dataesr/scientific-paragraphs-categorization huggingface dataset:
Coiled-coil domains were identified using Pcoils [23]. The ORF1 structure schematic shows domains 5' to the endonuclease. The ORF1 structure is drawn to scale, domain lengths are the minimum identified. We have used the term 'ORF1' for simplicity's sake, although in some cases domains are shown that are probably at the 5' end of the ORF2 (L2 subgroup 5 and CR1 subgroup 4) or at the 5' end of a single ORF (L2 subgroup 4 and CR1 subgroup 6). Domains are color-coded, details are shown in the key. CCHC, gag-like Cys 2 HisCys zinc-knuckle; CTD, C terminal domain; PHD, plant homeodomain; RRM, RNA recognition motif; zf/lz, zinc finger/leucine zipper. Transposase 22 refers to the RCSB Protein Data Bank entry 2yko_A and Pfam entry PF02994, the L1ORF1 protein composed of a coiled-coil, RRM and CTD domain [24].
14 Since 2012, Fondazione Telethon  28  took charge of the EuroBioBank and its management, and also supports the Telethon Network of Genetic Biobanks (TNGB). 29 It has been estimated that the EuroBioBank and TNGB store over half a million samples from over 500 RD, and that 13,000 samples are distributed worldwide per year. 30 he National Institutes of Health (NIH) Rare Disease Hub (RD-HUB) is a centralized database of biorepositories for rare biospecimens established by the Office of Rare Diseases Research, National Center for Advancing Translational Sciences, NIH to aid researchers in locating RD biospecimens and facilitate the link between specimens and patient clinical data that resides in the Global Rare Disease Patient Register Data Repository (GRDR). 31 GARB was developed to provide an infrastructure for advocacy organizations to establish and manage their biobanks and registries.
BBMRI-ERIC foresees a central executive management office that coordinates the interaction of national nodes established in several member states. This will provide a common access portal to resources available for BBMRI-ERIC partners as well as appropriate facilities and expertise. Further examples of biobank networks can be found in Figure  2. Biobank networks promote optimization and dissemination of quality banking practices, distribute quality material and associated data to scientific users, and disseminate biobank-pertinent knowledge and know-how to the scientific community. By improving accessibility of samples, biobank networks allow earlier and more accurate diagnoses as well as more effective treatments when available, and can improve medical and scientific collaboration in the field of RD.
RD-Connect is a global infrastructure project funded by the European Union FP7, although it is not in itself a biobank network. It is aimed at connecting databases, registries, biobanks, and clinical bioinformatics for RD research by developing robust mechanisms and standards for linking and exploiting data generated from RD research projects. 30, 34 o utilize the enormous power of the linked data and limited precious specimens, a suite of clinical bioinformatics tools, including data mining and knowledge discovery tools, are being developed within the RD-Connect framework. Further, major European research networks are underway, including NeurOmics  35, 36  which aims to expedite research on neurodegenerative and neuromuscular diseases and EURenOmics which aims to increase research on kidney diseases, and both directly interact with the RD-Connect network structure.
Linking biobanks and registries with other data sources ultimately enables clinicians and researchers to gain a better understanding of a disease. Providing secure access to data allows researchers in other institutions and those studying other RD to compare results and gain new insights. The Arizona Biospecimen Locator was built on an open source software framework that has served as a model for the Newborn Screening Translational Research Network (NBSTRN). The NBSTRN is an example of a successful network program which under the guidance of the American College of Medical Genetics and Genomics, refined a screening test for a rare immunodeficiency disorder. The underlying open source software program was further developed by a commercial information technology company and currently serves as a model for other virtual networks, like the Arizona Biospecimen Locator.
The rarity and diversity of RD and their associated biomaterials often require international collaboration and integration of data from different studies to achieve critical mass. 14, 37 The challenge is to ensure that this linking, or interoperability, is seamless and scalable. 38 Compounding this challenge are the numerous legacy information systems currently in use that have not necessarily been designed with interoperability as a priority. One strategy to address this bottleneck is to build modular registry frameworks from which registries can be deployed with additional functionality and minimal longer-term software development effort. 38, 39.
RD research and biobanking Research provides a multitude of opportunities to further the scientific and clinical understanding of RD, as well as that of more common diseases. Whilst approximately 3,000 genes have been mapped to RD, a further 3,000 RD are thought to be genetically determined, for which the genetic origin is currently unknown. 40 An overview of the current environment highlights that RD research efforts tend to focus on diagnostics and therapeutics, spanning a range of approaches including translational and biomedical research, and drug development. RD research has historically been highly fragmented by data type, research institution, and disease, and many still remain siloed. Until recently, biobanks designated to collect only samples from RD were few and far between. Those biobanks that did include samples of RD in their collection sometimes discarded these samples for lack of use.
In other biobanks, with RD samples included, identification and availability of these samples was not adequate. Given that there are a limited number of biospecimens for most RDs and it may be difficult or impossible to obtain a new sample, these samples are extremely precious. Additionally, to achieve utility, the quality of the biomaterials and of the associated information is of primary importance. 37 iloed research biobanks held in laboratories with noninteroperable databases can make it almost impossible to connect genetic data with detailed clinical information or biospecimen availability. As the awareness of RD increases, more patient registries and biobanks are being established, allowing RD biospecimens to be more effectively identified, located and retrieved. To accelerate progress in the field of RD, major medical research funders have come together in a global effort aiming to foster collaboration in RD research on a worldwide basis.
43 The IRDiRC policy and guidelines for research include the aim for RD patient registries and biobanks to be global in geographic scope and practice, interoperability and harmonization should be consistently pursued, linking and data transfer into existing platforms should be considered "best practice", and sharing and distributing of biomaterials among RD biobanks is highly encouraged. A specific working group on biobanks, within the IRDiRC's broader interdisciplinary scientific committee, has been established to help streamline access to harmonized data and samples. 43 linical care, biomedical research practices, and the growth of biobanking have been greatly facilitated by advances in computing technology and bioinformatics. The use of such technologies is providing new opportunities to accumulate, share, mine, and integrate datasets for both clinical and research purposes, providing greater potential for growth in translational research.
RD-HUB is one example of a central database for biorepositories/biospecimens of RD where pharmaceutical companies can locate and identify the specimens they need for their studies. In addition, RD-HUB facilitates linkage between patient clinical information and the corresponding biospecimens via the GRDR Global Unique Identifier, which is one of the RD-HUB search fields. The GRDR Global Unique Identifier allows tracking of patient data over time and across studies, repositories, and countries. This tool is essential in the general process of developing drugs, eg, testing for drug efficacy, drug toxicity, and personalized medicine issues.
Funding and sustainability Funding of RD biobanks is different from population and common disease biobanks which are usually supported by industry or public money. 14 Many RD biobanks are supported by the proactive role of patients and patient organizations, short-term research grants, private donations, membership fees, or fees for service. Although more RD biobanks are supported by industry dollars each year, this poses a challenge. As there are a limited number of potential participants, if each company working on a disease creates a biobank, the collections will be very fragmented and burden a limited resource. Securing long-term funding for developing and sustaining RD biobanks must follow fundamental business principles to achieve economies of scale, establish a compelling value proposition and understand the costs of doing business.
65 he start-up infrastructure investment and operating cost requirements for even a modest biobank represent a significant commitment. 66 There are several funding models for biobanks, which include private venture capital, government-funded, charitable not-for-profit and public-private partnerships. RD biobanks would benefit from a sustained  infrastructure. Establishing a biobank for any disease is extremely costly and time-consuming, and requires expert knowledge to maintain the physical infrastructure (including equipment) and data management of the inventory. The cost of the biobank is dependent on many factors, and not limited to the objectives of the biobank, size, location and type of samples. For biobanks specializing in RD, the cost is many times greater. The number of patients and samples collected are much smaller than for common diseases, such that, for commercial biobanks, the return over investment is not sufficient to make it an economical option.
Coding and ontologies Biobanks and collaborative research require sharing and/or integrating data coming from heterogeneous sources using a range of reference terminologies. Translation from one terminology into another requires semantic and syntactic interoperability. Until recently, there was no comprehensive nomenclature to classify and code RD. The World Health Organization's International Classification of Diseases Tenth Edition (ICD-10), now in worldwide clinical use, provides common, comparable diagnostic coding. However, it only provides specific coding for approximately 500 RD. 37, 67 This is likely to result in significant underreporting of RD and limits the ability of health systems to collect quality data for analyses. Orphanet, an international consortium that provides a reference portal for RD, has developed a comprehensive coding system for RD called Orphacodes,  37, 68  that is intended to be adopted in the ICD-11, due to be released in 2017.
The Orphanet classification of RD has adopted a clinical approach by following the organization of medical specialties. It is multihierarchical, meaning that each entry is classified in one or more categories and in one or more sections of a single classification (multiple parentage). Orphacodes accommodate all known RD and, when linked to clinical and genetic data and disease classification, ensures comparability of data. 14 herefore, in the near future, there will be an internationally accepted, comprehensive data classification system that supports RD. Examples of biobank networks that have adopted Orphacodes include TNGB and the EuroBioBank.
Patient involvement and support The involvement of patients and patient organizations raises awareness, trust, and interest in biobanks, and has been demonstrated to be instrumental in gaining the critical mass of samples essential for research into RD that are very rare. 29 atients are not simply providers of biological material. 14, 37 ngagement of participants, families, and the community in biobanks is an essential component in decision-making. It is increasingly being recognized that patients are a valuable source of detailed information about their conditions and treatments and that this information can be integrated into research datasets. As well as benefit sharing, some patients have advocated for shared participation. This creates a partnership between researchers and advocacy organizations, as is seen in a large number of collaborations led by advocacy organizations.
55 Somewhat more complex systems also exist, such as the Platform for Engaging Everyone Responsibly  71  and PatientsLikeMe. 72 RD-HUB is a centralized database of biorepositories for rare biospecimens to aid researchers,  73  and was created following a suggestion from a participant. RD patient organizations recognize the importance of access to high-quality human biological materials as being a prerequisite for research. As such, many have started their own biobanks, recognizing that in combination with a registry, the biobank creates an excellent system for cohort development, a process that could otherwise be prohibitively time-consuming and difficult. As an example, in Europe, the EuroBiobank Network was established in 2001 by two patient organizations, ie, Association Française contre les Myopathies and the European Organisation for Rare Diseases.
Given the need to address issues of consent and improve current policies and practices, patient representatives are included in the governance of projects such as BBMRI and RD-Connect. In the context of the BBMRI preparatory phase, a patients' working group was established as part of the BBMRI stakeholders forum in recognition of the fact that engagement with patients and patient organizations as stakeholders in biobanking practices results in valuable partnerships and sources of input in biobanking research. 30 Patient groups defined the basic principles for patient participation in the biobanking research infrastructure of BBMRI-ERIC. 77 he RD-Connect project will enable scientists and clinicians worldwide to access a single centralized repository for omics data and for phenotypic and biomaterial information.
The project serves as an opportunity for accelerating the rate at which patient clinical and genomic data is linked with biosamples, but also developing innovative ways in which patient participation in biobanking research systems can be improved. RD patient expectations regarding biobanking research systems will be explored through empirical research such as focus groups and deliberative debates. Increasingly, recruitment for participation in registries and biobanks comes from the use of social media. Simple tools, such as Facebook, Twitter, and custom social networking sites, work to aggregate individuals for biobanking, and in some cases even facilitate the entire process through recruitment, sample collection, and reporting general results. 32.
Governance and ethics Governance plays a crucial role in addressing the social and ethical challenges of biobanking. 78 0] [81]  This is especially pertinent to RD biobanking efforts due to the rarity and diversity of biomaterials and the role played by patients and patient organizations. 14 However, few efforts have defined what constitutes a proper governance framework in practice. 78, 82 ithout a proper governance framework, RD biobanks may unduly add layers of oversight, exhibit "silo attitudes" that inhibit sample sharing, and lack the infrastructure required to meet the level of transnational collaboration and harmonization needed. Currently, transnational and international collaborative research in RD is severely hampered by the lack of common quality standards, non-harmonized data management systems, and heterogeneity of ethical and legal requirements between countries.
Some biobanks and biobanking networks have developed common best practice guidelines and charters that are freely available on the Internet, such as the EuroBioBank, TNGB, and the international Public Population Project in Genomics and Society. 14, 29, 88 GARB has developed protocols and material transfer agreements for release of data and samples. Each member organization, with their respective biobank oversight committee, determines who will have access to their collection. Member organizations steward, govern, own, and manage their data and sample collections, determine what data and samples will be collected, and consent their participants. 32 ithin the TNGB, standardization has been mainly achieved through database management, use of a minimum common dataset, controlled terminologies, sample access policies, common informed consent forms, and quality control tools,  29  although it needs to be qualified that the TNGB are biobanks held within a single country, ie, Italy.
Next steps in biobanking for RD Further efforts are required to bring RD biobanks to the attention of scientists, clinicians, and patients, and to closely link information received from specialized diagnostic centers and disease experts for qualitative and quantitative improvements. 14 Clinical staff should have a clear understanding of the added value of participating in the biobank network and be encouraged to collect samples and update relevant databases. Their efforts are a generous contribution to global health research, but also a direct way of improving their own scientific and medical activities. 93 An accreditation and evaluation system should be established to recognize biobanks that provide high-quality samples, and reward and acknowledge scientists who establish and maintain highquality biobanks.
The legal and regulatory frameworks that apply to the biobanking area need to be synchronized to unify and simplify the practice of biobanking across multiple institutions and different countries. The current variability in biospecimen collection procedures will potentially result in varying biosample properties that may undermine future investigation. Mandatory use of standard operating procedures should be introduced to ensure data harmonization between biobanks. In addition, interoperability and harmonization between RD patient registries and RD biobanks is critical. This is in combination with procedures that are time-efficient and adapted to the clinical workflow that will promote clinical engagement and enhance diagnostic and therapeutic development for RD. Further, management of incidental findings is important to avoid dissemination of laboratory results from nonaccredited research or commercially oriented laboratories and maintain patient-centric approaches to RD management.
RD-Connect has developed a charter to provide a common overview and foundational framework for the practice of data sharing, and to frame a minimal list of the terms needed to achieve an equitable and ethically grounded data sharing agreement through engagement and consensus with patients, clinicians, institutions, and government agencies. 94 here is also great demand to add more extensive clinical data to RD biospecimens already collected and stored for research, and to link this to genetic data from whole exome or whole genome sequencing methods. This will enable researchers to better understand cellular interactions in response to therapeutic compound screening. Thus, biobanks will become a true resource of material and information to enhance RD translational research.
Acknowledgments The authors acknowledge the financial support of the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement numbers  305444  ( RD-Connect ), 305608 (EURenOmics), and  305121  (NeurOmics); TREAT-NMD operating grants,  FP6 LSHM-CT-2006-036825 ,  20123307 UNEW_FY2013 ;  Telethon grant   GTB12001  to TNGB, and AFM 16104; EUROPLAN 2012-2015, coordinated by.
Funding: The study was designed and conducted by the investigators and funded via research grants from Servier, France, including a grant to the academic statistical centre. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials. The sponsor had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript, but did assist with the set up and management of the study in each country. Sophie Rushton-Smith provided editorial assistance, limited to editing, checking content and language, formatting, and referencing, and was compensated by the sponsor. The CLARIFY registry enforces a no ghost-writing policy. This manuscript was written by the authors, who take full responsibility for its content. Competing Interests: PRK reports research grants from Servier; consultancy fees/honoraria from A Menarini and Servier.
Study Design CLARIFY is an ongoing international, prospective, observational, longitudinal cohort study in outpatients with stable CAD. The study rationale and methods have been published elsewhere (further information can be found online at www. clarify-registry. com)  [9, 10]. Briefly, the registry was designed to collect data on the current status of outpatients with stable CAD, including their demographic characteristics, clinical profiles, therapeutic strategies, and outcomes. More than 33,000 patients were enrolled in 45 countries in Africa, Asia, Australia, Europe, the Middle East, and North, Central and South America. A detailed list of countries, sites, and investigators has already been published  [10]. Patients are followed up for 5 years with data collected prospectively at annual visits, and interim phone calls every 6 months.
Data Collection The investigators completed standardized electronic case report forms (eCRFs) at baseline. All forms were sent electronically to the data management centre where checks for completeness, internal consistency and accuracy were run. A number of measures were implemented to ensure data quality, including onsite monitoring visits of all data in randomly selected centres (1% per annum). Data quality control happened at face-to-face quality control visits involving review of source documents supporting the adequacy and accuracy of data collected on the case report forms. At baseline data were collected on demographics, medical history, risk factors and lifestyle, physical condition and vital signs, current symptoms and treatments. Available results of laboratory tests, invasive and non-invasive tests were collected, but no test was mandated by the study and there was no standardized measurement of the left ventricular ejection fraction.
Statistical Analysis All CLARIFY data are collected and analysed at an independent academic statistics centre at the Robertson Centre for Biostatistics, University of Glasgow, United Kingdom, which is responsible for the management of the database, all analyses (using the SAS statistical program, version 9. 2 or higher), and storing the data according to regulations. Baseline characteristics for the whole population, according to renal function, are presented using descriptive statistics with mean (standard deviation [SD]) or median (quartile 1, quartile 3 [Q1, Q3]) for continuous variables, depending on the distribution of the data, and as counts (percentages) for categorical data. We analysed the CLARIFY population at baseline grouped by eGFR according to the CKD-Epidemiology Collaboration (CKD-EPI) formula  [11]  (Table  S2 ).