A Digital Repository for the Herbarium Collection of Brasília Botanical Garden

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

Handshake ecosystem for Educational Contents between Institutional Repository and OER based Repository

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

A repository is NOT a digital archive

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

From KAPTUR to VADS4R: Exploring Research Data Management in the Visual Arts

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

Sustainable linguistic repository built on DSpace

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

Micro Data Repositories: Increasing the Value of Research on the Web

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

Developing sustainable research data management services: an institutional collaboration

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

HIMALDOC: A Tool for a Regional Open Repository on Sustainable Mountain Development Issues in the Hindu Kush Himalaya

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

The Human Communication Science Virtual Lab (HCS vLab): A repository microclimate in a rapidly evolving research-ecosystem

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

Let's do data research work: the creation of a portal with research information from Catalan Universities

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

Invenio as a platform to implement the SCOAP3 Repository

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

Data-analytiikan hyödyntäminen yrityksen talouden ohjauksessa

Kandidaatintyö on toteutettu kirjallisuuskatsauksena, jonka tavoitteena on selvittää data-analytiikan käyttökohteita ja datan hyödyntämisen vaikutusta liiketoimintaan. Työ käsittelee data-analytiikan käyttöä ja datan tehokkaan hyödyntämisen haasteita. Työ on rajattu tarkastelemaan yrityksen talouden ohjausta, jossa analytiikkaa käytetään johdon ja rahoituksen laskentatoimessa. Datan määrän eksponentiaalinen kasvunopeus luo data-analytiikan käytölle uusia haasteita ja mahdollisuuksia. Datalla itsessään ei kuitenkaan ole suurta arvoa yritykselle, vaan arvo syntyy prosessoinnin kautta. Vaikka data-analytiikkaa tutkitaan ja käytetään jo runsaasti, se tarjoaa paljon nykyisiä sovelluksia suurempia mahdollisuuksia. Yksi työn keskeisimmistä tuloksista on, että data-analytiikalla voidaan tehostaa johdon laskentatoimea ja helpottaa rahoituksen laskentatoimen tehtäviä. Tarjolla olevan datan määrä kasvaa kuitenkin niin nopeasti, että käytettävissä oleva teknologia ja osaamisen taso eivät pysy kehityksessä mukana. Varsinkin big datan laajempi käyttöönotto ja sen tehokas hyödyntäminen vaikuttavat jatkossa talouden ohjauksen käytäntöihin ja sovelluksiin yhä enemmän.

Common dyslipidemias, high lipoprotein(a) concentrations and endothelial function in the children of the STRIP study families

Extreme lipid values predisposing on illnesses are dyslipidemias. Dyslipidemias evolve in early childhood, but their significance or persistency is not well known. Common dyslipidemias may aggregate in the same families. This thesis is a part of the longitudinal randomized Special Turku coronary Risk factor Intervention Project STRIP, in which 1054 families with six months old children were randomized to a control or to an intervention group. The family lipid data from the first 11 years was used. Fasting samples at the age of five years defined the lipid phenotypes. The dyslipidemias coexisting in the parent and the child were studied. At the age of 11 years 402 children participated artery ultrasound studies. The significance of the childhood dyslipidemias and lipoprotein(a) concentration on endothelial function was evaluated with the flow mediated arterial dilatation test. Frequently elevated non-HDL cholesterol concentration from one to seven-year-old children associated to similar parental dyslipidemia that improved the predictive value of the childhood sample. The familial combinations were hypercholesterolemia (2.3%), hypertriglyceridemia (2.0%), familial combined hyperlipidemia (1.8%), and isolated low HDL-cholesterol concentration (1.4%). Combined hyperlipidemia in a parent predicted most frequently the child’s hyperlipidemia. High lipoprotein(a) concentration aggregated in some families and associated to childhood attenuated brachial artery dilatation. Hypercholesterolemia and high lipoprotein(a) concentration at five years of age predicted attenuated dilatation. This study demonstrated that parental dyslipidemias and high lipoprotein(a) concentration help to find early childhood dyslipidemias. The association of hypercholesterolemia and lipoprotein(a) concentration with endothelial function emphasizes the importance of the early recognition of the dyslipidemias.

Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.