dataesr/scientific-paragraphs-categorization huggingface dataset:
Choosing the Type and Number of Seeds As proposed in section 5, we simulated the competition between two firms, one having higher number of less influential node and one having small number of highly influential node using all the three datasets. As we have seen that none of centrality is best for all datasets but it is data dependent so, we will use the centrality method which performed best for that particular dataset. So for Wikipedia Vote Network it will be Closeness, for Chess Interaction it will be Degree, and for Human Interaction network it will be Pagerank. As the Figure  2  depicts for all three datasets, the number of infected (or influenced) nodes remains same for both the sets up to few timestamps, but after that High-less set takes over then, stabilization is achieved. Overall winner is High-less (less number of highly influential nodes) if we use the better performing centrality measure as per datasets to assign the costs of nodes.
CONCLUSION In this paper, we investigate empirically two linked issue in a competitive contagion setting. First, we simulate a set of competitions between strategies based on ranking according to various centrality measures. The goal is to choose the best centrality method to rank the nodes for initial adoption with a motive to maximize the adoption of the product or ideology. Results show that no centrality is universally best but it depends on the network properties of the network dataset used. The second part deals with solving the general dilemma of whether to choose group of small number of highly influential nodes or a group of large number of less influential nodes. We conclude that it is better to select a small number of highly influential nodes than a higher number of less influential nodes.
D. Interventions targeting authors, peer reviewers, and editors: • Training for authors, peer reviewers, and editors on the importance, content, and use of RGs (e. g. The EQUATOR Network toolkits) (Intervention 9) The survey combined open and closed response questions to seek participants' perceptions of a series of interventions to improve authors' adherence to RGs that could potentially be implemented during the editorial process. We pilot tested the draft survey questionnaire with two collaborators of the MiRoR project who currently hold editorial positions. They were asked to review the survey for its clarity and completeness and to provide suggestions on how to improve its structure. Based on feedback from the pilot we decided not to include the intervention "Implementation of the automatic tool Statreviewer 16 " since participants were not aware of this software and stated that their perceptions would strongly depend on details about how it operates which are not publicly available.
Data analysis For quantitative data (Part 2 of the questionnaire), we used R version 3. 6. 0 18. As these data were ordinal, we calculated medians together and the 1st and 3rd quartiles. We excluded from the analysis one questionnaire where the participant just opened the survey and left without answering any question. We did not exclude any questionnaire based on the amount of time that the participant needed to complete it. For qualitative information, the lead investigator (DB) used the software program NVivo 12  19. We mapped the barriers and facilitators for each of the interventions explored, as well as other key themes such as the incentives for the use of RG and the implementation of further editorial strategies. The initial mapping made by the lead investigator was discussed with another investigator (SS) and subsequently refined.
Ethics approval & informed consent The Research Committee of the Governing Council of the Universitat Politècnica de Catalunya (UPC) granted ethical approval for this study (Reference EC 01, Date 2 May 2018). In the invitation email, we informed survey participants that (i) the completion of the survey indicated consent to participate, (ii) they were free to stop and withdraw from the study at any time without providing a reason, (iii) the estimated time to complete the survey was 15 minutes, (iv) any identifiable information obtained in connection with this survey would remain confidential, and (v) the results would be submitted for publication and the anonymised dataset would be made publicly available in the Zenodo repository. The original dataset was kept in a password-protected folder in Google Drive.
Please, re-evaluate. As I said above, the abstract is good, and there is room for growth (you have now 232 words). 8) However, I could not find the conclusions in the abstract helpful. Could you please inform better what you did conclude? What "points raised in the survey" should be considered?. "assessing completeness of reporting is a subjective task". This is one of the 9) Checklists as forms. most important sentences in this manuscript, it tells us about using reporting guidelines checklists as evaluation tools -for what they have not been created. I saw no discussion about this in your paper. The discussion of the paper is short, and it lacks several points pointed above. Mainly, it 10) Discussion. does not discuss the limitations and potential bias of this study. Discussing limitations is a requirement from F1000Research journal.
Another suggestion for We have re-written and expanded the Conclusions in the abstract. The abstract is now 295 words. 9) Checklists as forms. "assessing completeness of reporting is a subjective task". This is one of the most important sentences in this manuscript, it tells us about using reporting guidelines checklists as evaluation tools -for what they have not been created. I saw no discussion about this in your paper. We have now discussed this point in the paragraph before the limitations of the study. 10) Discussion. The discussion of the paper is short, and it lacks several points pointed above. Mainly, it does not discuss the limitations and potential bias of this study. Discussing limitations is a requirement from F1000Research journal. We have now included such a section in the Discussion section.
No competing interests were disclosed. Competing Interests: Reviewer Expertise: statistics, modelling, validation, decision making I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. https://doi. org/10. 5256/f1000research. 22597. r56388 © 2019 Logullo P. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited. Patricia Logullo EQUATOR Network, University of Oxford, Oxford, UK Positive points: This is a much-needed survey, and I am happy and thankful that the MiRoR team invested their time and expertise in undertaking it.
Competing Interests: 20 November 2019 Reviewer Report https://doi. org/10. 5256/f1000research. 22597. r55889 © 2019 Urrutia G. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited. Gerard Urrutia Iberoamerican Cochrane Center, Sant Pau Biomedical Research Institute, Barcelona, Spain This is an interesting and original study that deepens the knowledge about interventions aimed at improving adherence to reporting guidelines by medical journals, an important issue. The study focuses on the perspective of the biomedical editors and provides valuable information on their perceptions of various interventions that have been proposed, identifies barriers and facilitators and provides possible solutions.
/doi. org/10. 5256/f1000research. 22597. r55890 © 2019 Lendrem D. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited. Dennis W. Lendrem Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK This paper reports on the views of a sample of biomedical editors on editorial interventions to improve adherence to reporting guidelines. The paper is clear, transparent, and well documented.
https://doi. org/ 10. 5281/zenodo. 3404002 17. This project contains the following extended data: Figure S1: Survey questionnaire (Complete version of the survey questionnaire used in this project) PubMed Abstract | Publisher Full Text | Free Full Text 12. von Elm E, Altman DG, Egger M, et al. : The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Bull World Health Organ. 2007; 61(11): 867-72. 13. Chan AW, Tetzlaff JM, Altman DG, et al. : SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med. 2013; 158(3): 200-7. PubMed Abstract | Publisher Full Text | Free Full Text 14. Schroter S: Evidence-based publishing. BMJ. [cited 2019 Nov 28]. Reference Source 15. SurveyMonkey Inc: SurveyMonkey.
data of the project "A survey exploring biomedical editors perceptions of editorial interventions to improve adherence to reporting guidelines ". Zenodo. [cited 2019 Nov 29]. http://www. doi. org/10. 5281/zenodo. 3404002 18. R Core Team: R: A language and environment for statistical computing. 2019. Reference Source 19. QSR International Pty Ltd: NVivo.
Computational performance under complete rejection policy The experiments are coded in Matlab 2015a and run on a machine with an Intel Core i7 processor of 2. 6 GHz and 16 GB of RAM. Table  2  reports the CPU time needed to compute the steady-state probabilities as a function of λ in seconds, the demand size distribution and the inventory level S. We also report the optimal value of the inventory level denoted by S * for h = 1 and b = 20. The value of S * is found by means of a grid search starting from S = 0. In Table  2 , we show that, under the logarithmic demand size distribution and when λ = 0. 5, our algorithm requires, in most of the cases, less than one second to compute the steady-state probabilities for a given S, whereas  Feeney and Sherbrooke (1966) 's method needs more than 20 seconds.
Numerical Results for Inverse Problem. To make the test we used the data from ECGSIM. ECGSIM is an interactive simulation program that enables to study the relationship between the electric activity of the heart and the resulting potentials on the thorax (see for e. g. [20, 21] ). We would like to mention here that ECGSIM does not solve the inverse problem. From the ECGSIM we take the surfacic geometry, the measured ECG and the the simulated ECG with the initial parameter values. Having the surfacic geometry (from ECGSIM), we generate the volumetric mesh by using a three-dimensional finite element mesh generator Gmesh. With the volumetric mesh we generate the Transfer Matrix for the both cases, homogeneous (heart and thorax only) and non homogeneous (heart, thorax and lungs). In our simulation we use a measured ECG of 64 nodes on the body surface of a normal male (25 years 100 kg) and then interpolated to the rest of the thorax for 1000 samples nodes.
(2008) ,  Janssen and Manca (2006) ,  Votsi et al. (2012 ), D'Amico et al. (2013) ,  Votsi et al. (2014 ), D'Amico et al. (2016b) ,  Barbu et al. (2016 ), D'Amico et al. (2016a)  for semi-Markov processes in continuous or discrete time with various applications and  Sansom and Thomson (2001) ,  Bulla and Bulla (2006) ,  Barbu and Limnios (2008) , for hidden semi-Markov models and applications in climatology, finance and DNA analysis. Note that the semi-Markov theory is developed mainly in a continuous-time setting, while utterly less works address the discrete-time case. We refer the reader to  Limnios and Oprisan (2001)  for continuous-time framework and to  Barbu and Limnios (2008)  and references therein for discrete-time framework. The R package SMM that we present in this paper is developed in discrete time.
For instance, we can mention the simplicity of computations in discrete time, the fact that a discrete-time stochastic process does not explode, the potential use of discrete processes after the discretization of continuous ones, etc. for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are made on the shape of distributions. Moreover, the estimation can be done on the basis of one or several trajectories, with or without censoring. The aim of this paper is to describe the different possibilities of this package. To summarize, the package SMM that we present deals with different problems: • Parametric estimation for sojourn time distributions  (Uniform, Geometric, Poisson, Discrete  Weibull and Negative Binomial) or non-parametric estimation; • One or several sample paths; • Four different types of sojourn times: a general one depending on the current state and on the next state to be visited, one depending only on the next state, one depending only on the current state, and one depending neither on the current state nor on the next state; • Four different types of censoring: censoring at the beginning of sample paths, censoring at the end of sample paths, censoring at the beginning and at the end of sample paths or no censoring at all.
The SMM package The SMM R package is mainly devoted to the simulation and estimation of discrete-time semi-Markov models in different cases by the two following functions: simulSM() for the simulation of sequences from a semi-Markov model: -One or several trajectories -Discrete Weibull of type I with the density f (x) = q (x-1) βq x β for x = 1, 2,. , n with n > 0, q is the first parameter, 0 < q < 1, and β is the second parameter, β > 0; -Negative Binomial with the density f (x) = Γ(x + α) Γ(α)x! α α + µ α µ α + µ x for x = 0, 1,. , n with n > 0, Γ is the Gamma function, α is the parameter of overdispersion, α > 0, and µ is the mean, µ ∈ R. In order to avoid any confusion, note also that the expressions of the different densities considered in this package are also provided in the corresponding manual.
seq. markov[[1]][1:25] [1] "g" "g" "g" "g" "c" "c" "c" "a" "a" "a" "c" "c" "c" "g" "g" "c" "c" "c" "c" "c" "g" "g" "g" "a" "a" This package provides also non-parametric estimation and simulation and takes into account censored data of several types. To summarize, the importance and interest of the R package SMM that we have developed come from: -considering versatile tools, namely discrete-time multi-state semi-Markov processes, that are of use in a variety of applied fields, like survival analysis, biology, reliability, DNA analysis, insurance and finance, earthquake modeling, meteorology studies, etc. An example of application can be the description of DNA sequences by using a 2-state process to distinguish between coding and noncoding regions in DNA sequences. It is well known that the length of such regions generally does not follow a Geometric distribution.
2 To solve each instance, we first apply the preprocessing procedure of Section II-B to obtain a reduced list coloring graph which is then colored with the MMCOL algorithm. The preprocessing step takes typically from several seconds to dozens of seconds. In addition to these 1800 random instances, we also assess our approach on the set of 19 traditional benchmark instances from the COLOR03 competition 3  that were tested, for instance,  [17]  and  [21]. MMCOL was coded in C++  4  and compiled using g++ with the "-O3" option on a computer running Linux equipped with 2. 83 GHz and 8-GB RAM. When running the DIMACS machine benchmark procedure "dfmax. c" 5 on our machine, we obtain the following results: 0. 20, 1. 23, and 4. 68 s for graphs r300. 5, r400. 5, and r500. 5, respectively. The computational results reported in this section were obtained with the parameter setting shown in Table  I.
These instances are known to be particularly difficult  [25]  and only the two smallest ones of these five instances (qwhdec. order33. holes381. bal. 1 and qwhdec. order50. holes825. bal. 1) can be solved by very few approaches presented in  [17]  including CSP, hybrid strategy mixing LP/CSP, and SAT-based method. The difficulty of these instances are further confirmed by the most recent study reported in  [21] , where even the best performing heuristic Tr-ILS*  [21]  cannot solve any of these balanced instances. We also ran the source code of Tr-ILS* on our computer for a long computation time of 3600 s and still failed to solve any of these five balanced instances. It is thus remarkable that our MMCOL approach solves these instances consistently, even though MMCOL has a low success rate for two instances.
6, 0. 7}) which are known to be more difficult  [21]. On the other hand, MMCOL has no particular difficulty to solve these hard instances. In order to verify if the ILS algorithms can solve more instances by using more computation time, we ran the source code of the best performing algorithm Tr-ILS* under a much relaxed time limit of 3600 s on the instances of types (n ∈{ 50, 60, 70}, r ∈{ 0. 6, 0. 7}). One observes that the 100 instances of LSC-50-60 (n = 50, r = 0. 6) can be fully completed, 98 and 95 instances of LSC-60-60 (n = 60, r = 0. 6) and LSC-70-60 (n = 70, r = 0. 6) can be fully completed, respectively. Nevertheless, no instance of the types (n ∈{50, 60, 70}, r = 0. 7) can be fully completed even if these instances have more filled grids for the LSC. In summary, MMCOL competes very favorably with seven most recent methods in the literature and proves to be highly effective in solving the set of 1800 benchmark instances with no exception.
However, the underlying relational database structure of RAdb has its limitations when dealing with the integration of diverse data and knowledge sources. This limitation is exacerbated when these resources do not share a common vocabulary, as it is the case in our ecotoxicology risk assessment setting. In this paper we present a preliminary study of the benefits of using Semantic Web tools to integrate different data sources and knowledge graph embedding approaches to improve the ecotoxicological effect prediction. Hence, our contribution to the NIVA institute is twofold: (i) We have created a knowledge graph by gathering and integrating the relevant biological effect data and knowledge. Note that the format of the source data varies from tabular data, to SPARQL endpoints and ontologies. In order to discover equivalent entities we exploit internal resources, external resources (e.
The ECOTOX database We rely on the ECOTOXicology database (ECOTOX)  [9]. ECOTOX consists of ∼ 930k tests (or experiments) derived from the literature. Currently, an ECO-TOX test considers the effect of one of ∼ 12k chemicals on one of ∼ 13k species. Which implies that less than 1% of compound-species pairs have been tested. The effect is categorised in one of a plethora of predefined outcomes. For example, the LC50 outcome implies lethal concentration for 50% of the test population. Table  1  shows the most frequent outcomes in ECOTOX. Table  2  contains an excerpt of the ECOTOX database. ECOTOX includes information about the compounds and species used in the tests. This information, however, is limited and additional (external) resources are required to complement ECOTOX. The number of outcomes per compound and species varies substantially.
For example, there are 1,881 experiments where the compound used is sulfuric acid, and 9,436 experiments where Pimephales promelas (fathead minnow) is the test species. The median number of experiments per chemical and species are 3 and 6, respectively. Figure  3  visualises a subset of the outcomes, here the zero values are either no effect or missing. This figure shows certain features of the data, e. g. , that compounds are more diversely used than species and that compound similarity is closely correlated to effects with regards to a species. Currently, the ECOTOX database in used in risk assessment as reference data when calculating risk for a ecosystem. Essentially, comparing the reference and the observed chemical concentrations (per species). Since most compounds Fig. 3 : ECOTOX effects data.
Dataset integration into the TERA knowledge graph Figure  2  shows the different datasets and their transformation that contribute in the creation of the TERA knowledge graph. For example Triples (vii)-(ix) in Table  3  have been created from the ECOTOX effect data. Each compound in the ECOTOX effect data has a identifier called CAS Registry Number assigned by the Chemical Abstracts Service. The CAS numbers are proprietary, however, Wikidata  [21]  (indirectly) encodes mappings between CAS numbers and open identifiers like InChIKey, a 27 character hash of the International Chemical Identifier (InChI) that encodes the chemical information in a unique manner. Hence, other datasets, such as PubChem  [20] , can be used to gather chemical features and classification of compounds. PubChem is already available as a knowledge graph and can be imported directly.
However, the PubChem hierarchy only contains permutations of compounds. To create a full taxonomy for the chemical data, we use the ChEMBL SPARQL endpoint to  Aligning ECOTOX and NCBI. The species lineage in ECOTOX is not complete and therefore this (missing) information has been complemented with the NCBI taxonomy  [16] , a curated classification of all of the organisms in the public sequence databases (around 10% of the species on Earth). The tabular data provided for the ECOTOX species and the NCBI taxonomies has been transformed into subsumptions and disjointness triples (see first four triples in Table  3 ). Leaf nodes are treated as instance entities. Since there does not exist a complete and public alignment between ECO-TOX species and the NCBI Taxonomy, we have used the LogMap  [11, 12]  ontology alignment systems to index and align the ECOTOX and NCBI vocabularies.
In the near future, we intend to improve the current ecotoxicological effect prediction prototype and evaluate the suitability of more sophisticated models like Graph Convolutional Networks. The TERA knowledge graph will also be extended with additional information about species (e. g. , in-teractions) and compounds (e. g. , target proteins) which is expected to enhance the computed embeddings and the effect predictions. Resources. The datasets, evaluation results, documentation and source codes are available from the following GitHub repository: https://github. com/Erik-BM/NIVAUC.
Acknowledgements This work is supported by the grant 272414 from the  Research Council of Norway (RCN) , the  MixRisk  project ( RCN 268294 ), the  AIDA  project, The  Alan Turing Institute  under the  EPSRC grant   EP/N510129/1 , the  SIRIUS Centre for Scalable Data Access  ( RCN 237889 ), the  Royal Society ,  EPSRC projects DBOnto , MaSI 3 and ED 3. We would also like to thank  Martin Giese  and  Zofia C. Rudjord  for their contribution in early stages of this project.
Entity extraction The number of works addressing automatic extraction of significance levels is limited. (Hsu et al. , 2012)  used regular expressions to extract statistical interpretation, p-values, confidence intervals, and comparison groups from sentences categorized as "outcomes and estimation". The authors report precision of 93%, recall of 88% and F-measure of 90% for this type of information. (Chavalarias et al. , 2016 ) applied text mining to evaluate the p-values reported in the abstracts and full texts of biomedical articles published in 1990 -2015. The authors also assessed how frequently statistical information is presented in ways other than p-values. P-values were extracted using a regular expression; the system was evaluated on a manually annotated dataset. The reported sensitivity (true positive rate) is 96.