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  • Experiments with faster dissemination of research began in the 1960s, and in the 1990s first preprint servers emerged and became widely used in Physical Sciences and Economics. Since 2010, more than 30 new preprint servers have emerged and the number of deposited preprints has grown exponentially, with numerous journals now supporting posting of preprints and accepting preprints as submissions for journal peer review and publication. Research on preprints is, however, still scarce. The goals of this project are: 1) Study preprint policies, submission requirements and addressing of transparency in reporting and research integrity topics of all know preprint servers that allow deposit of preprints to researchers regardless of their institutional affiliation or funding. 2) Study comments deposited on preprint servers’ platforms and social media and their relation to peer review and information exchange. 3) Study differences between preprint version(s) and version of record. Team Members (by first name alphabetical order): Ana Jerončić,1 Gerben ter Riet,2,3 IJsbrand Jan Aalbersberg,4 John P.A. Ioannidis,5-9 Joseph Costello,10 Juan Pablo Alperin,11,12 Lauren A. Maggio,10 Lex Bouter,13,14 Mario Malički,5 Steve Goodman5-7 1 Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Croatia 2 Urban Vitality Centre of Expertise, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Department of Cardiology, Amsterdam, The Netherlands 4 Elsevier, Amsterdam, The Netherlands 5 Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA 6 Department of Medicine, Stanford University School of Medicine, Stanford, California, USA 7 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA 8 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA 9 Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA 10 Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA 11 Scholarly Communications Lab, Simon Fraser University, Vancouver, British Columbia, Canada 12 School of Publishing, Simon Fraser University, Vancouver, British Columbia, Canada 13 Department of Philosophy, Faculty of Humanities, Vrije Universiteit, Amsterdam, The Netherlands 14 Amsterdam UMC, Vrije Universiteit, Department of Epidemiology and Statistics, Amsterdam, The Netherlands
    Data Types:
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  • We identified the patients with end-stage heart failure and ventricular assist devices (VADs) who underwent bariatric surgery at Ochsner Medical Center, the only center with a VAD program in the State of Louisiana, USA. Every patient underwent a comprehensive preoperative evaluation that included psychological, metabolic, nutritional, and cardiovascular assessments. All patients were over the age of 18 years and underwent laparoscopic sleeve gastrectomy (LSG) between 2016 and January 2020. All patients were on chronic antiplatelet and anticoagulation therapy with warfarin and aspirin. Both medications were held at the time of admission and heparin infusion was started until the midnight prior to LSG. If the INR was > 1.5 the evening prior to the surgery, the patient received fresh frozen plasma and then, the INR was remeasured in the morning prior to surgery, which was performed when the INR was ≤ 1.5. Provided that the patient developed no bleeding complication, heparin was started at 200 U/hr eight hours after LSG, increased to 400 U/hr during the postoperative day one. The following day, heparin was titrated to a goal thromboplastin time of 35-45 sec with addition of aspirin. On postoperative day three, the goal thromboplastin time was increased to 45-54 sec and warfarin was restarted at a dose of 1 mg. This dataset (database: LSG in patients with VADs) includes baseline, periprocedural, and outcome data, which are described in one of the attached documents (description of variables).
    Data Types:
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  • This dataset is raw data and summary of quantified FD&C dye levels in children’s gummy vitamins, children’s tablet vitamins, prenatal vitamins, children’s cough/cold/allergy tablets & syrups, and children’s pain reliever tablets & syrups determined by high performance liquid chromatography with a photometric diode array (HPLC-DAD). The data can further be interpreted against the dosage given by each brand in each category to evaluate the consumption of the FD&C dyes and ADIs suggested for each FD&C dye by the US FDA. Syrups have the highest levels for the dosage described on the label and can be a significant contributor to FD&C dye intake for children, while all tablets and gummy vitamins showed low enough levels to be an insignificant amount of dye contributed to the ADI for most children.
    Data Types:
    • Tabular Data
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  • The inadequacy of breast cancer early detection methods in Sudan, makes the implementations in breast cancer awareness and regular breast self examination worth doing. Among medical students, the level of knowledge, attitude and practice of breast self examination is worth questioning, being the potential professional health care providers on whom the awareness spread duty is laid. the knowledge, attitude and practice level of breast self-examination in female undergraduate students, faculty of medicine university of Khartoum were found to be low.
    Data Types:
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    • Dataset
  • Datasets for policy preference identification, binary sentiment classification, and stance detection of debates from the House of Commons of the United Kingdom Parliament. For details, see: ParlVote: G. Abercrombie and R. Batista-Navarro. ParlVote: A Corpus for Sentiment Analysis of Political Debates. Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC-2020). European Languages Resources Association (ELRA), 2020. ParlVote+: Paper under review. This version includes policy preference labels for each example. It has also been cleaned up a little, and some incorrect examples from the original dataset have been removed. Data published under the Open Parliament Licence v3.0 : https://www.parliament.uk/site-information/copyright-parliament/open-parliament-licence/
    Data Types:
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  • General description: - This dataset includes a Python code for sequence-to-sequence time-series forecasting by training and evaluating recurrent neural network models. - The code was developed to enable rapid and wide-scale development, production and evaluation of time-series models and predictions. - The RNN's architecture has a convolutional layer for handling inputs, within a composite autoencoder’s neural network. Instructions for usage: - The Python code is located in a Jupyter notebook that can be opened online or locally, by using a Jupyter Notebook compatible platform as: https://jupyter.org (accessed 11 July 2020). https://colab.research.google.com (accessed 11 July 2020). - In order to use the code, a data source should exist in a "csv" file extension and it should be named as 'data_input.csv' or alternatively, an online link to the data source could be entered when executing the code. The data source should have first 4 columns for metadata. The unique name or identifier for each row will be located in the 2nd column, otherwise, a change has to be made in the code in the gen_data function (line 282) and line 286 in case of the need to change metadata columns size, into less or more. The rest of the columns indicate the accumulated number or value in each column. Important parameters: - target_pred: specifies which row in the data to predict. - crop_point: specifies which data point to crop the time-series data at, ex. training data = before crop_point, evaluation data = after crop_point. - time_steps: specifies which time-steps to use, ex. 15 or 20, meaning: 15 for X and 15 for Y in the sequence-to-sequence model. - RNN parameters: ex. batch size, epochs, layer sizes, RNN architecture (GRU or LSTM). - ext: specifies the end date of predictions. License: This code is licensed under MIT license.
    Data Types:
    • Software/Code
    • Dataset
  • Field level data coded, cleaned and analyzed with IBM SPSS version 25 software.
    Data Types:
    • Software/Code
    • Dataset
  • The present is a manually labeled data set for the task of Event Detection (ED). The task of ED consists of identifying event triggers, the word that most clearly indicates the occurrence of an event. The present data set consists of 2,200 news extracts from The New York Times (NYT) Annotated Corpus, separated into training (2,000) and testing (200) sets. Each news extract contains the plain text with the labels (event mentions), along with two metadata (publication date and an identifier). Labels description: We consider as event any ongoing real-world event or situation reported in the news articles. It is important to distinguish those events and situations that are in progress (or are reported as fresh events) at the moment the news is delivered from past events that are simply brought back, future events, hypothetical events, or events that will not take place. In our data set we only labeled as event the first type of event. Based on this criterion, some words that are typically considered as events are labeled as non-event triggers if they do not refer to ongoing events at the time the analyzed news is released. Take for instance the following news extract: "devaluation is not a realistic option to the current account deficit since it would only contribute to weakening the credibility of economic policies as it did during the last crisis." The only word that is labeled as event trigger in this example is "deficit" because it is the only ongoing event refereed in the news. Note that the words "devaluation", "weakening" and "crisis" could be labeled as event triggers in other news extracts, where the context of use of these words is different, but not in the given example. Further information: For a more detailed description of the data set and the data collection process please visit: https://cs.uns.edu.ar/~mmaisonnave/resources/ED_data. Data format: The dataset is split in two folders: training and testing. The first folder contains 2,000 XML files. The second folder contains 200 XML files. Each XML file has the following format. YYYYMMDDTHHMMSS ... ... ... The first three tags (pubdate, file-id and sent-idx) contain metadata information. The first one is the publication date of the news article that contained that text extract. The next two tags represent a unique identifier for the text extract. The file-id uniquely identifies a news article, that can hold several text extracts. The second one is the index that identifies that text extract inside the full article. The last tag (sentence) defines the beginning and end of the text extract. Inside that text are the tags. Each of these tags surrounds one word that was manually labeled as an event trigger.
    Data Types:
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    • Document
    • File Set
  • List of references of articles included in this literature search
    Data Types:
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    • Dataset
  • ABSTRACT Background. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care. Methods and findings. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in 43 general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%, p<0.001) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%, p<0.001) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%, p=0.046) at 24 weeks; the change at 12 months (23.5%) was no longer significant (95% CI, -4.5% to 61.6%, p=0.127). After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. Study limitations include the fact that practices were not randomized, and therefore unmeasured confounders may have influenced our findings. Conclusions. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.
    Data Types:
    • Tabular Data
    • Dataset
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