Variability of humidity conditions in the Arctic during the first International Polar Year, 1882-83

Of all the early instrumental data for the Arctic, the meteorological data gathered during the first International Polar Year, in 1882–83 (IPY-1), are the best in terms of coverage, quality and resolution. Research carried out during IPY-1 scientific expeditions brought a significant contribution to the development of hygrometry in polar regions at the end of the 19th century. The present paper gives a detailed analysis of a unique series of humidity measurements that were carried out during IPY-1 at hourly resolutions at nine meteorological stations, relatively evenly distributed in the High Arctic. It gives an overall view of the humidity conditions prevalent in the Arctic at that time. The results show that the spatial distribution of atmospheric water vapour pressure (e) and relative humidity (RH) in the Arctic during IPY-1 was similar to the present. In the annual course the highest values of e were noted in July and August, while the lowest occurred in the cold half of the year. In comparison to present-day conditions (1961–1990), the mean values of RH in the IPY-1 period (September 1882 to July 1883) were higher by 2.4–5.6%. Most of the changes observed between historical and modern RH values are not significant. The majority of historical daily RH values lie between a distance of less than two standard deviations from current long-term monthly means

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.

Introduction: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed

Characterisation of data resources for <i>in silico</i> modelling: benchmark datasets for ADME properties

<p><b>Introduction</b>: The cost of <i>in vivo</i> and <i>in vitro</i> screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data.</p> <p><b>Areas covered</b>: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31 selected datasets were assessed using specific criteria derived in this study.</p> <p><b>Expert opinion</b>: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed.</p

Collection of toxicity, physicochemical and characterisation data to enable modelling of nanomaterial effects

This is a poster presentation delivered at the Nanosafety 2013 conference, November 2013, Saarbruecken, Germany: http://nanosafety.inm-gmbh.de/<br><br>Disclaimers:<br><br>(1) this presentation has not undergone peer review<br><br>(2) this presentation may report preliminary results which may have been revised in subsequent publications<br><br>(3) no endorsement by third parties should be inferred<br><br>Presentation abstract:<br> <p>A number of EU projects have been established to address concerns about the potential health risks posed by nanomaterials. The NanoPUZZLES project is developing new computational methods for predicting the toxicity of nanomaterials based on Quantitative Structure-Activity Relationships (QSARs), chemical category formation and read-across approaches. Successful application of these approaches requires sufficient quantities of high quality toxicological and physicochemical data on well-characterised nanomaterials to be organised self-consistently within an electronic database. NanoPUZZLES is contributing to the development of such a database based on data curated from public domain sources.</p> <p>Initial data collection efforts within NanoPUZZLES yielded a significant number of data points from various peer-reviewed publications. By extending the Klimisch criteria for toxicological data quality assessment, criteria for assessing the quality of data reported for nanomaterials, as well as the suitability of datasets for building QSARs, were developed. However, organising nanomaterial data remains a challenge. The current focus of data collection efforts within NanoPUZZLES is the exploration and evaluation of standards for organising experimental data for nanomaterials: the recently published ISA-Tab-Nano file format is of particular interest. The need for a unique identifier for nanomaterials and minimum information standards for a nanomaterials database is also being addressed. </p> <p>Funding through the European Commission 7th Framework Program NanoPUZZLES (FP7-NMP-2012-SMALL-6, Grant Agreement no. 309837) and NanoBRIDGES (FP7-PEOPLE-2011-IRSES, Grant Agreement no. 295128) projects is gratefully acknowledged.</p><p>N.B. The spreadsheet images provided in this poster, of provisional NanoPUZZLES files, are used with permission from Microsoft. </p> <br