The MNI data-sharing and processing ecosystem

AbstractNeuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dettmann_MacFarlane_2018

Data from: Trans-species predictors of tree leaf mass, Dettmann, Garret, MacFarlane, Dave. Contains data on 188 trees sampled within Michigan of varying species of dry leaf mass and relevant variables including DBH, competition, live crown ratio, basal area increment, age, crown area, DLB, and specific gravity of the wood. If used please cite the original works

Data from: Trans-species predictors of tree leaf mass

Tree leaf mass is a small, highly variable, but critical, component of forest ecosystems. Estimating leaf mass on standing trees with models is challenging because leaf mass varies both within and between tree species and at different locations and points in time. Typically, models for estimating tree leaf mass are species-specific, empirical models that predict intra-specific variation from stem diameter at breast height (DBH). Such models are highly limited in their application because there are many other factors beyond tree girth and species that cause leaf mass to vary and because such models provide no way to predict leaf mass for species for which data are not available. We conducted destructive sampling of 17 different species in Michigan, covering multiple life history traits and sizes, to investigate the potential for using a single, ‘trans-species’ model for predicting leaf mass for all the trees in our study. Our results show the most important predictors of tree leaf mass are DBH, five-year basal area increment, crown class, and competition index, none of which are species specific. Species-specific variation could be captured by leaf longevity and shade tolerance and wood specific gravity was a statistically significant, but marginally-important predictor. Together, these variables describing tree size, life-history traits, and competitive environment were able to allow us to develop a generalized leaf mass model applicable to a diverse set of species, without having to develop species-specific equations

Evaluation of forest interception estimation in the continental scale Australian Water Resources Assessment - Landscape (AWRA-L) model

The AWRA-L model is a core component of the joint Bureau of Meteorology (BOM) and CSIRO Australian Water Resources Assessment (AWRA) system which integrates hydrological models and a variety of observations and satellite products to produce a National Wa