A STATISTICAL PROCEDURE TO EVALUATE CLEAN-UP STANDARDS

The object of this paper is to develop a suitable statistical procedure to evaluate clean-up standards athazardous waste sites.Under the assumptions that contaminant masses at a site follow a gammadistribution and that the data from the pre-remediation baseline sample as well as from the interim orfinal sample taken after a certain period of operation are both distributed as gamma with the same shapeparameter but different scale parameters,we derive a uniformly most powerful unbiased test of thehypothesis that a specified percentage of contaminant mass has been reduced.A large-sampleapproximation of the exact test procedure and a comparison with the likelihood ratio test are provided.     

Observational Indicators of Formation Excitation of H2

Theoretical predictions by Farebrother et al. and Meijer et al. of rovibrational excitation probabilities in H₂ arising from formation by Eley-Rideal processes on a graphite surface are incorporated into a model of the chemistry and excitation of interstellar H₂. The model includes the usual radiative and collisional pumping of H₂ rotational and vibrational states, in addition to the formation processes. Predictions are made for HH₂ rovibrational emission line intensities for representative points in diffuse and in dark interstellar clouds. We find that – if all the interstellar HH₂ is formed by this Eley-Rideal process – then the consequences of formation pumping, as distinct from collisional and radiative pumping, should be clearly evident in both cases. In particular, we predict a clear spectral signature of this direct HH₂ formation process on graphite, distinct from radiative and collisional pumping; this signature should be evident in both diffuse and dark clouds; but the emissivity for dark clouds is predicted to be some 500 times greater than that in diffuse clouds in which the dense material may be embedded. An observational search for this signature in two dark cloud sources was made, but a preliminary analysis of the data did not yield a detection. The implications of and possible reasons for this preliminary conclusion are discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Ten best practices to strengthen stewardship and sharing of water science data in Canada

Water science data are a valuable asset that both underpins the original research project and bolsters new research questions, particularly in view of the increasingly complex water issues facing Canada and the world. Whilst there is general support for making data more broadly accessible, and a number of water science journals and funding agencies have adopted policies that require researchers to share data in accordance with the findable, accessible, interoperable, reusable (FAIR) principles, there are still questions about effective management of data to protect their usefulness over time. Incorporating data management practices and standards at the outset of a water science research project will enable researchers to efficiently locate, analyse and use data throughout the project lifecycle, and will ensure the data maintain their value after the project has ended. Here, some common misconceptions about data management are highlighted, along with insights and practical advice to assist established and early career water science researchers as they integrate data management best practices and tools into their research. Freely available tools and training opportunities made available in Canada through Global Water Futures, The Gordon Foundation DataStream, the Digital Research Alliance of Canada Portage Network, Compute Canada, and university libraries, among others are compiled. These include webinars, training videos, and individual support for the water science community that together enable researchers to protect their data assets and meet the expectations of journals and funders. The perspectives shared here have been developed as part of the Global Water Futures programme's efforts to improve data management and promote the use of common data practices and standards in the context of water science in Canada. Ten best practices are proposed that may be broadly applicable to other disciplines in the natural sciences and can be adopted and adapted globally.