dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods: The systematic review will include both prospective and retrospective articles. Five databases will be searched: PubMed, Cochrane CENTRAL, EMBASE, and ScienceDirect. In addition, a search of gray literature will be performed using Google Scholar and ClinicalTrials. gov. Searches will be circumscribed to include only older adults (aged over 60 years) who underwent a bipedal quiet standing measure of their balance and for whom the number of falls was reported. Two authors will independently assess the risk of bias for each included article using a 26-item checklist. Funnel plots will be drawn to attest of possible publication biases for each COP parameters. The results will be synthesized descriptively and a meta-analysis will be undertaken. When trial methodological heterogeneity is too great for pooling of the data into a meta-analysis, evidence strength will be evaluated using best evidence analysis.
Search strategy An electronic database search of titles and abstracts published will be performed between March 2017 and July 2019 to identify all articles published that include fall data for older people and their COP recordings. Five databases (PubMed, Cochrane CENTRAL, EMBASE, and ScienceDirect) will be used as sources for published articles. The search will be performed for articles published without date restriction until July 1, 2019, using associations of keywords (Table  1 ) from the PICO methodology. The following MeSH terms will be also used: "Accidental Falls/prevention & control," "Accidental Falls/statistics & numerical data*," "Aged," "Postural Balance/physiology*," "Posture/physiology*," "Predictive Value of Tests," and "Regression Analysis. " The main database search will be supplemented by a review of gray literature which will be conducted through web searches on Google Scholar and ClinicalTrials.
Paper review process Potentially eligible studies will be screened for inclusion eligibility independently by two review authors (FQ and AV) based on their title, abstract, and full text. Articles will first be imported into the Zotero® bibliographic database (Corporation for Digital Scholarship and the Roy Rosenzweig Center for History and New Media, USA) before screening so that all articles can be reviewed from the same source in order to select those that meet the criteria. If there is disagreement between the reviewers, the study will be discussed until a consensus is reached. Papers that are eligible will then be subjected to data extraction and a "risk of bias" evaluation, as described below.
Orthopedic disorders affecting balance such as recent arthroplasty or amputation will also not be included in the review. Intervention criteria -Articles analyzing the balance through COP recordings during quiet standing with both feet on the ground and evaluating the risk of falling by the number of falls during a period of time (retrospectively or prospectively) -Any article measuring the risk of falling without an estimation of the number of falls per participant (i. e. , indirect assessment through fear of falling tests or epidemiologic data only) or not related to the risk of falling (comparing elderly vs. young for example) will be discarded. -If training (e. g. , exercise training or a physiotherapy program) is a part of the intervention, the article will be discarded unless a baseline of the quiet standing capacities is recorded.
Outcome criteria -A COP recording is mandatory to not be excluded. All studies than do not compute any parameter to quantify balance through COP data but focus on sway measurement only through sway meter, cumulative balance score (e. g. , Sensory Organization Test) or motion capture will be discarded. Studies using Center of Mass (COM) without a COP recording will be discarded too. Equipment criteria -There are no equipment criteria as long as the research recorded COP displacement over time. Force platforms, pressure insoles, or any other COP recording systems are all accepted but will be noted. Population criteria -All studies including young (< 60 years old), healthy people without a comparison group of older people will be discarded. -The presence of a neurologic pathology that could influence posture will be an exclusion criterion.
Data extraction and analysis Following inclusion of the articles for analysis, the text from each reference will be imported into Microsoft Excel (version 2013, Microsoft Corp. , Redmond, WA) for data extraction. One assessor (FQ) will extract and collate information following the recommendations of the Joanna Briggs Institute Reviewers' Manual  [47]. Another assessor (AV) will verify the extracted data from the included articles in order to confirm coherence of the data. Key characteristics to be extracted will include information about the study itself such as author(s), title, year of publication, inclusion and exclusion criteria, sample size, study methodology (retrospective or prospective fall evaluation), study duration, rate of falls, and mention of any adverse events that occurred during the study (Additional file 1).
Population characteristics will also be recorded including demographic and biometric data such as participants' gender, age, weight, height, BMI, and cognitive capacities (e. g. , following a Mini Mental State Examination-MMSE). Data gathered about the falls will include the studies' definition of a fall and how they were evaluated and the geographical location of the work (country, region, and establishment where the measures took place); quiet standing test parameters to be collected will include test conditions of the tests such as, for participants, whether they wore shoes or were barefoot, had their eyes open or closed, or if they were asked to use a comfortable or standardized foot position. For the test itself, data will be recorded on the type of standing surface (e. g. , firm or foam) used, whether it was a cognitive double or single task, test duration, who performed the tests, the time interval between the different test parts, the data collection methods (type of tools, sampling frequency, and filter characteristics), and the COP features.
For predictive (in prospective studies) or classification (in retrospectives studies) models, their characteristics and level of accuracy will also be extracted, when a statistical model has been used. When these data are unavailable from the main text, Additional file 1 will be examined for more information. When data on the force platforms or other kind of equipment (such as the sampling frequency or the provider) are not available even in Additional file 1, the specifications will be sought from other articles by the same author(s). For experimental studies, the available COP data will be extracted from the baseline measurements that were taken before any intervention had been implemented as long as the history of fall is also available (retrospective classification). If the COP parameters before the intervention are not included, the article will not be analyzed.
For observational studies with prospective evaluation of falls, data recorded before the followup assessment will used as in the analysis; if measurements were not performed before follow-up, the article will be excluded. Using software (like Plot Digitizer) to obtain data from figures was not considered as an option to extract data since this technique has been shown to be flawed concerning inter-rater reliability, with only a 50% agreement between both raters and an agreement of 70% with the original data even for trained raters  [48]. In addition to the time consumption of extracting data by two authors independently, there is no guidance for this kind of extraction so far  [49]. Therefore, we had rather not extracting the data on graphs to avoid introducing new biases. Finally, authors will be contacted via e-mail up to three times to request missing data when they are not available in the main text or from other sources as described above.
Strategy for data synthesis Extracted data from included articles will be presented descriptively, especially study characteristics, population characteristics, COP features used, and the risk of bias. The risk of bias will be assessed using the value of the percentage scores from the 26-item checklist: score distribution will also be studied to look for a Gaussian distribution or, on the contrary, a trend in favor of the studies included in the meta-analysis. The quality scores will also be used as a parameter of the COP heterogeneity level in the meta-analysis. For pooling predictor data from COP recordings, at least three studies must have used the same feature. If the included studies show consistency between their protocols, particularly with regard to the homogeneity of patient populations and the quiet standing test conditions, a meta-analysis of the aggregated data will be considered.
Inter and intra-participant reliability for the different COP parameters will also be discussed in order to assess their usefulness in clinical practice  [56] [57] [58]. If the data are detailed enough, the causes of falls will be investigated further to determine whether external factors independent of balance disorders were involved in the fall/non-fall status; such external factors could weaken the overall ability of COP measures to predict falls. If the heterogeneity for a given COP parameter within the meta-analysis is too great (as measured by I 2 > 50%), the decrease of this heterogeneity will be tested by the deletion of studies that use a particular COP recording configurations (with a different material than the other studies included for this parameter for example); the heterogeneity decrease will then be discussed in relation to the study(s) deleted.
Discussion This systematic review is expected to provide a valuable means of predicting and so preventing falls in older individuals by providing robust, evidence-based guidelines for the clinical and laboratory evaluation of risk of falling using a simple and reliable bipedal COP test. The proposed study will retrieve and extract data from clinical trials and observational studies. It will report the spatio-temporal parameters of the center of pressure displacements during a bipedal quiet standing task in older people who are then classified as "fallers" and "non-fallers. " We have purposefully chosen bipedal tests because of the applicability of these tests to all older people. Unipedal tests, which are more difficult to perform, tend to exclude frailer individuals who find themselves unable to stand on one leg  [60].
32%) authors. While we are not disentangling the causes of this underrepresentation, it is interesting to note that since 2014 the rate of female trainees (students and postdocs) attending the Society for Neuroscience meeting oscillated between 49% and 50%, displaying a balanced pool of young candidates. Over the same period, only 30%-32% of faculty attendees were women, with no clear upwards trend. Please see Supporting information Data S1 for details on data collection and analysis.
ACKNOWLEDGEMENTS We thank  Aina Frau-Pascual  for her help with data collection, as well as for in-depth discussions on the content and contribution to the writing. We also thank the  BiasWatchNeuro team  for assistance in collecting data and helpful comments and  Dr. Anne Urai  for sharing useful materials (Supporting information Table  S1. n) and providing comments on the text. We thank the signatories (listed in Supporting information Table  S2 ) for their feedback and for supporting our work. Finally, we thank everyone involved for their actions toward a more inclusive field. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  654038  to  J. S. WiNRepo  received the support of the  Marie Curie Alumni Association.
In this paper, a variational inverse modeling method is used to determine the most probable 106 Ru source location and its magnitude using 106 Ru air concentration measurements. The decision to use a variational inverse technique was based on its ease of implementation and the fact that the computing time is less prohibitive than with Bayesian methods. We first describe the observations reported in Europe. Then, the methodology of the inverse method is presented and applied to identify the 106 Ru source location and the quantities released in the atmosphere from the most reliable location. The main results obtained by inverse modeling are discussed and validated by performing a modelto-data comparison. Observations Air Concentration Measurements. The available dataset of 106 Ru air concentrations (for further details see ref.
The simulations have a tendency to overestimate the observed deposits by about a factor of 3, partic-ularly in Dema-Ufa. However, the data are incomplete at this station, since daily deposition values are available only for 26 September. Other Russian stations are situated on the edge of the most significant deposits in an area of steep gradients (Fig. 6 ), which explains the difficulty of reproducing the deposits observed at these stations. Low deposit levels are recorded at several Russian stations including Dimitrovgrad at the beginning of October, despite somewhat limited air concentrations levels. The deposition simulations have difficulty in reproducing this event, despite simulated air concentrations that coincide rather well with the observed air concentrations. The simulated deposits in southeast Bulgaria are > 100 Bq/m 2.
Objective to assess the current use of big data and artificial intelligence (ai) in the field of rheumatic and musculoskeletal diseases (rMDs). Methods a systematic literature review was performed in PubMed MeDline in november 2018, with key words referring to big data, ai and rMDs. all original reports published in english were analysed. a mirror literature review was also performed outside of rMDs on the same number of articles. the number of data analysed, data sources and statistical methods used (traditional statistics, ai or both) were collected. the analysis compared findings within and beyond the field of rMDs. Results Of 567 articles relating to rMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. the mean number of data points was 746 million (range 2000-5 billion) in rMDs, and 9.
Key messages What is already known about this subject? ► Big data and artificial intelligence are rapidly evolving fields with the potential to profoundly modify rMD research and ultimately, patient care. What does this study add? ► this literature review showed the variety of big data sources, and the important heterogeneity in the methods used to analyse big data, with more than seven different methods, in rheumatology and in other medical fields. ] [10]  A clear definition of big data is needed as it appears that there is no consensual description, and that the meaning of this term has evolved during the last decades. The concept of big data was first defined in 1997 as 'data sets that are too large or complex for traditional data-processing application software to adequately deal with'  11  ; more recently, the European Medicines Agency (EMA) defined big data as 'extremely large datasets which may be complex, multi-dimensional, unstructured and heterogeneous, which are accumulating rapidly and which may be analysed computationally to reveal patterns, trends and associations'.
search strategy The SLR was performed on PubMed MEDLINE on 21st of November 2018 and updated on 19th of February 2019. The key words ('big data' (All Fields) OR 'Artificial Intelligence' (MeSH Terms)) were combined with ('musculoskeletal diseases' (MeSH Terms) OR 'musculoskeletal diseases' (All Fields) OR 'rheumatology' (MeSH Terms) OR 'rheumatology' (All Fields)). The following filters were applied: English language publications and studies performed in humans. The resulting articles were included if they reported use of big data (as defined by the articles' authors) in RMDs and were original articles. The mirror review outside RMDs was performed also in PubMed MEDLINE on 28th of November 2018 and updated on 20th of February 2019. The key words ('big data' (All Fields) OR 'Artificial Intelligence' (MeSH Terms)) and NOT RMDs were used, with the same filters as above.
The resulting articles were included if they reported use of big data (as defined by the authors) in healthcare but outside RMDs, and were original articles. Since this search was performed to obtain a mirror non-systematic review outside RMDs, we performed it in a retro-chronological way, including the most recent articles corresponding to our criteria, up to the same number of articles as found by the SLR in RMDs. One reviewer (JK) assessed titles and abstracts for suitability for inclusion in review, according to the pre-determined inclusion criteria, followed by full-text review (online supplementary table 1). Support from coauthors was provided, in particular when data scientist skills were needed. data extraction Data were extracted to answer the following questions: (1) the current definition of big data; (2) data sources of big data; and (3) type of analysis used to deal with big data.
To answer the question of the current definition of big data, on the one hand, definitions provided or referenced in the included articles were collected 12 ; on the other hand, the number of data points in the paper was reported. The number of data could refer to number of units of observation (eg, number of patients or number of MRI analysed) or the number of data point, it is to say the set of one or more measurements on a single member of unit of observation, if provided. 19 o answer the question of data sources of big data, we collected data sources and types and classified them into the following categories: clinical data; data provided by registers or cohorts; electronic health records; claims databases; trials or patient generated Health Data; biological data (including various kinds of -omic data); imaging data; and other kinds of data (including data provided by text mining from publications).
data analysis Findings were described and then compared between those covering RMDs and those from other medical   fields by non-parametric statistics. A comparison of statistical methods used to analyse big data was also performed in subgroups of data sources (clinical vs other sources), using exact Fisher's test, and the results were considered significant if the p value was below 0. 05. Meta-analysis was not appropriate; potential selection bias was not accounted for. Epidemiology Epidemiology Epidemiology In June 2019, a sensitivity analysis was performed using key words referring specifically to the different rheumatic diseases and to AI methods. This sensitivity analysis aimed to assess the additional number of articles that would be found using additional and more specific key words, without using the papers by extracting data from the relevant papers.
This study has strengths and weaknesses. First, only one reviewer performed the screening of the articles and extracted information from the selected articles. However, support was provided by coauthors, especially when data scientist skills were needed. Second, the research was only performed on PubMed MEDLINE with a language restriction. Ideally, multiple bibliographical databases would have been searched. The key words used to perform the SLR were restrictive. To assess the impact of the choice of key words, a sensitivity analysis showed that the use of additional key words referring to RMDs by name and/or to specific AI methods would have brought less than 10% more articles. We believe this indicates the validity of the present findings. Moreover, the aim was to obtain an overview rather than an exhaustive view of the topic.
Finally, classification of statistical methods, in particular machine learning methods, may be discussed. However, it was based on accepted classifications, which are considered reliable references in this field. 22 23  key finding from this review is that there is no consensual definition of big data. 7] [28]  The definition provided in recent EMA recommendations may be considered as a synthesis of all these notions. 12 Although all the authors of the selected articles agree that big data refers to a very large number of data points, there is also no consensual 'cut-off' to define what is meant by 'very large'. Some authors proposed log(n×p) superior or equal to 7 (n being the number of units of observation and p the number of variables),  29  however, giving the rapid growth of datasets in the last decade, some authors rather propose to think in terms of terabytes  (10 12  ) or petabytes  (10 15  ).
For workers, perceived job insecurity represents a threat and an obstacle towards achieving a decent and sustainable dimension of wellbeing at work and in life. Using the theoretical background of the psychology of sustainability and sustainable development, the aim of this study is to deepen the relation between subjective job insecurity, self-related health and life satisfaction considering the effect of personal resources, such as specific coping strategies that people may undertake facing job insecurity perceptions. The hypotheses were tested in a convenience sample of 769 employees in France. Data were collected with a self-report questionnaire and analyzed with the Statistical Package for the Social Science (IBM SPSS). Results showed that job insecurity was negatively related to self-reported health and life satisfaction; furthermore, problem-focused coping of the type of job, social support and training searching behaviors resulted in worsening this negative relationship.
Data Analysis All the measures were available in French and have been successfully used in earlier studies, except for the coping scale of which the psychometric characteristics were tested. The overall sample (N = 769) was randomly split into two subsamples. In the first subsample (N = 382) an exploratory factor analysis (EFA -PC extraction) was performed by IBM SPSS software (IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24. 0. Armonk, NY, USA. ) after the evaluation of the Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett's test of sphericity, which results indicated that conducting factor analysis on the data was appropriate (KMO = 0. 85; Bartlett's test = 2237. 27 (df = 55; p < 0. 001). Principal component (PC) and Varimax rotation analyses were used on the 11 items of the coping scale.
Results of the exploratory factor analysis showed that the chosen factor solution for specific coping strategies resulted in three factors (based on the scree test, eigenvalues > 1). This solution explained 73. 59% of the variance. All the three factors showed acceptable saturations corresponding to all items, with factor loadings above the conventional cut-off value of 0. 40  [85]. In the second subsample (N = 387) a confirmatory factor analysis (CFA -ML maximum likelihood) was performed by MPLUS, version 7. 3, a statistical modeling program edited by Muthén and Muthén  [86]. According to the literature  [87]  several goodness-of-fit criteria were considered: the χ 2 goodness-of-fit statistic; the Root Mean Square Error of Approximation (RMSEA); the Comparative Fit Index (CFI); the Tucker Lewis Index (TLI); the Standardized Root Mean Square Residual (SRMR).