Determination of nutrient salts by automatic methods both in seawater and brackish water: the phosphate blank

9 páginas, 2 tablas, 2 figurasThe main inconvenience in determining nutrients in seawater by automatic methods is simply solved: the preparation of a suitable blank which corrects the effect of the refractive index change on the recorded signal. Two procedures are proposed, one physical (a simple equation to estimate the effect) and the other chemical (removal of the dissolved phosphorus with ferric hydroxide).Support for this work came from CICYT (MAR88-0245 project) and Conselleria de Pesca de la Xunta de GaliciaPeer reviewe

Data sharing as social dilemma: Influence of the researcher's personality

It is widely acknowledged that data sharing has great potential for scientific progress. However, so far making data available has little impact on a researcher’s reputation. Thus, data sharing can be conceptualized as a social dilemma. In the presented study we investigated the influence of the researcher's personality within the social dilemma of data sharing. The theoretical background was the appropriateness framework. We conducted a survey among 1564 researchers about data sharing, which also included standardized questions on selected personality factors, namely the so-called Big Five, Machiavellianism and social desirability. Using regression analysis, we investigated how these personality domains relate to four groups of dependent variables: attitudes towards data sharing, the importance of factors that might foster or hinder data sharing, the willingness to share data, and actual data sharing. Our analyses showed the predictive value of personality for all four groups of dependent variables. However, there was not a global consistent pattern of influence, but rather different compositions of effects. Our results indicate that the implications of data sharing are dependent on age, gender, and personality. In order to foster data sharing, it seems advantageous to provide more personal incentives and to address the researchers’ individual responsibility

Descriptive statistics for the nominal-scaled predictors and dependent variables.

<p>Descriptive statistics for the nominal-scaled predictors and dependent variables.</p

Predictive values of sociodemographic variables and personality domains on the attitudes towards data sharing.

<p>Predictive values of sociodemographic variables and personality domains on the attitudes towards data sharing.</p

Predictive values of sociodemographics and personality domains on enablers (E1-E8): B and standard deviation of B (in brackets).

<p>The values of the regression models (R²) are listed in the first row under the dependent variables.</p

Descriptive statistics for the interval-scaled predictors and dependent variables.

<p>Descriptive statistics for the interval-scaled predictors and dependent variables.</p

Predictive values of sociodemographics and personality domains on barriers (B1-B5): B and standard deviation of B (in brackets).

<p>The values of the regression models (R²) are listed in the first row under the dependent variables.</p