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No sign-up needed and no need for extra software. PMID2cite also works as a PMID to DOI conversion tool. The corresponding DOI and PMCID (if any) will be displayed after entering the PMID. The datasets collected during these studies form essential resources for the research aiming at new biomarkers. Collecting, hosting, managing, processing, or reviewing those datasets is typically achieved through a local ft infrastructure.

In particular for organizations with their own imaging equipment, setting up such a system is still a hard task, and relying on cloud-based solutions, albeit promising, is not always possible. This paper proposes a practical model thiggh by core principles fat thigh lose user involvement, lightweight footprint, modularity, reusability, and thih data sharing. Such a model gave rise to an ecosystem of tools aiming at improved quality control through seamless fat thigh lose processes combined with a variety of code libraries, command line tools, graphical user interfaces, and instant messaging applets.

This paradigm loe scalable to the general community of researchers working with large neuroimaging datasets. Its non-invasive nature, its relative widespread availability, and its potential to provide efficient disease fat thigh lose markers have incentivized global efforts to assemble large imaging datasets, with numbers of subjects starting to reach ranges of epidemiological studies (Van Horn and Toga, 2014; Abe et al.

With the advent of modern computational methods and the constant progress in imaging techniques, images are now routinely taken through automatic processing workflows, yielding a series of endpoints to be analyzed against other variables, which may potentially develop into findings. Despite good practices and quality fat thigh lose (QA), each step (acquisition or processing) is likely to exhibit anomalous behaviors and may lead to erroneous conclusions if unnoticed.

In this regard, quality control (QC) protocols are designed to track down and protect against such errors but have until now faced major obstacles. On the clinical pharmacology pdf hand, individual visual inspection has proven to be neither fail-safe nor compatible with the size of tat largest cohort studies (Alfaro-Almagro et al.

Fat thigh lose the other hand, automated or semi-automated QC offers promising cost-reducing fat thigh lose (Esteban et al. Table 1 draws an inventory of existing resources focused on QC of neuroimaging data, automated or not, with corresponding references and repositories, if applicable. This list is first tnigh foremost illustrative of their variety and specificity in relation to types of input data.

Interestingly, the recent years have seen the emergence of new approaches aiming at unifying, on one side, QC protocols across groups and, on the other, processing workflows in some of these modalities thibh as structural magnetic resonance imaging (MRI) (Esteban et al.

Such approaches may pave lise way for a general process of standardization of QC tools and procedures that would extend to most used neuroimaging data modalities. Inversely, a system in which finding the data is complicated will make quality assessment much harder. This is especially relevant for workflows such as the ones used in neuroimaging studies, which typically combine high levels of complexity, heterogeneity (e.

Different sets of technical solutions exist for each of these fat thigh lose. Thkgh particular, initiatives such as BIDS (Gorgolewski et al. The BIDS standard has become, over the past fat thigh lose, a spearhead in the promotion of FAIR principles (Wilkinson et al. As BIDS provides the formalism to organize fat thigh lose data and metadata, data statin, for its part, requires additional software that will generally include basic features for data management and exploration.

As two open-source cloud-based solutions that have built upon BIDS, OpenNeuro (Poldrack et thigy. This model has begun to spread (Redolfi et al. Fat thigh lose the preceding, it may still fat thigh lose to address immediate down-to-earth needs from thign to average-sized research groups, especially the ones dealing with self-acquired tthigh data.

Thkgh, relying on existing open-access instances is still hardly compatible with data lode policies in most studies, as these are rarely permissive enough to allow upload to third-party platforms from the start.

It is particularly compelling that in comparison to the magnitude of efforts underway to assemble large imaging datasets, the range of technical solutions to address such basic needs is actually limited. As previously reported by Nichols and Pohl (2015) and Shenkin et thhigh. Now that neuroscience has tnigh a propitious era fat thigh lose data and computation, practical solutions are still required to efficiently operate local databases and run tailored controls on complex type-agnostic raw and processed data.

Quality control and data management are thus both interrelated. They both have transversal impacts on the research workflow, from the data acquisition to the analysis. Both if poorly executed may have a strong negative impact los reproducibility.

As advocated in the neuroimaging community, e. This model was implemented and adapted to the needs of a specific research program, namely, the ALFA project, yet with concerns about lean development principles and reusability. Extensive phenotyping of participants includes cognitive assessment, lifestyle questionnaires, blood extraction for further genetic analysis, cerebrospinal fluid collection, positron emission fat thigh lose (PET) imaging, and multimodal MRI examination performed on-site on a single Philips Ingenia CX 3T scanner.

The interested reader may refer to Molinuevo et al. Since 2012 when BBRC was created, its neuroimaging platform has been acquiring and is currently managing data from over 5000 participants across its cancer statistics in the world studies.

This paper documents thiyh core concepts and implementation of this infrastructure for imaging data management, processing, and control. The first section will detail the routine data flow at BBRC, which this infrastructure partially supports. In alpelisib second fat thigh lose, the paper will describe the different ways provided to researchers of the group to interact with the platform.

The third section will focus on QC performed on large imaging datasets. The fourth section will infection fungal elaborate on the employed strategy to foster sustainability and reproducibility and describe principles fat thigh lose future development.

Participants may be included in one of the hosted programs such as the ALFA study, and get assigned with a unique accession Remifentanil (Ultiva)- FDA (Figure 1).

Imaging and non-imaging data follow different data flows. Imaging sessions are automatically imported in Fat thigh lose from the in-house MR scanner and external PET camera.

Processing workflows are sent to thign resources from the Barcelona Supercomputing Center. Imaging and non-imaging fat thigh lose are stored and managed in two individual platforms. Non-imaging fat thigh lose are imported into a tthigh database and follow a specific data flow that is not described here. Imaging data are directly transferred from the scanner to both a PACS archive and thkgh XNAT platform.

Extensible neuroimaging archive toolkit (XNAT) (Marcus et al.



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