dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). The European Society of Radiology (ESR) published a position paper on Imaging Biobanks (European Society of Radiology, 2015) defining imaging biobanks, outlining their purpose, and advocating the creation of a network/federation of such repositories with existing biobanks. Many funders advocate or mandate that data generated by studies they fund are made public and the International Committee of Medical Journal Editors (ICMJE) has proposed that deidentified patient information is shared before research manuscripts of randomised controlled trials will be considered for publication  (Taichman et al. , 2016). While this data sharing may be relatively straightforward for tabular demographic data (i. e. the types of alphanumeric data that can be held in traditional databases), the situation is much more complex for brain image data  (Toga, 2002 , Marcus et al.
, 2007a). Factors like the size of imaging files and the possibility of identifying subjects from images impose non-trivial technological challenges. While initiatives such as NeuroVault (www. neurovault. org -  Gorgolewski et al. 2015)  avoid the problem by publicly sharing statistical maps for data aggregation it does not include whole datasets. By contrast, a repository like OpenfMRI (www. openfmri. org) includes raw-data, with some subject-level variables, which allows newer analyses to be performed. Even when there is a desire to share imaging data, there are a number of technical, legal and practical problems to be overcome:  (Poline 2012; Poldrack and Gorgolewski, 2014, Pernet & Poline, 2015).
Learning from existing databanks and population studies Against this background, a group of experts, including specialists in image acquisition and analysis, clinical disciplines, epidemiology, legal, ethics, and data science, met to discuss and debate conceptual, legal, ethical and technical issues around creating brain image banks. We aimed to highlight the issues that need to be addressed, from the ethical to the practical, achieve some consensus, promote best practice and provide useful advice for ongoing and planned studies. The primary aim of the meeting was to encourage data sharing, construct pan-institutional brain image databank consensus, and facilitate linking between databanks. Here, we describe lessons learned from existing image databanks, provide advice on technical, epidemiological and legal challenges and identify areas where agreement was not reached that should be addressed as the field evolves.
Representatives of major groups involved in neuroimaging databases were present at the meeting (Table  1 ). We recognise that there are other imaging databases, many summarised in recent publications (e. g. Sharing the Wealth: Brain Imaging Repositories in 2015; Neuroimage 124 Part B), but here we provide the lessons learned by this international group with experience in building databases and sharing data under various schemas, for healthy participants and people with neurological disorders, from prenatal to old-age. We noted that the information collected in each databank was very different depending on the perspective and expertise of the individual, e. g. computer scientists or data analysts versus clinicians or epidemiologists. 1 ). Here, we discuss each of these aspects, presenting lessons learned and recommendations.
This can be achieved by citing a paper, 'read me' descriptors, or website that describes the original study, linked from the database. A subject should only contribute once to a particular analysis. However, several open datasets include the same individuals  (Dickie et al. , 2012)   availablehowever this approach may not be reliable, and also implies that even deidentified data about an individual cannot be considered truly anonymous, as data may be susceptible to data linkage attacks  (Fung et al. , 2010). Simply asking participants about inclusion in other studies is unlikely to be reliable. Preferably, subjects would be assigned a unique study identifier linked to a unique study registration, but this is difficult to implement in practice, as there is no central database of studies. However, tools such as The Global Unique Identifier (GUID) may perform this function (https://dataarchive.
g. educational system to allow international comparison). Further discussions are required to agree a minimum normative subject dataset, but this is likely to include some measure of sociodemographic variables, clinical features (main comorbidities, medications), cognitive ability (at least a general cognitive test). Availability of biomarkers (such as DNA) may also be useful. The potential to link to data collected for other reasons, such as participation in nationwide Biobanks or data collected during clinical care, may reduce the burden on participants if appropriate consent is provided and relevant reliable data are available.
C. Consent Individuals must be informed and provide informed consent where possible. One issue is that it may be impossible to re-contact previous participants who have already provided consent for further use of their data, where imaging databanks were not specifically mentioned. In such case, retrospective approval might be given by an ethics committee. Individual projects should also decide whether there is a need to link subjects back to their anonymised data, and consent therefore must include information on being re-contacted. Individuals and researchers should be informed of who 'owns' the data (often the host institution: principal investigators should be aware of any restrictions if they move between institutions), if there are copyrights on images, and consent could be indicated in a metatag.
A perinatal sub-section of the BRAINS database is under development, and the developing Human Connectome Project (http://wp. doc. ic. ac. uk/dhcp/) aims to make data available from 1500 fetuses and newborns between 20-44 weeks' post-menstrual age. Experience from perinatal brain repositories is that newborn neuroimaging research is acceptable to parents and carers, especially those with children at risk of long term impairment. Newborns, including those who require intensive care, can be looked after safely in the MRI environment  (Merchant et al. , 2009). Research quality data can be acquired from infants without the use of sedation by using feed-and-wrap techniques, and by allowing flexibility within in the scan schedule to allow for coaxing to sleep and managing wakefulness. Perinatal image data are readily contextualised by maternal, pregnancy and birth information, and it can be analysed with information from infant biosamples  (Boardman et al.
, 2014; Sparrow et al. , 2016)  and standardised childhood neurodevelopmental / cognitive outcome tests  (Woodward et al. , 2006) : achieving prospective consensus over minimum datasets in these domains will maximise opportunities of perinatal data-sharing in future initiatives. Older age: Normal ageing is associated with increasing variability in brain structure, such as brain tissue loss (atrophy) and the accumulation of white matter hyper-intensities, which may or may not have functional impact on cognition, mood, gait etc. (Dickie et al. , 2015a). Many brain image biobanks were created to study pathological ageing, e. g. dementia, and only small numbers of 'healthy' older people were previously included in accessible databanks  (Dickie et al. , 2012) , making it difficult to define 'normal ageing' or pathological ageing.
Addressing Data heterogeneity Where more than one study is included in a brain image bank, like 3-CITIES  (Alperovitch et al. , 2002)  or BRAINS  (Job et al. , 2016) , there is usually substantial heterogeneity of the acquired demographic/clinical and imaging data. This can be addressed either by describing each variable (3-CITIES), or by harmonising metadata (BRAINS). Having many variables makes the database large and difficult to search, while transforming variables to agreed standards, which is simpler for the end user, is resource intensive and requires additional data provenance documentation. Alternatively, in approaches such as ENIGMA  (Thompson et al. , 2015)  and UK Biobank (Matthews and Sudlow, 2015), both raw and derived data from each of the participating centres can be used for large scale analyses: either in meta-analyses, which can circumvent issues of data sharing and transfer between countries, but may restrict the analyses that can be performed; or in 'mega'-analyses using original data, which may be difficult to access due to data access controls, and/or too different to combine.
A. Minimum provenance of study data Brain imaging uses indirect measures to make inferences about brain structures. The meaningfulness of these measures will vary with the amount and heterogeneity of the data. The version of scanner and software used for data collection and analysis should be clearly documented. Decisions need to be made about the inclusion of raw or processed data in the database, recording of processing steps, and whether outputs of imaging data will be raw, pre-processed or fully processed. The amount of data storage required for imaging (and e. g. genetic) data may require the use of high performance networking, storage and computing, which can be upgraded without compromising the database, adequate bandwidth, and/or the use of cloud computing, taking account of privacy and security issues  (Poldrack 2014).
Database infrastructure Many of the studies that led to the creation of imaging databanks started over a decade ago, and reported issues relating to changing technology  (Mazziotta et al. , 2001). For example, technical staff need to consider the impact of hardware changes (e. g. upgrading or changing scanner software or hardware; changes in data storage solutions and formats) and software evolution, which can make keeping track of multiple analyses of the database challenging  (Poldrack, 2014). Such changes in technology have, for instance, been shown to impact on local brain volume  (Lorio et al. , 2016)  and atrophy measurements  (Leung et al. , 2015). Changing requirements for data governance and data management also need to be considered: file names and structures may need to be updated to newer recommendations such as BIDS (Brain Imaging Data Structure)  (Gorgolewski et al.
, 2015). Similarly, better understanding of disease aetiology, or changes in taxonomy, may affect how clinical characteristics are coded, and therefore what a database entry means, e. g. changing definitions of dementia subtypes. Studies may be cross-sectional or longitudinal. In studies of development or ageing, longitudinal data are particularly valuable  (Mills and Tamnes, 2014) , and systems need to be in place to ensure that future data acquisition can be matched to the correct subject, and that imaging parameters are similar enough to allow comparison.
A. Technical infrastructure To maximize usage and usability, any planned databank should make use of a formal imaging database tools. We considered five software tools that create sharable, searchable databases and offer maximum flexibility: COINS (http://coins. mrn. org/) (Landis et al, 2016)   (Marcus et al, 2007a). The ability to link and search make those applications different from repositories; however each has strengths and limitations, some significant. Information reported below reflect user experience and discussion with software developers. Each of these different software tools has different strengths, discussed at the meeting. allowing direct transfer between the database and the health care service. As imaging is just one facet of any research study, it is vital that all these tools also allow storing of non-MRI data such as demographic, clinical, behavioural, and genetic data.
Other types of data such as electroencephalography can be stored but may not be viewable using imaging analysis/display interfaces, and may require additional software. Another important aspect to consider relates to the software: (1) How are MRI data linked with other types of data? (2) How can the data be visualised?; and (3) How can the data be explored? Linkage (bringing together datasets) is an important aspect to consider regarding the size of the dataset and its utility. For instance, LORIS is built with two distinct databases (imaging vs non-imaging) that allows the user to interrogate, process and retrieve information separately or together. XNAT uses an XML-defined schema for searching data, and also uses and underlying object-relational database PostgreSQL. Like LORIS, this allows searching and accessing data separately.
Considering the end-user perspective of the database one wishes to build, all five tools allow one to search for image data within and across projects and to visualise data, though with some restrictions depending on the software. The final important aspects to consider are the overall maturity, usability, maintainability, extensibility and support of the software and access control. LORIS and XNAT, for instance, are well established and maintained, while newcomers (COINS, Scitran, NiDB) are more adapted to new data types and use more recent software technology. Mostly, the software can be installed with minimal programming expertisebut this differs between tools. Setting up multiple access control levels, linking or adding new functionalities, can however require much more expertise. For instance, XNAT, the most widely used platform, requires programming expertise to enable its extensible XML structure.
In contrast, simpler tools with extensive search solutions exist (e. g. LORIS, NiDB), but modifying the search tool to suit dedicated needs also requires dedicated programming expertise. In terms of usability, it is worth considering data visualization, and whether the pipeline includes data quality control or basic data pre-processing. All the platforms considered include such options but various levels of programming expertise are required. It is important to also consider 'futureproofing' the technology, at a minimum ensuring accurate version control, and direct access to code. Another essential aspect to consider is access control: COINS, LORIS, Scitran, XNAT have extensive security levels to create, read, and edit data, while NIDB is limited to user/project management. Thus, at present, the consensus was that there is no 'best' software, the field is moving rapidly, and it is worth considering every aspect discussed here with expert advice to choose a software tool that best suits specific needs.
B. Security A brain scan is unique, and could allow identification of the individual (e. g. by someone who already holds a copy of their brain image). There is a trade-off between removing all potential identifiers and retaining the scientific value of the data. Clear processes are required regarding sensitive, or potentially identifiable, data to ensure that all reasonable safeguards are put in place, e. g. the DICOM Confidential software; completely removing all textual personally identifiable information; generating new "anonymous" identification numbers; "defacing" brain MRI  (Marcus et al. , 2007b, Milchenko and Marcus, 2013; Rodriguez, 2010). A balance between accessibility and security is important, ensuring that potentially identifiable data (text or images) are protected from remote access in the database and if released for external use.
C. User interface The user interface should suit the main proposed users. The requirements of clinicians, researchers, or industry are however likely to be very different (for instance searching per pathology vs. scan feature, looking for raw vs. processed data). It is worth considering the user interface and its design. In XNAT for example, the level of detail displayed to users can be made dependent on both their relationship with the databank (external user, contributor, etc. ) and/or the intended use of the data of interest  (Marcus et al. , 2007a). The use of an Application Program Interface (API) allows for easier creation and customisation of user interfaces for different user groups, but can bring new security concerns.
E. Quality assurance and control of data Quality assurance (QA) of all data is key to providing high quality and robust research findings  (Ducharme et al. , 2016). All QA steps of data collection (blinding of researchers, checking of data entry, standard operating procedures and calibration of equipment, particularly methods such as phantom scanning to describe scanner stability) should be recorded and provided with the data. Quality control should be implemented at all stages of the database from provenance to visualization. Data aggregation centres could provide a useful service with a common QC procedure across all datasets included. An important step is to be transparent on how this is implemented and what is tested on the data. If it is planned to incorporate processed data from external groups (e.
Database management The legal and ethical framework of individual countries, and agreements reached between them, may affect how and where data are or can be stored. Systems are required to ensure data security, but allow appropriate access. Relevant approvals should be transparent, e. g. in publications and on websites. During the meeting it was recognized that brain image databanks should have a Steering Committee, including independent and lay representatives, to monitor and review progress. This has the advantage of providing oversight of data usage and the opportunity to review data requests, but can be time consuming, it may be difficult to get agreement among stakeholders, and therefore delay access to data, and it can be difficult to identify lay representatives. Consideration should be given to how 'sleuthing' of individual's data can be minimised or prevented, and to the legal and ethical aspects of data collection, storage and sharing.
The issue for databanks is what to do if an abnormality of potential health significance is noted, or new health implications for existing findings come to light during secondary useshould the information be fed back to the participant, or the researchers that produced the data? These issues should be considered in the data donation agreement. If data are fully anonymised such feedback would not be possible. The UK Health Research Authority (http://www. hra. nhs. uk/) is one example of a body that promotes research to improve clinical care of patients. The sharing and use of already collected data with appropriate safeguards fits this duty. The Royal Society, Science as an Open Enterprise, (2012) report, promotes Intelligent Opennessintelligibility, verifiability, accessibility if robust, if there is commercial confidence and personal privacy.
B. Publication To maximise use, databanks should be registered on a publicly accessible registration website. Currently there is no international registry for neuroimaging studies or databanks. A general platform such as ClinicalTrials. gov could be used in the interim. -Bandrowski et al. , 2015). The consensus from our meeting was to discourage authorship, but to have the data paper cited along with the inclusion of the databank acronym with a web-reference or, better still, a DOI. A standardised template for reporting these papers (such as CONSORT for randomised controlled trials, see http://www. equator-network. org/) would be useful. This would allow an emphasis on clinical and epidemiological as well as technical perspectives.
C. Funding Increasingly, funders are keen to encourage data sharing; indeed some go so far as to refuse future funding unless the results of prior funded studies have been published open source (http://www. wellcome. ac. uk/About-us/Policy/Policy-and-positionstatements/WTD002766. htm). While most brain imaging studies are individually funded through grants to acquire data and undertake initial analyses, it has previously been difficult to obtain funding to create a brain image bank, or if initial funding was secured, then it may be insufficient to maintain the database long term, and deal with data requests. Storage and back up is, however, necessary, and inclusion for provision of this is encouraged. Researchers at ADNI estimate that 10-15% of funding, and 15% of time has been spent on data sharing  (Wilhelm et al.
, 2014). The use of large-scale distributed computation can make the work more efficient, but users need to be aware of the heterogeneity of the constituent datasets e. g. , in ENIGMA no one national government had to finance all the capital infrastructure. Similarly, a distributed image databank could be supported by funding from individual countries, much as are some multinational clinical trials. There is a tension between the desire to share the data and the feasibility of actually affording to do so, and a range of models exist. A federated international network of normative brain image biobanks is achievable (see table 2) and would have many advantages, including: 1) facilitating large scale meta-analyses of brain structure and function, in health and disease, following successful precedents (e.
Comparing first the aspects related to the nature and format of imaging data, all of the above software tools support the Digital Imaging and Communications in Medicine (DICOMhttp://dicom. nema. org/) format and can interface directly with scanners. Some software tools support almost any other image formats (COINS, NiDB, XNAT), while others are restricted to specific ones (LORIS, Scitran). Only XNAT has an explicit tool to link the data from the database directly to clinical PACS (Picture Archiving and Communication System), i. e.
Acknowledgements Thanks to  Guarantors of Brain ,  British Geriatrics Society (Scottish Branch) ,  Royal Society of Edinburgh ,  SINAPSE (Scottish Imaging Network: a Platform for Scientific Excellence) SPIRIT, International Neuroinformatics Coordinating Facility  and  Nuffield Foundation  for contributions towards funding the meeting which formed the basis of this paper. TEN is supported by the  Wellcome Trust  ( 100309/Z/12/Z ).
records how the databank was established, and the decisions made at each stage (e. g. for UK Biobank at http://www. ukbiobank. ac. uk/wp-content/uploads/2011/11/UK-Biobank-Protocol. pdf) and a 'data paper' that can include technical data and then be cited in the methods section of future results papers (e. g. for the imaging in UK Biobank http://www. nature. com/neuro/journal/v19/n11/full/nn. 4393. html). The wording on citation and authorship should not contravene ICJME (International Committee of Medical Journal Editors) rules http://www. icmje. org/recommendations/browse/roles-and- responsibilities/defining-the-role-of-authors-and-contributors. html. There is also the possibility of having a Research Resource Identifier (RRID One option would be a data posting website for imaging, like dbGaP for genetics (http://www.
One fresh aliquot was used to inoculate directly the bioreactor for generating the fresh condition that constituted the initial control point. The stored aliquots were thawed in an anaerobic chamber after 3 and 6 months of storage and inoculated in 5 mL of medium. The same steps of fermenter inoculation were performed as described previously, with the exception that this time fermentation was carried out on a chemostat during seven residence times (7 × 24. 96 h) to achieve stabilization  [15]. The oxidation/reduction potential (ORP) was monitored using a specific probe. The acquisition and control during the fermentations were handled by C-BIO software (GPC-Global Process Concept). After stabilization, aliquots were sampled for analysis.