dataesr/scientific-paragraphs-categorization huggingface dataset:
According to Button and colleagues  [17] , "the average statistical power of neuroscience studies is very low. The consequences of this include overestimates of effect size and low reproducibility of the results. " Their analysis used a database of 49 meta-analyses published in 2011 in the field of neuroscience, most pertaining to psychiatric disorders or neurological diseases. This raises two questions. First, is the replication rate of initial studies in psychiatry similar to neurology? Second, how do these compare to a sample of somatic diseases? The present study was designed to address both questions. To assess the credibility of studies investigating the association of markers or risk factors with diseases, we collected all meta-analyses published during a five year period related to four psychiatric disorders (attention deficit hyperactivity disorder (ADHD), autism, unipolar depression, schizophrenia), four neurological diseases (Alzheimer and Parkinson diseases, epilepsy and multiple sclerosis) and four somatic diseases (breast cancer, glaucoma, psoriasis and rheumatoid arthritis).
Articles were excluded if: 1) they were related to the treatment, screening or diagnosis of the disease, 2) if they reported on voxel-based brain imaging studies, 3) the disease itself was the risk factor for another outcome, 4) no meta-analysis was conducted, or 5) the article was not related or relevant to the disease. Meta-analyses were included if: 1) they compiled results from at least 7 distinct datasets reported in at least 4 independent publications, 2) the effect size was expressed as mean difference, standardized mean difference (Cohen's d), odds ratio (OR) or risk ratio (RR), and 3) parameters were measured and/or validated by professionals (i. e. , psychological traits that were only self-assessed by subjects were not considered). A number of articles reported several meta-analyses.
We included all of them providing that they were dealing with distinct parameters or risk factors. If an article reported several metaanalyses on the same parameter and the same outcome, we selected the most comprehensive one (i. e. , the one containing the most datasets) and the one reporting the fixed effects rather than the random effects model. When two or more articles dealt with the same parameter and the same outcome, the most recent one was used. For genetic association studies, we used the meta-analysis results for the allelic model when the data were available. However, when the corresponding initial study put forward another model we selected this one.
Selection of initial studies and largest studies From each meta-analysis we identified the initial study and we collected its full text article. We checked them to assert that no study on the same topic was previously published. We thus identified 110 initial studies that were not listed in the corresponding meta-analyses. These omissions were mainly due to two reasons. First, a meta-analysis article of 36 associations between brain imaging observations and schizophrenia explicitly mentioned that data published before 1998 were not taken into account. Therefore, we looked for the "true" initial studies. Second, initial studies were often replicated by the same group using an extended population and the second publication often included the initial data. Therefore, the corresponding meta-analyses did not include the initial study.
Data extraction Data were extracted independently by two authors (EDM and FG), and discrepancies resolved by mutual consent. A third author (KB) conducted a further 10% data check, which resulted in a small number of errors being detected and corrected. The following data were extracted from initial, largest and meta-analysis articles: year of publication, first author, 2012 Impact Factor of the journal that published it, nominal statistical significance (based on p<0. 05), effect size, its p-value for datasets reporting a significant effect or its confidence interval for non-significant ones, number of patients and of healthy controls. Moreover, regarding meta-analyses we also extracted the number of included datasets. If the effect size was reported as a mean difference, it was converted to Cohen's d using the software OpenMetaAnalyst  [18].
Defining a small effect We used our dataset of meta-analyses reporting a significant association to define a small effect in each of our four association types: cognitive/behavioral, brain imaging, genetic and other. For each category we sorted in quintiles the significant effect sizes. The largest effect size of the lowest quintile was defined as the maximal value of a small effect. All effects sizes of the 65 significant cognitive/behavioral studies were expressed as standardized mean difference and a small effect was estimated to be d 0. 51. All but two effect sizes of the 69 significant brain imaging studies were expressed as standardized mean difference. The two others were expressed as odds ratio and were converted to standardized mean difference. This gave a small effect as d 0. 29.
Here we used our database of 425 significant associations reported by meta-analyses to define a small effect in each of our four association types: cognitive/behavioral, brain imaging, genetic and other (see Methods). We therefore conducted a sensitivity analysis where we restricted our analysis to associations investigated by stringent meta-analyses. We defined a meta-analysis as stringent either if it reported an effect as significant at p<0. 005 or, when non-significant, if its confidence interval excluded a threshold effect, as determined relatively to typical effect sizes in that field. Using these criteria we categorized 385 meta-analyses as stringent, of which 326 reported a significant effect (Table  2 ). When initial studies were compared to these corresponding stringent meta-analyses, sensitivity and specificity remained similar to those observed when all 663 associations were considered (Table  2 ).
Are initial studies representative of later studies? Fig  4  shows the relationship between the number of patients in initial studies and the average number of patients in subsequent ones. This average number of patients was calculated by subtracting the number of patients in initial studies from the total number of patients included in each meta-analysis and by dividing it by the number of datasets minus one. For the eight subgroups, the sample size of initial studies was always smaller than for the averaged subsequent Relationship between the number of patients in initial studies and the average number of patients in subsequent studies. The average number of patients in subsequent studies was calculated by subtracting the number of patients in initial studies from the total number of patients included in each metaanalysis and by dividing it by the number of datasets minus one.
When the "true" effect size is large, as in our sample of cognitive/behavioral studies, even initial studies with a small sample size are replicated. In contrast, initial genetic studies related to psychiatry were less often replicated than in neurology not because their sample size was smaller but because it was too small to accurately deal with a much smaller "true" effect size. The fact that the replication rate of largest studies is much better than that of initial studies further supports the view that inadequate sample size is a major cause of low replication validity. Three previous studies have already highlighted that many brain imaging studies report inflated effect sizes  [28] [29] [30]. In particular, Ioannidis evaluated the 461 primary datasets included in 41 meta-analyses on the associations between psychiatric disorders and brain volume abnormalities  [28].
Limitations Our study is based on the view that the summary effect reported by a meta-analysis is a reliable estimate of the "true" effect. For this reason we only considered meta-analyses dealing with at least seven independent datasets published in at least 4 independent publications. Indeed, a comparison between pairs of meta-analyses published at a five-years interval about the same association showed that the change in summary effect size was inversely related to the number of datasets  [20]. Despite this selection criterion, it is likely that some meta-analyses of our sample still reported an inflated effect or even a false positive effect. Indeed, publication bias (i. e. , the preferential publication of positive results) and the inclusion of primary studies with low methodological quality are likely to inflate the effect size estimated by meta-analyses  [34, 35].
However, without study pre-registration, these biases are difficult to detect. Several tests have been proposed to identify publication bias in meta-analyses and to correct for it  [34, 35, 36, 37] , but these methods suffer from limitations and often require meticulous explicit modeling of each potential source of bias  [37, 38]. Ioannidis and colleagues propose two methods to probe the robustness of meta-analyses that report a statistically significant effect at p<0. 05  [20, 22, 39]. Both methods require no assumption about the sources of potential biases, but are not easy to apply in the context of our large database. However, these studies inspired us to adopt a simpler approach to identify, among nominally significant meta-analyses, those likely to retain their statistical significance with the inclusion of further studies.
05  [22]. Among them, 27 had a p value below 0. 005 of which 26 were analyzed as providing at least a strong support to the association. In contrast, none of the 23 genetic associations with a reported p value between 0. 5 and 0. 005 provided a strong support. Ioannidis listed small sample size and small "true" effect size as two major causes of replication failure  [1]. However, he also highlighted other causes related to the methodological quality of the studies and to the general characteristics of each research field (i. e. , the number of associations tested, the influence of financial interests and the strength of the competition between researchers)  [1]. However, because these other causes of replication failure are difficult to identify and quantify, especially when considering a large database, they were not investigated in the present study.
We collected and analyzed the data of 663 initial studies and their corresponding largest studies and meta-analyses. We did not individually consider each of the 10,154 primary datasets included in these 663 meta-analyses. Therefore, while we provide direct data about the replication rate of initial and largest studies, we have only indirect and limited evidence regarding that of other primary studies in each subgroup. Our emphasis on initial studies, and especially on those reporting a significant effect, is justified by their influence on subsequent research efforts  [15] , and by the fact that they are much more echoed by the media than subsequent studies  [40]. Our selection of 12 pathologies is partly arbitrary. Regarding neurological diseases we excluded stroke because it is also a cardiovascular disease, traumatic brain injury because it is accidental and autism, which is rather a psychiatric disorder.
Thus, our selection of neurological diseases includes all four severe diseases exhibiting the highest prevalence rate  [41]. Our selection of four psychiatric disorders is less systematic: it included two severe disorders (autism and schizophrenia) and two less severe and more prevalent disorders (ADHD and unipolar depression). Finally, our selection of four somatic diseases is even more arbitrary. We selected them because their biological causes and risk factors are still poorly elucidated and because they cover a range of prevalence similar to that of both other domains. Our sample of initial studies was extracted from meta-analyses built from at least seven primary datasets. We did not investigate initial studies not followed by meta-analyses or included in less extensive meta-analyses.
Making sense of big data in health research On 30 October 2015, the Health Directorate of the Directorate-General for Research and Innovation at the European Commission (EC), the executive body of the EU, organized in Luxembourg a workshop entitled "Big data in health research: an EU action plan"  [7]. The aim of the workshop was to ask stakeholders in the "big data revolution" for their input on how European funding for health research should take into account the opportunities, limitations, and concerns of the anticipated developments in health and healthcare. Participants included bioinformaticians, computational biologists, genome scientists, drug developers, biobanking experts, experimental biologists, biostatisticians, information and communication technology (ICT) experts, public health researchers, clinicians, public policy experts, representatives of health services, patient advocacy groups, the pharmaceutical industry, and ICT companies.
Another goal of the workshop was to develop an EU action plan for research funders towards the integration of big data into policy development, biomedical research, and clinical practice in health and wellness management. Big data comes from a variety of sources, such as clinical trials, electronic health records (EHRs), patient registries and databases, multidimensional data from genomic, epigenomic, transcriptomic, proteomic, metabolomic, and microbiomic measurements, and medical imaging. More recently, data are being integrated from social media, socioeconomic or behavioral indicators, occupational information, mobile applications, or environmental monitoring  [11]. Big data comes in a wide range of formats. Data streams have to be assessed and interpreted in a timely manner to benefit patients affected by diseases and to help citizens remain in good health  [8, 12].
Multidimensional signatures that take into account a wealth of prior information, both from the patient's previous life history and state-of-the-art information from the literature and relevant databases, will hopefully deliver a much higher predictive power than the single biomarkers used today. There is also potential for research on the impact of healthcare interventions and monitoring trends in infectious diseases to inform public health policies  [40]. Finally, there is an opportunity to engage with the individual patient more closely and import data from mobile health applications or connected devices. This interaction with the patient will result in the collection of more detailed clinical, environmental, and lifestyle information, such as heart frequency and body temperature, physical activity and nutrition habits, and sleep and stress management, which will prevent risk exposure and disease onset  [41].
For example, there is no consensus within international law on whether specific requirements should be applicable to genetic information. Several documents exist at the regional and international levels that include useful guidelines, such as the UNESCO International Declaration on Human Genetic Data (2003)  [97]  and the Organisation for Economic Co-operation and Development (OECD) Guidelines on Human Biobanks and Genetic Research Databases (2009)  [98]. The GA4GH has developed the Framework for Responsible Sharing of Genomic and Health-Related Data  [99].
Acknowledgements We would like to thank the organizers of the EC workshop  Anders Colver ,  Tomasz Dylag ,  Christina Kyriakopoulou , and  Sasa Jenko , who serve as scientific officers at the  EC Health Directorate. Big data in health research: an  EU  action plan workshop was organized by the  Health Directorate of the Directorate-General for Research and Innovation at the European Commission  with the contribution of the  Innovative Medicines Initiative office, Digital Society, Trust & Security Directorate of the Directorate General for Communications Networks, Content & Technology, Health systems and products Directorate of Directorate-General for Health and Food Safety, Joint Research Centre  and EUROSTAT http://bigdata2015. uni. lu/eng/European-Commission-satellite-workshop. http://ec.
Methodology Our sample consisted of 52 students from the University of Lille 3 (France), out of 55 students in the first year of our Master program. Between September and December 2014 they followed a lecture on information rights and ethics that is an obligatory part of the Master "Information and Documentation". Most students are from France but some 20% are from foreign countries (Europe, Maghreb, Sub-Saharan Africa, China). Half of the students already have professional experience, often as teachers or information professionals. After the twoyear Master program, one part of these students will become public, high school or university librarians while others will work in public administration or in the corporate sector, in jobs related to documentation, web design and referencing, community management, economic intelligence, records management, database administration, knowledge organization etc.
In their own words: "The goal of information professionals is to make all kinds of data accessible. " "One of the most important tasks of the librarian is to promote availability of information. " "The library staff cannot act on the author's intellectual property as they want (yet) each user has the right to get the same quantity and quality of information as anyone else. " For the students, the primary function of the librarian is to help and support the patron's information needs and rights, not to make compromises between those needs and intellectual property issues. Their viewpoint has more to do with advocacy than with professionalism and law. Responsibilities towards individuals and society 15% of the other students share this opinion of the political character of the librarian's role, as they emphasize his/her social responsibility (Clause 2) towards the society as a whole but moreover and most of all towards the individual patron as a real person, member of a cultural community, a social class or an ethnic group.
Whether they decide to lend this book or not, are they acting "for freedom of expression or against racist propaganda"? Is this censorship? One student conducted a survey on the sociology of the librarian's work. She reports that librarians do not want to buy everything their patrons ask for, especially when they ask for sentimental novels or pulp fiction, and she adds that "this poses a problem because the user can feel illegitimate in his/her reading practices. However, can librarians judge the legitimacy of their patrons' reading behaviour?" Privacy and transparency We also mentioned above that privacy does not seem to be the most important issue in our sample. Those students who deal with this clause express concern with section 215 of the USA PATRIOT Act on access to records and other items under the Foreign Intelligence Surveillance Act, including the possibility to have access to data collected and stored by public and scientific libraries.
Again, the students' question is which role a library should play protection of the patron-citizen against authorities, or support of public action against crime and terrorism? Should a librarian alert security agencies because of suspicious information behaviour? 6 And in fact, what is suspicious information behaviourreading about Islamic religion and history, an interest for specific fields of chemistry? At least some students appear to know the US debate on the PATRIOT Act and libraries very well, in particular through conferences and publications of the American Library Association. Also, students do not cite many real cases regarding transparency. The main opinion is that a policy of open data will foster democracy and increase freedom of information and transparency. One student adds that librarians should rather inform people about their civil rights than do "whistleblowing" because they are either not able or not willing to do this, again because of political dependency.
These data should remain confidential and be stored in a secure way, without disclosure to corporate societies, governmental agencies or other patrons. In their own words: "Privacy is a matter of data and also of usage. " "Searching for documents about specific topics provide information on the patron's interest subject to protection of privacy, e. g. on religion, travel destinations, political preference etc. " A last field of potential conflict is intellectual property, i. e. the conflict between the legal constraints and obligations and the ethics of freedom of information and open access, highly valued by students. Students express in quite a similar way what Tomas Lipinski stated some years ago as follows: "ownership or control rights often conflict with other rights such as access to information, privacy, or a general sense of free speech" (2012).
Another aspect is that information ethics should not be taught as a limited "knowledge field", complementary to other, technological or organisational topics, but as a transversal approach to different domains and in different lectures, on database administration as well as on knowledge management, information architecture, marketing or law. Students do not learn ethics as they learn XML or a foreign language. Ethics make sense when applied to concrete situations. Education on ethics should focus on conflicts and criticisms, on the students' cognitive dissonance rather than on fundamental values. As said above, they are generally aware of these values but they are also aware of limits and conflicts with work reality, local authorities, communities, government etc. Teaching should focus on these conflicts in order to help students to find their own solutions, mentally as well as behaviourally -especially for the main conflicts between universal ethics and communitarian values, and between fundamental rights, local authorities and the security policy of the State.
Data includes the time of first payment of each user, an individual and conscious action that tracks adoption behaviour. In addition we follow the "subscription" service over 42 months since 2008 (for results see Supplementary Information  [SI] ). In contrast to other empirical studies where incomplete knowledge about the underlying social network leads to unavoidable bias  [22] , we use here the largest connected component of the aggregated free Skype service as the underlying structure, where nodes are Skype users and links confirmed contacts between them. This is a good approximation since it maps all connections in the Skype social network without sampling, and the paid service is only available for individuals already enrolled in the Skype network. The underlying structure is an aggregate from September 2003 to November 2011 (i.
1b ), which in turn induce a percolating component of connected stable nodes that holds the global adoption cluster together (Fig. 1c ). Consequently, in the structure of the adoption network primary triggering effects are important only locally, while external and secondary triggering mechanisms seem to be responsible for the emergence of global-scale adoption. To model the observed dynamics and explore the effect of immune nodes, we perform extensive numerical simulations of the threshold model with parameters determined directly from the data (see MM and SI). We use a network structure with empirical degree and threshold distributions and fix p n = 0. 00019 as the constant rate of innovators, implying that the time scale of a Monte Carlo iteration in the model is 1 month. We measure the average size of the largest (LC) and second largest (LC 2nd ) connected components of the background social network, and of the stable, vulnerable and global adoption networks, as a function of the fraction of immune nodes r.