripts available in this repository have been used in the elaboration of the paper: "Multi-sensor data fusion calibration in IoT air pollution platforms" submitted to the IEEE Internet of Things journal., National Spanish funding; Regional Project; EU H2020 CAPTOR Project; AGAUR SGR44; 10.13039/501100011033-Agencia Estatal de Investigación; Spanish Ministry of Economy, Industry and Competitiveness, Peer reviewed

Proyecto: EC/H2020/688110
DOI: http://hdl.handle.net/10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106
HANDLE: http://hdl.handle.net/10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106
PMID: http://hdl.handle.net/10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106
Ver en: http://hdl.handle.net/10261/217106
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217106

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107
Dataset. 2019

DATA USED IN PAPER "A COMPARATIVE STUDY OF CALIBRATION METHODS FOR LOW-COST OZONE SENSORS IN IOT PLATFORMS"

  • Ferrer-Cid, Pau
  • Barceló-Ordinas, José María
  • García Vidal, Jorge
  • Ripoll, Anna
  • Viana, Mar
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR)., Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR)., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107
HANDLE: http://hdl.handle.net/10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107
PMID: http://hdl.handle.net/10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107
Ver en: http://hdl.handle.net/10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217206
Dataset. 2020

CANTARERO ET AL 2020 EVOLUTION REPOSITORY DATA

  • Cantarero, Alejandro
  • Mateo, Rafael
  • Camarero, Pablo R.
  • Alonso, Daniel
  • Fernández-Eslava, Blanca
  • Alonso-Álvarez, Carlos
Dataset (excel) containing the data used in the analyses of the accepted manuscript entitled: Testing the shared-pathway hypothesis in the carotenoid-based coloration of red crossbills. The article will be published in Evolution., Descriptions of each variable are included as comments on the name of the variable (first row). Readme file added., This is a dataset that allowed testing the shared-pathway hypothesis on the honest signaling based on animal colorations created by red ketocarotenoid pigments. The dataset includes levels of pigments and vitamins in blood and feathers as well as feather coloration in captive Eurasian crossbills under captivity., Ministerio de Economía y Competitividad, Ministerio de Ciencia e Innovación, Fundación Ramón Areces, Spain., No

DOI: http://hdl.handle.net/10261/217206
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217206
HANDLE: http://hdl.handle.net/10261/217206
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217206
PMID: http://hdl.handle.net/10261/217206
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217206
Ver en: http://hdl.handle.net/10261/217206
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217206

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217248
Dataset. 2020

OTIC NEUROGENESIS IS REGULATED BY TGFΒ IN A SENESCENCE-INDEPENDENT MANNER DATASET

  • Magariños, Marta
  • Barajas-Azpeleta, Raquel
  • Varela-Nieto, Isabel
  • Aburto, María R.
This work has been supported by Spanish MINECO/FEDER SAF2017-86107-R to IV-N and MM., Peer reviewed

DOI: http://hdl.handle.net/10261/217248
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217248
HANDLE: http://hdl.handle.net/10261/217248
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217248
PMID: http://hdl.handle.net/10261/217248
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217248
Ver en: http://hdl.handle.net/10261/217248
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217248

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217251
Dataset. 2020

RAW DATA CORRESPONDING TO PAPER: “RELATIONSHIP BETWEEN SOIL PROPERTIES AND BANANA PRODUCTIVITY IN THE TWO MAIN CULTIVATION AREAS IN VENEZUELA”, PUBLISHED IN JOURNAL OF SOIL SCIENCE AND PLANT NUTRITION

  • Olivares, Barlin O.
  • Araya-Alman, Miguel
  • Acevedo-Opazo, César
  • Rey, Juan C.
  • Cañete-Salinas, Paulo
  • Giannini Kurina, Franca
  • Balzarini, Mónica
  • Lobo, Deyanira
  • Navas Cortés, Juan Antonio
  • Landa, Blanca B.
  • Gómez Calero, José Alfonso
Raw data from which all the analysis and paper results have been generated., Ibero-American scholarship program (2018-2019) of Banco Santander. “Technological innovations for the management and improvement of the quality and health of banana soils in Latin America and the Caribbean” financed by FONTAGRO and coordinated by Bioversity International. .SHui project funded by the European Commission (GA 773903)., Peer reviewed

Proyecto: EC/H2020/773903
DOI: http://hdl.handle.net/10261/217251
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217251
HANDLE: http://hdl.handle.net/10261/217251
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217251
PMID: http://hdl.handle.net/10261/217251
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217251
Ver en: http://hdl.handle.net/10261/217251
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217251

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217541
Dataset. 2020

ARIMOV DATASET

PATRONES DE ASENTAMIENTO, MOVIMIENTO E IMPERIALISMO, EN LOS VALLES DE ARICA (ARICA Y PARINACOTA, CHILE)

  • Mendez-Quiros, Pablo
  • Saintenoy, Thibault
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 800617., Ver "AriMov_Leeme.txt", Peer reviewed

Proyecto: EC/H2020/800617
DOI: http://hdl.handle.net/10261/217541
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217541
HANDLE: http://hdl.handle.net/10261/217541
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217541
PMID: http://hdl.handle.net/10261/217541
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217541
Ver en: http://hdl.handle.net/10261/217541
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217541

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217542
Dataset. 2020

AAZCAM DATASET

  • Saintenoy, Thibault
"AAzCam" es el acrónimo de un juego de datos construido para investigar la morfogénesis de las redes viales, desde la época prehispánica hacia la actualidad, en la cuenca alta de Azapa (región de Arica y Parinacota, Chile)., Programme Altos Arica, Ministère de l'Europe et des Affaires Etrangères de la république francaise. MSCA800617 UE H2020. Fondecyt 11121665. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 800617., ver archivo "AAzCam_Leeme.txt", Peer reviewed

Proyecto: EC/H2020/800617
DOI: http://hdl.handle.net/10261/217542
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217542
HANDLE: http://hdl.handle.net/10261/217542
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217542
PMID: http://hdl.handle.net/10261/217542
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217542
Ver en: http://hdl.handle.net/10261/217542
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217542

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217602
Dataset. 2020

FUNCTIONAL HABITAT SUITABILITY AND URBAN ENCROACHMENT EXPLAIN TEMPORAL AND SPATIAL VARIATIONS IN ABUNDANCE OF A DECLINING FARMLAND BIRD, THE LITTLE BUSTARD TETRAX TETRAX

  • Arroyo, Beatriz
  • Estrada, Alba
  • Casas, Fabián
  • Cardador, Laura
  • De Cáceres, Miquel
  • Bota, Gerard
  • Giralt, David
  • Brotons, Lluís
  • Mougeot, François
The dataset includes information on little bustard abundance over 10 years in relation to foraging, nesting habitat suitability and urban land, as well as information used to calculate habitat suitablity., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/217602
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217602
HANDLE: http://hdl.handle.net/10261/217602
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217602
PMID: http://hdl.handle.net/10261/217602
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217602
Ver en: http://hdl.handle.net/10261/217602
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217602

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/218057
Dataset. 2020

[DATASET] THROUGHFALL ISOTOPIC COMPOSITION AND DROP SIZE DISTRIBUTIONS SCOTS PINE STAND

  • Pinos, Juan
  • Latron, Jérôme
  • Nanko, Kazuki
  • Levia, Delphis F.
  • Llorens, Pilar
Data information Meteorological variables, rainfall amount, throughfall amount, rainfall and throughfall isotopic composition and rainfall and throughfall drop size distributions. Open area and Pinus sylvestris L forest in Vallcebre research catchments. Study period: from May 2018 to July 2019. Data structure Date and time format • dd/mm/yyyy: day/month/year. • hh:mm:ss: hour/minutes/seconds Meteorological variables • Temp (°C): Air temperature (°C) • RH (%): Relative humidity (%) • Rn (W m-2): Net radiation (W m-2) • u (m s-1): Wind speed (m s-1) • Wind dir (°): Wind direction (°) Hydrometric variables • Rainfall (mm): Rainfall volume (mm) • Throughfall (mm): Throughfall volume (mm) Isotopic data • ISCO_sample: Sample number recorded by the ISCO sampler • RF_δ18O (‰): Rainfall oxygen-isotopic composition (‰) • RF_δ2H (‰): Rainfall deuterium-isotopic composition (‰) • TF_δ18O (‰): Throughfall oxygen-isotopic composition (‰) • TF_δ2H (‰): Throughfall deuterium-isotopic composition (‰) Drop size distribution data • RF_N drops: Number of recorded rainfall drops • RF_Vel mean: Mean velocity of rainfall drops (m s-1) • RF_nKE: Rainfall kinetic energy (J m-2) • RF-0.1 to RF-10.0: Rainfall volume (mm) for 0.1 mm drop size class to rainfall volume (mm) for 10 mm drop size class. • TF_N drops: Number of recorded throughfall drops • TF_Vel mean: Mean velocity of throughfall drops (m s-1) • TF_nKE: Throughfall kinetic energy (J m-2) • TF-0.1 to TF-10.0: Throughfall volume (mm) for 0.1 mm drop size class to Throughfall volume (mm) for 10 mm drop size class “N/D” indicated no available data and “Discarded samples” indicated the water isotope samples that have been discarded from the analysis because the (rainfall or throughfall) water samples contained mixed water either from pre- or post-event, therefore, they corresponds to the first or last samples of certain events., Throughfall isotopic composition and drop size distributions in a Mediterranean mountain forest (Scots pine stand)., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/218057
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/218057
HANDLE: http://hdl.handle.net/10261/218057
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/218057
PMID: http://hdl.handle.net/10261/218057
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/218057
Ver en: http://hdl.handle.net/10261/218057
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/218057

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