Evaluating the in-situ effectiveness of indoor environment guidelines on occupant satisfaction

Post occupancy evaluation (POE) studies typically use a combination of occupant questionnaires and physical measurements of various aspects of the indoor environment to assess building performance. These physical measurements are often compared against published reference limits to evaluate compliance and satisfactory performance. This study investigates whether indoor environment conditions compatible with published indoor environment quality (IEQ) standards and guidelines are predictive of occupant satisfaction. Data used in this study were collected as part of two large building evaluation field studies conducted in the past eight years. Occupant questionnaire and physical measurement data from 11 office buildings across North America were used (N=194). Inputs for the analyses were demographic factors and workstation characteristics, as well as aspects of the measured physical indoor environment. Outcome variables were various measures of environmental satisfaction (i.e. lighting, acoustics/privacy, and ventilation/temperature). The results of this study suggest that occupants had higher satisfaction with lighting when measured desktop illuminance levels were within IESNA RP-1-12 (2012) recommendations. Measured sound levels and thermal conditions within reference limits did not correlate to higher occupant satisfaction in their respective categories

A classification system for services in nondomestic buildings

A classification system is described for heating, ventilation, and air-conditioning services in nondomestic buildings and is applied to data from a sample of detailed energy surveys of such buildings. Analyses are reported of the occurrences of servicing systems of different types in the surveyed premises, and of the relationships between system types, sizes of premises, built form, and the activities accommodated.

Standardized reporting of neuroimaging results with NIDM in SPM, FSL and AFNI

International audienceThe growing awareness of underpowered and irreproducible research [1] has highlighted the necessity for data sharing in neuroimaging. Encouragingly, in the past ten years, the number of publicly available datasets has greatly increased, principally thanks to consortiums dedicated to data sharing such as the Human Connectome Project [4], the International Data sharing Initiative [5] or more recently the Consortium for Reliability and Reproducibility [3] and study forrest [10]. But while there is an increasing interest in sharing raw or pre-processed data (e.g. after motion correction, registration to a template), statistical images supporting neuroimaging publications are rarely made available. Furthermore, despite the availability of guidelines [7], ambiguous or incomplete methodological reporting is still commonplace [2] making image-based meta-analysis and reproducibility studies particularly challenging. In [6], we introduced the Neuroimaging Data Model (NIDM), a domain-specific extension of the recently-approved W3C recommendation, PROV-DM [9]. Here, we introduce NIDM-Results, a standardised representation of “mass univariate” neuroimaging results for the three major software analysis packages: SPM, FSL, and AFNI

NIDM-Results: Standardized reporting of mass univariate neuroimaging results in SPM, FSL and AFNI

International audienceResults of a neuroimaging study are usually shared through the publication of a scientific paper describing the experiment and analysis outcome. While hundreds of gigabytes of data are usually generated as part of an fMRI experiment, in the literature the authors typically report their results as a list of significant local maxima, i.e. locations in the brain defined in a standard space (e.g. MNI) that pass rigorous statistical testing. This practice is unsatisfactory in terms of data re-use as it does not allow for the automatic extraction of acquisition or processing information and it provides only sparse information about the location of the brain activity. While databases have been built to provide manually curated (such as BrainMap ​ [1]​) or automatically-extracted (e.g. NeuroSynth ​ [2]​) meta-data associated with published papers, ideally, these meta-data should be made available by the authors themselves at the time of the publication