Open knowledge in science and elsewhere

Presentation at the Nordic Perspectives on Open Access and Open Science seminar, Helsinki, October 15, 2013

Heading for Open Science: Filling the Knowledge Gap

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Open research data policies, what makes the difference?

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

New business models for open research

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Open publishing: lessons from particle physics

Syksy Räsänen's presentation at Kirjastoverkkopäivät, Helsinki 21.10.2015.

Open monographs

Presentation at the seminar "Publishers and Funders for OA in Finland", Helsinki, May 24, 2016

Open què?: el repte d'obrir i compartir el coneixement generat a la universitat

Què vol dir open access? open licences? open standards? open courseware? Presentació i explicació de les llicències Creative Commons i les seves aplicacions en el camp del coneixement científic

Les Carr explains the moral high ground on open educational resources

In this video, taken in front of the Parthenon at the Acropolis in Athens, Su White interviews Les Carr about why he asserts that there is a moral duty for teachers who create educational content to put that content in the public domain, rather than hoarding it in their institution.

Maturidade de plataformas SOA Open Source vs proprietários

O bom funcionamento de uma empresa passa pela coordenação dos seus vários elementos, pela fluidez das suas operações diárias, pelo desempenho dos seus recursos, tanto humanos como materiais, e da interacção dos vários sistemas que a compõem. As tecnologias empresariais sentiram um desenvolvimento contínuo após a sua aparição, desde o processo básico, para gestão de processos de negócios (BPM), para plataformas de recursos empresariais (ERP) modernos como o sistema proprietário SAP ou Oracle, para conceitos mais gerais como SOA e cloud, baseados em standards abertos. As novas tecnologias apresentam novos canais de trânsito de informação mais rápidos e eficientes, formas de automatizar e acompanhar processos de negócio e vários tipos de infra-estruturas que podem ser utilizadas de forma a tornar a empresa mais produtiva e flexível. As soluções comerciais existentes permitem realizar estes objectivos mas os seus custos de aquisição podem revelar-se demasiado elevados para algumas empresas ou organizações, que arriscam de não se adaptar às mudanças do negócio. Ao mesmo tempo, software livre está a ganhar popularidade mas existem sempre alguns preconceitos sobre a qualidade e maturidade deste tipo de software. O objectivo deste trabalho é apresentar SOA, os principais produtos SOA comerciais e open source e realizar uma comparação entre as duas categorias para verificar o nível de maturidade do SOA open source em relação às soluções SOA proprietárias.

qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software

Background qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl. Findings Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. Conclusion qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Detecting Plants on Railway Embankments

This paper investigates problems concerning vegetation along railways and proposes automatic means of detecting ground vegetation. Digital images of railway embankments have been acquired and used for the purpose. The current work mainly proposes two algorithms to be able to achieve automation. Initially a vegetation detection algorithm has been investigated for the purpose of detecting vegetation. Further a rail detection algorithm that is capable of identifying the rails and eventually the valid sampling area has been investigated. Results achieved in the current work report satisfactory (qualitative) detection rates.