dataesr/scientific-paragraphs-categorization huggingface dataset:
The use of multiple endpoints for the same technology (e. g. Docker) act as a fail safe, so that when one registry is down, the other registry can serve the containers. This is the case for both Singularity and Docker containers. However, this prompted the need for a centralized metadata registry that enables to search, find and retrieve bioinformatics tools and the corresponding containers. All the metadata for the tools is stored in a centralized MongoDB database. A set of Python pipelines is used to retrieve the metadata from the Dockerfile and Conda recipes and store them in the database. Also, metadata about the publications and other external identifiers related to the tool are pushed to the database from EuropePMC or biotools. The pipelines, MongoDB database are deployed in a Kubernetes cluster in the ELIXIR compute platform.
Tools and containers metadata A crucial part of the FAIR principles is the correct annotation of the bioinformatics tools and containers. For each bioinformatics tool, the central registry stores standardized metadata agreed between the Bioconda and BioContainers communities  [2]. The ELIXIR tool platform has defined that for every bioinformatics tool the following metadata should be capture (Figure  2 )  [4] : The tool metadata includes: • General metadata: Name, description, license • Keywords: transcriptomics, proteomics • Main project URL 3/9 • External identifiers such as PubMed accession or DOI (Digital Object Identifier) • Maintainer • List of container images The container metadata includes: • Container type (Docker, Conda, Singularity) • Number of pulls (number of times a container has been downloaded/pulled from the original registry) • Full tag • Container size • Date of Last update The relation between a bioinformatics tool and a container is defined through a set of versions (Figure  2 ).
Restful API The BioContainers Restful API is an implementation of the TRS standard from the GA4GH consortium  [6]. The TRS specification defines an standard API to exchange bioinformatics tools, workflows and containers enabling distribute data processing of omics datasets. The API enables two main functionalities: • Search for bioinformatics tools and the corresponding containers (/ga4gh/trs/v2/tools): Tools can be searched using the metadata of both the tools and the containers. The API enables to search using specific fields such as id, name, organization, or by using all the metadata fields (e. g. proteomics tools). Also, search results can be refined or filter using different facets (properties) such as license or tool tags (keywords to classify the tools). The following example proteomics and license Apache-2, provides all the tools with proteomics keyword in the metadata and license Apache-2.
5/9 The search results can be sorted by the id, name, organization, description of the tool, the number of downloads, and/or the usage of each tool (Figure  3 ). The number of downloads and pulls represents the sum of all containers' downloads and pulls. Sorting the results by the number of downloads enables users to know which tool has been used the most by the community to perform a specific task or which tools are widely used in a particular field (e. g. proteomics, transcriptomics). 6 Restful API command line interface restful API and web registry interface allows users to easily search and retrieve bioinformatics tools and containers. However, containerized applications are executed in workflows or command-line data analysis pipelines requiring a compatible interface (command line or Python package) to inquire the BioContainers Registry.
A python package named bioconda2biocontainer was developed to inquire the registry from the command line interface or through python code as a module. This python package can be used to search Conda packages and versions retrieving the corresponding BioContainers image name. It allows a direct synchronization between Conda environments and containerized analysis pipelines. The tool can be installed using Conda, Docker or pip: The biocontainers-search inquire the BioContainers Registry and return a TAB separated table with Name, Versions (comma separated), Description, License and number of pulls.
$ biocontainers -search --search_term proteomics The -json parameter can be use output the results in json format instead of a tab-delimited output. The name of the tool can be then provided to the 6/9 The output of the command is a list of containers for the version of the tool 2. 27. 0. The images can be sorted by date, size or number of downloads. In addition, the container type parameter can be use to retrieve only specific technology containers: Docker, Singularity or Conda. bioconda2biocontainer package can be used to pull the latest BioContainers Docker image for a Conda package and version as it is shown next:.
Conclusion BioContainers is a growing community with multiple on-going projects, including the maintenance and creation of new software containers for bioinformatics tools, the implementation of improvements in the findability of the tools, and facilitating their re-usability. Recently, a new architecture has been developed to guarantee multiple registries for each container which enables high availability for all the stored BioContainers. Here, we have introduced a new Restful API and web application that can be used to search for bioinformatics tools and its corresponding packages and containers. Researchers can find which are the most downloaded and used tools for a particular task, in addition to having access to all versions of a specific tool. The Restful API is an implementation of the GA4HG standard which enables compatibility with other resources and initiatives like Dockstore.
Participants In the early phase of the COVID-19 pandemic (week 17-19 2020), an online questionnaire was distributed among university students in 10 countries. The current study uses data on students in 7 of these countries 2 : Belgium, France, Ireland, Italy, the Netherlands, Sweden and Portugal. Our sample consisted of 6759 graduate and undergraduate students. The survey was approved by the Internal Review Board of Erasmus University Rotterdam in advance and was shared with the target group for 13 consecutive days using the online survey software Qualtrics. Students could choose to complete the survey in English, Dutch or French, and translations were made by two native speakers per language. All students signed an informed consent form at the start of the survey. On average, respondents were 22.
Results We present descriptive statistics, Cronbach's alpha values and correlations in Table  1. Information on the country samples is presented in Appendix B (Table  B. 1). In general, student compliance with COVID-19 measures in our sample was high, especially for social distancing behaviors. Self-reported impulsivity as measured by the BIS-Brief correlated negatively with both social distancing and hygiene compliance, whereas the discount rate correlated positively with social distancing and hygiene compliance. Impulsivity and the discount rate were not Note: correlations in excess of |0. 02| are statistically significant at the 5% level. 2 We do not use the data from students in Spain, Colombia, or India due to (i) a translation mistake in the Spanish version (Spain and Colombia) of the delay discounting task and (ii) the large difference in discount rates between Indian and European students (likely due to differences in currency and the perceived value of money).
3 Regressions on 50 imputed datasets based on all the main variables in the final model were conducted. The average values of the pooled estimates and regression coefficients were almost identical to the results from the nonimputed dataset. statistically related, in line with prior studies  (Reynolds et al. , 2006; McLeish & Oxoby, 2007). To test our hypotheses, we conducted regression analyses with social distancing compliance (Table  2 ) and hygiene compliance as the dependent variables (Table  3 ). All models controlled for country differences using dummy variables (omitted from the regression tables). We first estimated the models without control variables (model 1), then included age and gender (model 2), and finally included social norms (model 3). We based our conclusions on the final model (model 3, Tables 2 and 3).
Robustness and sensitivity checks To further investigate the results, robustness and sensitivity checks were conducted which are discussed and presented in the Appendix. We conducted subsample analyses by country (Appendix C), gender (Appendix D), nationality (international versus domestic students) (Appendix E), and age groups (Appendix F). Moreover, we tested whether the relationships hold when using follow-up data (Appendix G), when transforming the skewed dependent variables (Appendix H) and when using differentbut relateddependent variables (Appendix I). Overall, the results show that the impulsivity compliance is robust across analyses. Moreover, we generally confirm the positive relationship between discount rate and compliance with COVID-19 measures in most analyses, although in some subgroup analyses (with smaller N) the result is not present or statistically significant at conventional p-value levels.
Limitations and future research While this study is, to the best of our knowledge, the first to study the role of impulsivity and delay discounting in compliance with COVID-19 measures in a large sample of students, it has limitations. The data were collected using an online survey with self-reported measures, which elicits social desirability bias among respondents. While anonymity was emphasized and the data were collected in an online environment, students could have overreported their compliance with public health measures. Finally, the task that we used to assess the discount rate differs from the decision to comply with COVID-19 measures in three ways. First, we used a money-related instead of a health-related discounting task  (Bleichrodt et al. , 2016). This may be problematic as discount rates for money and health have not always been found to be universal  (Attema, 2012).
Declaration of competing interest None. 'How did/does the current coronavirus crisis affect your financial security?' (Likert scale, 1-5), which was negatively correlated to the discount rate (-0. 12). However, all results remained almost identical when controlling for changes in financial security. 5 This explanation is not in line with the positive link found between the discount rate and compliance measured during a later phase of the pandemic (December 2020) in our follow-up dataset (Appendix G). At that moment, the pandemic had continued over a prolonged period, hence students may have become aware that the benefits of compliance would not be reached before long.
Acknowledgements We thank  Jose Maria Millán ,  Jorge Barrientos Márin ,  Jinia Mukerjee  and  Joern Block  for their involvement in the data collection. Moreover, we thank  Kirsten Rohde  for her expert advice on delay discounting. Roy Thurik  and  Olivier Torrès  are members of  LabEx Entreprendre of the Université de Montpellier  ( Montpellier Management , MOMA) funded by the  French government (LabEx Entreprendre ,  ANR-10-Labex-11-01 ) as well as of the public research centre Montpellier Research in Management ( EA 4557 ,  Université de Montpellier). Srebrenka Letina  is part of the relationship programme supported by The  Medical Research Council and Scotland's Chief Scientist Office  ( MC_UU_00022/3 ) and with  CSO funding of the Relationships programma  ( SPHSU18 ).
Transparency would be paramount, with sharing of protocols, followed by aggregated data and individual patient data. Transparency would be guaranteed, regardless of whether the drug is approved or not. Clinical trial registries, such as ClinicalTrials. gov, could evolve to support the uploading of all these documents. Prospective registration on these public registries is the norm for clinical trials. Moreover, they have evolved to also include trial results, and, thus, are in a privileged position to expand toward incorporating more comprehensive open science tools empowering Findability, Accessibility, Interoperability, and Reusability (FAIR) access to any study related data. Undoubtedly, such a radical push toward transparency in planning, conducting, and reporting research, if promoted by an influential national or transnational regulatory authority, would have profound consequences for the entire field of biomedicine.
Data The datasets used in this study are from the Human Mortality Database  [54] , consisting of observed death counts, D x,t , and exposure-to-risk, E x,t , classified by age at death (x = 0,. , 110+), year of death (t = 1960,. , 2019) and sex, and normal retirement age data collected from National Statistical Offices (NSOs) and social security bureaus. For Spain, we use total population data as a proxy of pensioner data, since the latter are not publicly available. The model fitting, forecasting, and simulation procedures, and additional computations have been implemented using an R software routine.
This means combining models is a way to leverage their strengths and minimize their downsides. The results obtained in this dataset show that no individual stochastic model dominates based on the predictive accuracy criteria, with the CP-Splines and the weighted Hyndman-Ullah models providing consistently good results across populations and the Plat model generating the worst forecasting performance. We note that the forecasting results do not incorporate the effects of the pandemic on mortality rates. Recent projections by national statistical offices (e. g. , the Spanish NSO) indicate that the long-term effect of the pandemic on life expectancy prospects is relatively small once mortality rates return to pre-pandemic values. Figure  3  plots for Finland, first, the BMA forecast of the unisex period and cohort life expectancy measures at the sustainability factor reference indexation age (62 years old) from 2020 to 2050, together with the corresponding 95% MATA confidence intervals (left panel).
This research was funded by  Portuguese national science  funds through  FCT  under the grant  UIDB/04152/2020-Centro   de Investigação em Gestão de Informação (MagIC)  (J. M. Bravo), by the  Spanish Ministry of Science and Innovation  for funding received under grant  PID2019-105986GB-C21  ( M. Ayuso ) and by  Secretaria d'Universitats i Recerca del departament d'Empresa i Coneixement de la Generalitat de Catalunya  for funding received under grant  2020-PANDE-00074. Institutional Review Board Statement: Not applicable. Not applicable.
fMRI setup MRI data were collected with a Siemens Trio 3T system using a 32-channel head coil. fMRI was recorded in four blocks. Each volume consisted of 40 slices (2 Â 2 Â 2 mm in-plane resolution and 1 mm gap, repetition time = 2. 47 s, echo time 26 ms). We tilted volumes by 30°from the anterior and posterior commissures connection line to avoid distortions in the frontal cortex  (Deichmann et al. , 2003). Participants viewed the screen via a head-coil mounted mirror. High-resolution T1 and MT weighted structural images were acquired after functional scanning was completed. fMRI preprocessing MRI data preprocessing and analysis was done using SPM12 (Wellcome Department of Cognitive Neurology, London, United Kingdom). First, volumes were realigned and unwarped to account for head movement and distortion during scanning.
Higher accuracies could be achieved by larger training datasets (to reduce the noise induced by individual outliers). Also, more detailed network models might better reflect the underlying neural computations and thus yield higher predictive accuracy. A number of limitations of the present study need to be acknowledged. First, it remains unclear whether reduced directed exploration constitutes a vulnerability factor or a consequence of continuous gambling. It would therefore be interesting to see whether these effects are tied to the clinical development of patients (e. g. , to the escalation of gambling behavior or treatment effects) or whether they manifest as stable factors that increase the risk for the development of the disorder. Second, a comparison of the present results to patients with substance-use disorders would be of considerable interest  (Morris et al.
FUNDING This works has been supported by the CONNECTION Cluster (https://www. edf. fr/groupe-edf/premier-electricienmondial/activites/recherche-et-developpement/nos-projetsphares/cluster-connexion-le-controle-commande-du-futur),financed through the  Investissements d'Avenir/Briques Génériques du Logiciel Embarqué.
Introduction Causal inference purely from non-temporal observational data is challenging. Instead of learning causal structure of an entire dataset, some researchers focus on analysis of causal relations between two variables only. The state-of-the-art conditional independence-based causal discovery methods (see, e. g. ,  [16, 24] ) construct graphs that are Markov equivalent, but these methods are not applicable in the case of two variables, since X → Y and Y → X are Markov equivalent. The statistical and probabilistic causal inference methods based on assumptions of independence of cause and mechanism (see  [17]  for general overview) appeared relatively recently, and achieve very reasonable empirical results. The main idea behind these methods is as follows: if a simple function that fits data exists, then it is likely that it also describes a causal relation in the data.
test(test='cor'). Note that the above mentioned conditional independence tests from the bnlearn R package return "big" p-values if variables are conditionally independent, and the p-values are small (with an arbitrary threshold 0. 05) for dependent variables. We consider and simulate discrete and continuous data for two following scenarios: 1) X → Y , and X ← U → Y. We test a various number of observations, from 10 to 10,000, and we observe that in the discrete case, even for such a simple problem as one with variables taking binary values, a big number of observations is needed to obtain a reasonable performance. Figure  2  illustrates the p-values of the conditional independence tests for the discrete (two left plots) and continuous (two right plots) settings. We show the results for both cases X ⊥ ⊥ I Y |Y and Y ⊥ ⊥ I X |X.
Objectives Randomised controlled trials conducted using cohorts and routinely collected data, including registries, electronic health records and administrative databases, are increasingly used in healthcare intervention research. A Consolidated Standards of Reporting Trials (CONSORT) statement extension for trials conducted using cohorts and routinely collected data (CONSORT-ROUTINE) has been developed with the goal of improving reporting quality. This article describes the processes and methods used to develop the extension and decisions made to arrive at the final checklist. Methods The development process involved five stages: (1) identification of the need for a reporting guideline and project launch; (2) conduct of a scoping review to identify possible modifications to CONSORT 2010 checklist items and possible new extension items; (3) a three-round modified Delphi study involving key stakeholders to gather feedback on the checklist; (4) a consensus meeting to finalise items to be included in the extension, followed by stakeholder piloting of the checklist; and (5) publication, dissemination and implementation of the final checklist.
BACKGROUND 2] [3] [4]  The CONSORT statement helps to facilitate critical appraisal and interpretation of RCTs by providing guidance to authors on a minimal set of items that should be reported for all trials. 5 The CONSORT 2010 guideline aimed to improve the reporting of two-arm parallel group RCTs. Trials may use a cohort or routinely collected data for: (1) identification of eligible participants, (2) outcome ascertainment and (3) to implement an intervention, or for a combination of these purposes. For example, in registry-based RCTs, a registry could be used to identify eligible participants for a trial, for the collection of participant baseline characteristics and as the source of outcome data; some registries have used interactive technology to actively flag participants for RCT enrolment as patient data are entered into the registry.
12 In some EHR trials, the EHR itself is used to implement an intervention. For example, one RCT tested an intervention to reduce antibiotic prescribing by feeding back personalised antibiotic prescription data to primary care physicians. 17 he use of cohorts and routinely collected data may make RCTs easier and more feasible to perform by reducing cost, time and other resources. 18 19 It may also facilitate the conduct of trials that more closely replicate real-world clinical practice. These trial designs, however, are relatively recent innovations, and published RCT reports may not describe important aspects of their methodology in a standardised way. Trials conducted using cohorts and routinely collected data share certain elements with conventional RCTs, but there are also distinctive elements to report that are not covered in the CONSORT 2010 statement.
Open access Project phase 1: project launch, establishment of team and funding Need for the guideline and literature review An initial informal review of reports of published protocols and reports of trials using cohorts and routinely collected data by BDT and LK suggested that there appeared to be deficiencies in reporting of such trials. For instance, many reports did not adequately describe the cohort or database from which trial participants were recruited, processes used to link participants across databases were not always provided and it was sometimes unclear whether trial outcomes were assessed by the triallists or ascertained via existing databases used to conduct the trial. A review of the EQUATOR website and published literature indicated that there was no existing reporting guideline for these types of trials.
The RECORD statement addresses reporting issues related to routinely collected data but does not include guidance on reporting of trials. Many trials conducted using routinely collected data are pragmatic or use cluster designs, for instance, but CONSORT extensions for those types of trials do not address issues germane to the use of cohorts or routinely collected data to conduct trials. 7 9 oject launch and identification of CONSORT-ROUTINE project members Initial discussions on developing a CONSORT extension for RCTs conducted using cohorts occurred in November 2016 at the Trials within Cohorts symposium in London, UK (LK, MZ, CR and BDT). 26 Discussions continued virtually and key people involved in cohort-embedded trials or the EQUATOR network were approached during December 2016 (HMV, DM, IB, PR, JN, RU and DT).
It was suggested that trials conducted in registries had many characteristics similar to those in cohorts, and there was agreement to include registry-based trials in the extension. People with expertise in registry-based trials were approached in March 2017 (OF, LT, MKC and DE), and an experienced librarian (MSam) and patient representative familiar with trials conducted using cohorts (MSau) were also included in the group at that point. The project was registered on the EQUATOR website in April 2017. During the preparatory phase, while developing searches and reviewing example publications, we became aware that trials conducted using EHRs and administrative databases also shared similar characteristics with trials in cohorts and registries, and it was decided to expand the scope to trials conducted using cohorts and routinely collected data.