La edición y el acceso a la literatura y los materiales GLTBQ

A la pregunta sobre si existe un interés profundo en el mercado editorial por la temática gay, lesbiana, transexual, bisexual y queer (GLTBQ) o si se trata de un simple “nicho” en alza, suele responderse desde dos perspectivas casi antagónicas: entre quienes se atreven a opinar —no sin razón en algunas ocasiones— que detrás de toda moda o tendencia del mercado editorial hay un sinfín de intereses creados y los que creemos que aún no es sufi ciente median no sólo algunos kilómetros de distancia, sino, sobre todo, motivos distintos y a veces hasta enfrentados. Sin duda los primeros responderían que sí, mientras que los segundos responderíamos seguramente lo mismo, pero con algunos matices. Los primeros alegarían falta de criterio, paja en un mercado saturado de títulos inútiles y falta de motivos, falta de “visibilidad”; los segundos nos acogeríamos a un derecho básico, el de la información —que es la base de la educación en la diferencia..

REMVEG: Remote assessment of vegetation status by hyperspectral imagery

Broad-band, high-spatial resolution satellite Remote Sensing (i.e., Landsat-TM, Spot-HIV) has proved to be an essential tool for landcover change mapping, but insufficient to (i) resolve subtle categories like the ones often needed for ecological research, and (ii) measure biophysical magnitudes. Narrow-band hyper-spectral airborne Remote Sensing provides data that can be used for these purposes, but also generates specific processing needs. Classical multivariate clustering techniques and simple vegetation indexes, traditionally applied to broad-band satellite imagery, are insufficient to retrieve information from narrow-band hyper spectral imagery. The processing of this imagery needs (i) to be driven from dedicated field data, in particular from field spectroscopy and (ii) to abandon the pixel as the processing unit and to substitute it by patches, as produced by segmentation algorithms (Lobo 1997). Within the frame of our general goals in the project, we have focused on the evaluation of DAIS imagery to (i) characterize different vegetation covers in terms of their hyperspectral signatures and to (ii) use the DAIS signatures to assess vegetation condition in a parallel way as this has been previously done with hand-held field spectroradiometry. We describe in this intermediate report our first steps and results processing DAIS hyperspectral imagery acquired over a humid Mediterranean forest in Les Gavarres (NE Spain, 41 ° 54' N, 2° 58' E).DAIS y Fundació Catalana per a la Recerca i la InnovacióN

Challenge 2: Ocean Variability and Climate

17 pages, 1 figureOceans cover 71% of the Earth’s surface. Most of the solar radiation is absorbed by the oceans. The way the oceans use and distribute this radiation affects the Earth’s weather and climate. Advances in our knowledge of the interplay between air-sea interactions, mechanical turbulent mixing and the biological carbon pump remain key to understanding the past, present and future climate scenarios of Eart

OCEANS OF BIG DATA AND ARTIFICIAL INTELLIGENCE

International audienceThe ocean is a fundamental element for the Earth and for the wellbeing of human societies. It influences weather and climate, impacting sectors such as marine ecosystems, economy, tourism, and human health. Urgent actions are demanded to help in understanding and managing the ocean in a multidisciplinary and integrated way. Here we present the major ocean research challenges for the next decades, CSIC leadership and resources needed. In this context, this chapter specifically addresses big data and AI

OCEANS OF BIG DATA AND ARTIFICIAL INTELLIGENCE

International audienceThe ocean is a fundamental element for the Earth and for the wellbeing of human societies. It influences weather and climate, impacting sectors such as marine ecosystems, economy, tourism, and human health. Urgent actions are demanded to help in understanding and managing the ocean in a multidisciplinary and integrated way. Here we present the major ocean research challenges for the next decades, CSIC leadership and resources needed. In this context, this chapter specifically addresses big data and AI

Challenge 8: Oceans of Big Data and Artificial Intelligence

Chapter 8.Oceans are no longer inaccessible places for data acquisition. High-throughput technological advances applied to marine sciences (from genes to global current patterns) are generating Big Data sets at unprecedented rates. How to manage, store, analyse, useand transform this data deluge into knowledge is now a fundamental challenge for ocean sciences. Artificial Intelligence and Machine Learning are the most promising and exciting approaches addressing this challenge. These technologies are directly applicable to many data analysis problems and major challenges in the study of the ocean microbiome, ocean observation and forecasting, animal biology, ecology and conservation, resource management, and marine geosciences. We are only at the beginning of an era when machines are able to solve complex tasks that, until today, have required human expertise. We envision that the combination of ocean Big Data and Artificial Intelligence will provide the means for ground-breaking advances in our understanding of ocean functioning.Peer reviewe