dataesr/scientific-paragraphs-categorization huggingface dataset:
Among the issues one must consider are the following: What is the scope for anonymization of data? How can the rights of individuals be protected in a rapidly changing digital world? How does one enable the potential positive benefits of data sharing? These require the development of new ways of working with, and securely sharing, identifiable data in a scalable and rigorous manner. Below, we outline the Minerva Initiative-a research data resource (Minerva Image Resource-MIR) and an open research consortium (Minerva Consortium-MC), which has been set up to allow the sharing of identifiable patient data, such as facial photographs and collaborative research projects on rare diseases.
Coming to the conclusion that a particular variant is disease contributing for a set of phenotypes is not trivial. There are a number of initiatives to collect information on patient phenotypes, gene, and variants in databases, such as DECIPHER  (Wright et al. , 2018) , PhenomeCentral  (Buske et al. , 2015a) , PhenoDB  (Hamosh et al. , 2013) , GeneMatcher  (Sobreira et al. , 2015) , IRUD  (Adachi et al. , 2017) , KCCG Patient Archive ("Patient Archive", n. d. ), MyGene2 ("MyGene2-Home" 2, n. d. ), Human Disease Gene Website series ("Human Disease Genes", n. d. ), ClinVar  (Landrum et al. , 2016) , and the Centers for Mendelian Genomics  (Chong et al. , 2015). Many of these resources are connected as the Matchmaker Exchange  (Philippakis et al. , 2015) , via a common Application Program Interface  (Buske et al.
, 2015b) , allowing researchers and clinicians to identify additional patients for novel disorders, and in some cases collect additional cases for known ones. However, as these resources grow, it is no longer safe to infer pathogenic causality just because a putative variant has been observed in another patient previously. As the number of variants of uncertain significance grows, it is expected that the rate of false-positive mutation matches will also increase  (Dorschner et al. , 2013; Akle et al. , 2015; Krawitz et al. , 2015). Consequently, the current approach to search disease databases for a mutation match might be insufficient to identify new pathogenic mutations. Patients with very different rare diseases are still likely to have rare mutation matches by random chance. Employing phenotype metrics could add additional power to DNA variant pathogenicity classifications and should feature in efforts to understand genomic variants in clinical settings, such as ClinGen  (Rehm et al.
PHENOTYPING FROM PHOTOGRAPHS Developments in computer vision and deep learning are being applied to patient datasets with the aim of aiding diagnosis, prediction of outcomes, and monitoring of clinical phenotypes. One of the first clinical settings where this is being applied is clinical dysmorphology for rare diseases. It should be noted that the idea to objectively assess body form (anthropometrics) in Western medicine was pioneered by Francis Galton well over a century ago  (Galton, 1879)  and applied to images by  Sheldon et al. , in 1940 (Sheldon et al. , 1945). However, the formalized discipline of clinical dysmorphology, as the study of birth defects, was not conceptualized until 1966  (Smith, 1966). The challenge of bringing to bear machine learning and data analytic approaches to clinical dysmorphology, and anthropometrics from image data, has been the target of many threads of research.
All deep learning approaches are reliant on large datasets to learn from in order to perform accurately. Identifiable medical datasets are typically relatively small in the context of deep learning, and data are usually sourced from single health care settings or specific populations. This makes models over fit, that is to say, become overly specific to the particular datasets they have been shown, and consequently, they perform poorly on new datasets. To efficiently use big data approaches, we need to address the challenges of working with data outside of the isolated silos of single projects or health care systems. Ethical, legal, data security, and intellectual property issues are key barriers preventing the ascertainment, interrogation of big datasets, and the implementation of big data solutions in health care.
Traditional research consent structures may constrain or prevent data sharing and the integration of datasets essential for these new approaches. In the context of the strict regulation of and accountability in health care data, if there are any doubts about the rules around sharing, the default position is to not share. Consequently, data sharing is unlikely to happen without specific consent for integration of personally identifiable information. Consequently, most data-sharing efforts bringing together global data rely heavily on de-identification or the minimization of sharing more sensitive data  (Dyke et al. , 2017). The reality of big data is that anonymization might not be possible  (Gymrek et al. , 2013; de Montjoye et al. , 2015). Furthermore, data security concerns for data access rights and the prospects of malicious digital activity (hacking) imply often unacceptable risk where organizations are liable for data loss.
MINERVA INITIATIVE The Minerva Initiative has been developed to enable research into the use of image analysis for the diagnosis of diseases, prevent data siloing, and foster further healthy competition between various image phenotyping approaches. The Minerva Initiative is an effort to construct a precompetitive space for enabling research on clinical phenotyping tool development. It has been constructed in the spirit of open science  (Woelfle et al. , 2011; Nielsen, 2012)  and within the bounds of ethics and data governance constraints. The Minerva Initiative has the following objectives: to build a community of researchers and clinicians; to continue to develop ethical structures and provisions for working on identifiable clinical images; and to deliver secure data sharing between consortium members.
MINERVA CONSORTIUM The Minerva Consortium (MC) is an international network of clinicians and researchers, from both public and private organizations, involved in the Minerva Initiative. Within the MC, there are many purposes and interests, but all MC members seek to establish a commonly agreed set of conduct and praxis for the benefit of all stakeholders: patients, participants, researchers, clinicians, and social systems. The MC consists of a management group, clinical collaborators, working groups, and phenotyping groups. The management group is tasked with guiding directions of joint MC efforts, MC policy and strategy, acceptance of new MC Computational Phenotyping Groups, and dispute resolution. For prospective data collected under the Minerva Initiative consent, the Management Group also acts as a Data Access Committee overseeing Phenotyping Groups' access to the Minerva Image Resource.
MINERVA IMAGE RESOURCE To improve the models for phenotype descriptions through deep learning, data need to be brought together in a unified compute system. Distributed systems are not feasible because either they sacrifice security, by virtue of exchange of semi-identifiable data, or because data transfer time and volumes are increased to such a level they are not workable. While fixed algorithms can be deployed in distributed frameworks, this would also prevent iterative learning regimens, which are required for continuing improvements. Consequently, the Minerva Consortium has focused on legal, ethical, and data governance structures to allow the global pooling of data in a unified system. The Minerva Image Resource (MIR) is a centralized repository of personally identifiable data, which covers both images and linked medical data.
ETHICS AND DATA FLOW Retrospective Data Patient datasets that comprise collected data and images from previous projects can be joined into the MIR if the existing consent allows for data use for general research purposes. The threshold that should be met for data to be contributed to the MIR is consented for use in research, not necessarily consented for publication. No identifiable information held in the MIR is shared outside the scope of legal agreements covering agreed research purposes. The MC has a defined standardized Material Transfer Agreement and collaborator letter that is recommended for use within the consortium, but ultimately it is up to each clinical collaborator and phenotyping group to sign any given agreement. It is up to MC Clinical Collaborators to vouch that this consent is in place and that the wording of the patient consent encompasses image data.
Prospective Data Clinicians can recruit patients into the Minerva Initiative if they obtain consent for the use of patient's data and images collected through routine clinical practice. The Minerva Consortium have drafted patient information and consent forms, but locally valid research ethics approvals need to be sought for the inclusion of patient data and images in Minerva Consortium research. The patient consent forms cover a broad remit of health-related research using images and associated clinical data. Once research ethics committee-approved based consent has been given and a Data Submission Agreement is in place, data can be entered into the MIR and become part of the Minerva Initiative. The data flow pathway for this is not prescriptive. The data could, in future, be deposited in the MIR directly or through affiliated third-party apps or other health care record platforms.
Analyzed Data Identifiable data within the MIR is only available to the original data submitters or MC Computational Phenotyping Groups. MC Computational Phenotyping Group researchers could be from all over the world and may be working in academia or commercial companies. Access to the Minerva Image Resource is only granted for purposes that align with the research goals of the Minerva Initiative (to improve diagnosis, clinical treatment, and understanding of a wide range of illnesses). Data access conditions are further explained below. The MIR, as a precompetitive space, does not enforce a single approach or initiative but rather seeks to encourage multiple efforts. There is the expectation that results from analyses on the images and identifiable data will be returned and shared within the MC.
COLLABORATION ENABLING NETWORK One of the purposes of the MC is to encourage and enable collaborative work on identifiable patient data. These collaborative initiatives are not centrally coordinated but are expected to be conducted through normal scientific practice. In addition, there is a framework for collective attribution through a publication policy. Any data submitted that contribute to a publication using the pooled dataset will be acknowledged in accordance with the guidelines of the International Committee of Medical Journal Editors ("ICMJE | Recommendations | Defining the Role of Authors and Contributors", n. d. ). We envisage two broad groups of publications to arise from project data, each with distinct guidelines for recognizing contributions: I. "Core" MC papers have a broad focus and use large amounts of data from multiple aspects of the project.
Named authors (details below) plus "Minerva Consortium" with all project participants listed in the end matter [cf. Mells et al. WTCCC3 paper (Mells et al. , 2011) ] II. "Affiliated" MC papers arising from more specific collaborations focused on a particular phenotype, methodology, policy question, etc. Named authors based on contribution and the Minerva Consortium as a "corporate" author [project participants not listed, cf. Firth, Wright & DDD Study paper  (Firth et al. , 2011) ]. Manuscripts derived from research conducted on data acquired through the Minerva Initiative are reviewed by a Publication Review Group to affirm that author attribution follows the publication policy. The publication review group also ensures that all statements, and images used in Minerva Initiative publications comply with participant consents, image usage rights, data privacy, and data governance considerations.
IDENTIFIABLE INFORMATION FLOW AND INTELLECTUAL PROPERTY While analyses produced by models are shared within the Minerva Initiative, the initiative does not require that the models themselves are shared. This is to ensure data protection and intellectual property rights. For many models being trained, there is a large gap in understanding of interpretability-to the degree that there is no guarantee that personally identifiable information from individuals in the training data might not be recoverable. Thus, to assure future compliance with the protection of individuals' data rights within the MIR, the models must not be shared without corresponding data governance assurances. Secondly, as the Minerva Initiative allows the coexistence of both academic and commercial enterprise initiatives, the fair and equitable basis of IP domains must be clear.
In other words, in line with personality rights, ownership of the data and photographs remains with the original creators (the person in the image). The means to compare data between people, the models, is where we expect new intellectual property to be created. It should also be noted that for most anticipated uses, models, in themselves, will contribute very little without access to the datasets. To successfully identify patients with rare disease and match them with others around the world, broad and unfettered interrogation of data is key. No model will have the accuracy or confidence to make assertions about rare diseases without reference back to the original patients and data. Consequently, it is in every stakeholder's interest that the data are shared as openly and as widely as possible.
JOINING THE MINERVA CONSORTIUM AND DATA ACCESS CONDITIONS As a clinician or research academic seeking to collaborate on de-identified data or wishing to submit consented patient data to the MIR for analysis, the Minerva Initiative is open to join. The criteria are a formal affiliation and good standing with professional organizations in either a clinical or academic capacity. The Minerva Consortium relies on a peer system of oversight on membership, with the Management Group having power to rule in disputes. In the case of a group seeking to become a Phenotyping Group, who can access identifiable data held in the MIR, this status should only be granted to who align with the research goals of the Minerva Initiative (to improve diagnosis, clinical treatment, and understanding of a wide range of illnesses).
CONCLUSION The Minerva Initiative is a framework for global collaboration on identifiable patient data including photographs. It is designed to complement current initiatives for global data sharing in rare diseases by specifically addressing the analysis of data where anonymization is not possible. The Minerva Initiative directly addresses the ethical, legal, and data security challenges associated with inherently identifiable data, thereby enabling big data research. We envisage that the frameworks established within the Minerva Initiative will provide a useful community model to ensure that the amount or variety of identifiable data that any one group, institution, or country can assemble is no longer the limiting factor for advancements in clinical translation of new machine learning methods.
There are several ongoing missions to planets and small bodies beyond Mars. Space agencies are currently planning missions of astrobiological interest to promising targets such as Ganymede, Enceladus, and Europa. Therefore, updating the COSPAR Planetary Protection Policy is timely and of the utmost importance. In March 2016, the project ''Planetary Protection of the Outer Solar System'' (PPOSS) funded by the European Commission's H2020 Programme was kicked off. This project was intended to provide an international platform and forum where various communities (e. g. , policy makers, scientific community, and industry) gather and exchange views on the matter of planetary protection with a focus on outer Solar System bodies. Through an intensive 3-year program  (2016) (2017) (2018) , the project has nurtured and catalyzed discussions to define strategic science and policy recommendations related to the prevention of biological and organic contamination in the frame of the exploration of the outer Solar System bodies.
, 2005). The magnetospheres of Jupiter and Saturn also exhibit high fluxes of energetic electrons that can interact with the surface layers of Europa and Enceladus down to a depth of the order of a meter or so if one also considers their bremsstrahlung photons. Heavy ions induce severe, and difficult to repair, damages in all living organisms  (Cucinotta et al. , 2017; Moeller et al. , 2017). Data regarding the radiation environment on and around Europa from the Voyager and Galileo spacecraft were used to model the depth profile of radiation within the europan ice  (Paranicas et al. , 2007 (Paranicas et al. , , 2009)). The penetration depth depends on the type and energy of the particle. The derived yearly radiation dose at 1 m depth was calculated to be 0. 3 Gy/a. Thus, only a few hundred Gray would be accumulated during a stay in that depth of 1000 years, if no transfer or exchange with the deeper subsurface occurs.
Acknowledgements The financial support of  OTKA K115939  and  K128410 , as well as the Nanoporous and  Nanostructured Materials  for Medical Applications projects ( H2020-MSCA-RISE-2016-734641  Nanomed) is gratefully acknowledged. B. B. is grateful for the PhD scholarship from the  Institut Laue-Langevin  and the  George Olah Doctorate School of the Budapest University of Technology and Economics. The authors thank  E. Geissler  for linguistic corrections.
Statistical analysis/Multi-state analysis Analyses were done using the statistical software R 3. 4. 2  42. Categorical data were summarized as absolute and relative frequency. Metric data were summarized by median, minimum, and maximum. Multi-state analysis was performed to analyze the probabilities and risk factors for non, low, and highly progressive disease requiring multiple interventions following diagnosis, as well as to develop a prediction model with starting point 2 years after diagnosis. For every patient, the outcome of interest was the types of intervention over time, differentiated into surgical and non-surgical intervention (ChT or RT). Accordingly, a Markovian MSM with the clock-forward approach and unidirectional arrows was used. Time was measured in years from diagnosis. Supplemental Figure  S1  displays the schematic model of possible states and transitions used for first data structuring.
State-probabilities at certain time points for the whole cohort and subgroups with at least 40 patients per group were obtained from the Aalen-Johansen estimate 26 calculated by the "mstate" R-package  29. State-probabilities were based on a transition-specific Cox model. For better inter-study comparability of our results we chose identical classification of subgroups as stated in the preceding HIT-LGG 1996 study  5  and confirmed in the SIOP-LGG 2004 trial for non-NF-1-patients receiving ChT  16. Cofactors for analysis of state-specific risk groups and influences were as follows: age group according to age at diagnosis (< 1 year, 1-7 11/12 years, ≥ 8 years), sex, main histology, main location, and NF-1 status. Stacked-plot figures were produced with the "mstate" R-package  29  and are shown with state-probabilities 5 years after diagnosis starting (i) from state 1 at time of diagnosis (Figure  3 4 5 6 7 8 ) or (ii) from different states at 2 years after diagnosis (Figure  9 ).
Demographic and clinical characteristics of cohort Table  1  summarizes demographic and clinical characteristics of our cohort. We identified 1587 patients with primary diagnosis PLGG treated in Germany from the SIOP-LGG 2004 database and followed them up until April 27 th , 2016. Median follow-up was 6. 3 years. Five years after diagnosis, 36 patients had deceased and 10 tumors had transformed to high-grade glioma. Throughout the observation time we identified 22 disease states and 44 transitions between states, displayed in Figure  1:  • 1229/236/40/5/1 patients received 1/2/3/4/5 surgical interventions without prior adjuvant therapy, respectively. • 475/140/59/20/3 patients received 1/2/3/4/5 adjuvant therapies with or without prior surgical intervention, respectively. • 27 transformed to high-grade histology.
Novelty and Impact First multi-state analysis of a very large population-based cohort of pediatric low-grade glioma [PLGG]. It extends classical survival analysis by considering subsequent and competing events of chronic diseases, such as PLGG. Our seven-state model delineated a more refined course over time, including influence of subsequent events and prognostic factors. We identified three levels of progressiveness and generated a prediction model for PLGG-disease. This unique approach will be validated prospectively in the ongoing LOGGIC Core Bioclinical Databank.
Materials and Methods The hard X-ray Raman experiment for liquid water and ice was performed by using the XRS spectroscopy instrument (51) at the beamline ID20 of the European Synchrotron Radiation Facility. The momentum transfer used for detection was q = 2. 6 ± 0. 6 Å-1. The scans for the ice sample were performed from below and from above to confirm that radiation damage did not introduce an error in the data (SI Appendix, Fig. S2 ). For the experiment, the liquid water sample was filled into a custom-made flow cell  (52) , and the ice sample was prepared in situ in a 2-mm quartz capillary continuously cooled by using a cryostream (Oxford cryosystems) at approximately at -88 • C. For both samples, milli-Q water was used. The raw data were handled as described in ref. 53. The intensity integral values in the data are presented in SI Appendix, Table  S1.
ACKNOWLEDGMENTS. We thank  Prof. M. Odelius  for providing us with an ab initio MD (AIMD) simulation for core-hole dynamics; and  Prof. J. Tse  for providing us the XRS spectra of ice phases. S. E. , R. M. J. and A. F. were supported by  European Research Council   Advanced Grants 2014 Advanced Investigator Grant   669531  " Beating Complexity through Selectivity: Excited State Dynamics from Anti-Stokes and Non-Linear Resonant Inelastic X-Ray Scattering " under the  Horizon 2020 European Union Framework Program for Research and Innovation. Parts of this research were performed at the ADRESS beamline of the Swiss Light Source at the  Paul Scherrer Institut (PSI)  with  SAXES spectrometer in Villigen PSI, Switzerland. The work at  PSI  is supported by the  Swiss National Science Foundation (SNSF)  through the  National Centre for Competence in Research MARVEL (Grant   51NF40 141828 ), through the  Sinergia network Mott Physics Beyond the Heisenberg Model  (Grant  CRSII2 160765/1 ), and a  Deutschland-Austria-Confoederatio Helvetica  project ( SNSF Research Grant   200021L 141325 ).
X. L. was supported by the  European Community 's  Seventh Framework Program  FP7/2007-2013 under Grant  290605 (PSIFELLOW/COFUND ). AIMD simulations underlying the XES spectrum simulations were performed on resources provided by the  Swedish National Infrastructure for Computing. V. K. and F. G. were supported within the  State Contract of the Ministry of Education and Science of the Russian Federation for Siberian Federal University  for Scientific Research in 2017-2019 Project 3. 2662. 2017; Knut and Alice Wallenberg Foundation  Grant  KAW-2013. 0020 ;and the  Swedish Research Council.