dataesr/scientific-paragraphs-categorization huggingface dataset:
Index ML tool name Step in the systematic review process the tool is used Computational methods involved Last update Number of citations Hyperlink to access to the tool Machine learning algorithms 1 A Keyword-Based Literature Data extraction NLP 2019 1 http:// www. mdpi. com/ 2073- Review Data Generating 8994/ 12/6/ 903/ s1 Algorithm (*) 2 Active_learning_document_ Screening NLP, SL 2020 1 https:// github. com/ afcar vallo/ screening (*) active_ learn ing_ docum ent_ scree ning 3 Active-learning-for-system- Screening NLP, SL 2019 1 https:// github. com/ sxzha atic-review (*) ng1201/ active-learn ing-for- syste matic-review 4 DAE-FF (*) Screening SL 2019 1 https:// github. com/ gkont onats ios/ DAE-FF 5 InclusionCriteria (*) Screening NLP 2019 1 https:// github. com/ infoq ualit ylab/ Inclu sionC riter ia 6 Machine Learning Func- Screening SL 2020 1 https:// syste matic revie wsjou tions (*) rnal.
biome dcent ral. com/ artic les/ 10. 1186/ s13643-020-01520- 5# Sec14 7 PubmedClassifier (*) Screening NLP, SL 2018 1 https:// github. com/ Yujia Bao/ Pubme dClas sifier 8 RAPTOR (*) Data extraction NLP 2021 1 https:// github. com/ Cochr aneSc hizop hrenia/ RAPTOR 9 Rules_cochranereviews (*) Screening, Data extraction NLP 2020 1 https:// github. com/ dsuri an/ rules_ cochr anere views 10 SLR_SearchStrings (*) Search, Screening NLP 2018 1 http:// bit. ly/ 2PwL3 7v 11 TextRank (*) Search NLP, USL 2020 2 https:// cran. r-proje ct. org/ web/ packa ges/ textr ank/ index. html Desktop-applications 12 ASReview Screening NLP, SL 2021 2 https:// asrev iew. nl 13 ASReview-covid Screening NLP, SL 2021 1 https:// github. com/ asrev iew/ asrev iew-covid 19 14 FASTREAD (*) Screening SL 2020 1 https:// github.
com/ fastr ead/ src/ tree/ v1. 4. 0 15 GAPscreener Screening NLP, SL 2017 2 http:// hugen aviga tor. net/ HuGEN aviga tor/ HNDes cript ion/ opens ource_ GAP. html 16 Pvtopic (*) Screening, Data extraction SL 2016 1 http:// nactem. ac. uk/ pvtop ic 17 RapidMiner Screening NLP, SL, SSL, USL 2021 6 http:// rapid-i. com 18 Rax Search, Screening, Data NLP, SL 2021 1 https:// raxter. io extraction 19 SWIFT-Review Search, Screening SL 2019 8 https:// www. sciome. com/ swift-review Software packages/libraries 20 BIBOT (*) Search NLP 2018 1 https:// github. com/ Nurtal/ BIBOT-light-versi on 21 Costumer (*) Search SL 2017 2 https:// github. com/ UBESP- DCTV/ costu mer 22 Litsearchr (*) Search NLP 2019 2 https:// github. com/ eliza grames/ litse archr 23 Revtools (*) Deduplication, Screening NLP 2019 2 https:// cran.
iris. ai extraction 44 Leximancer Search NLP, USL 2021 2 https:// www. lexim ancer. com 45 Linguamatics Search NLP 2021 1 https:// www. lingu amati cs. com 46 PICO Portal Screening, Deduplication NLP, SSL 2021 1 https:// picop ortal. org 47 Rayyan Screening NLP, SL 2021 17 https:// www. rayyan. ai 48 RCT tagger Search NLP, SL 2017 6 http:// arrow smith. psych. uic. edu/ cgi-bin/ arrow smith_ uic/ RCT_ Tagger. cgi 49 Research Screener Screening NLP, SL 2021 1 https:// resea rchsc reener. com 50 RobotAnalyst Search, Screening SL 2018 11 http:// www. nactem. ac. uk/ robot analy st 51 RobotReviewer (*) Data extraction, Risk of Bias NLP, SL 2020 14 https:// www. robot revie wer. net/ 52 RobotSearch Screening NLP, SL 2019 5 https:// github. com/ ijmar shall/ robot search 53 Sample Size Search (SSS) Tool Search NLP 2020 1 https:// iheal th.
in climate4impact: icclim • Python code developed at Cerfacs since September 2013 • Funded by EU FP7 IS-ENES2, FP7 CLIPC and H2020 IS-ENES3 • Generic and modular approach, can be reused in other environments • New V5 completely rewritten and using underlying xclim functions, based on xarray and dask • I/O interface is structured for optimal performance • Implement the proper percentile indices calculations when calculation period overlaps reference period (called bootstrapping method).
Probability distributions are very useful in modeling lifetime datasets. However, no specific distribution is suitable for all kinds of datasets. In this study, the bounded truncated Cauchy power exponential distribution is proposed for modeling datasets on the unit interval. The probability density function exhibits desirable shapes, such as left-skewed, right-skewed, reversed J, and bathtub shapes, whereas the hazard rate function displays J and bathtub shapes. For the purpose of modeling dependence between measures in a dataset, a bivariate extension of the proposed distribution is developed. The bivariate probability density function displays monotonic and non-monotonic shapes, making it suitable for modeling complex bivariate relations. Subsequently, the applications of the distribution are illustrated using COVID-19 data.
Among these probability distributions used for modeling diseases, those defined on the unit interval play a major role due to their usefulness in areas such as health, psychology, and epidemiology, among others. For instance, researchers may be interested in modeling mortality or recovery rates. Observations measured on these variables are usually proportions, fractions, or rates, which are defined in the unit interval. Although the beta distribution is the oldest for modeling datasets measured on the unit interval, the intractability of its cumulative distribution function (CDF) and quantile function has called for the development of new distributions with tractable CDFs and quantile functions that are also capable of modeling data on the unit interval. Unit distributions proposed recently in literature include: unit Gamma/Gompertz distribution  [12] , bounded odd inverse Pareto exponential distribution  [13] , bounded shifted Gompertz distribution  [14] , unit modified Burr-III distribution  [15] , unit generalized half normal distribution  [16] , unit Lindley distribution  [17] , unit Gompertz distribution  [18] , logit slash distribution  [19] , unit Weibull distribution  [20]  and unit inverse Gaussian distribution  [21].
The Moors (coefficient of) kurtosis is usually given by K(α, λ) = Q(7/8; α, λ) -Q(5/8; α, λ) + Q(3/8; α, λ) -Q(1/8; α, λ) Q(3/4; α, λ) -Q(1/4; α, λ). Large values of the Moors kurtosis imply that the distribution has a heavy tail, and small values are indications of a light tail. Figure  3  displays the Moors kurtosis for the BTCPE distribution. It can be observed that the BTCPE distribution exhibits various degrees of kurtosis. When the parameters α and λ are equal, the distribution displays a platykurtic shape. The overall shapes show how flexible the BTCPE distribution is with regards to modeling datasets having different degrees of kurtosis and skewness. The Moors (coefficient of) kurtosis is usually given by  ( Q Q Q Q K Q Q α λ α λ α λ α λ α λ α λ α λ - + - = -. Large values of the Moors kurtosis imply that the distribution has a heavy tail, and small values are indications of a light tail.
Bivariate Extension Researchers may be interested in modeling the dependence between two (quantitative) measures in a dataset. For instance, one may be interested in modeling the relationship between age and the body mass index of individuals. Bivariate distributions have been used in reliability analysis, queuing theory, finance, and insurance risk analysis, among others, to study interdependency (see  [26] ). In this section, the bivariate extension of the BTCPE (BEBTCPE) distribution is proposed following the strategy developed in  [26]  and  [27]. Given a bivariate continuous random vector ( , ) X Y , the CDF of the BEBTCPE distribution with parameters 1 2 3 , , , , α λ δ δ δ , where 0, 0, α λ > > 1 3 1 1 δ δ -< + < , 2 3 1 1 ,0 1 x δ δ -< + < < < and 0 1 y < < ,.
Bivariate Extension Researchers may be interested in modeling the dependence between two (quantitative) measures in a dataset. For instance, one may be interested in modeling the relationship between age and the body mass index of individuals. Bivariate distributions have been used in reliability analysis, queuing theory, finance, and insurance risk analysis, among others, to study interdependency (see  [26] ). In this section, the bivariate extension of the BTCPE (BEBTCPE) distribution is proposed following the strategy developed in  [26, 27]. Given a bivariate continuous random vector (X, Y), the CDF of the BEBTCPE distribution with parameters α, λ, δ 1 , δ 2 , δ 3 , where α > 0, λ > 0, -1 < δ 1 + δ 3 < 1, -1 < δ 2 + δ 3 < 1, 0 < x < 1 and 0 < y < 1, is given by F XY (x, y; η) = (1 -4 π arctan[(1 -x λ ) α ])(1 -4 π arctan[(1 -y λ ) α ]) 1 + (δ 1 + δ 3 ) 4 π arctan[(1 -x λ ) α ] + (δ 2 + δ 3 ) 4 π arctan[(1 -y λ ) α ] -1 , (12) where η = (α, β, δ 1 , δ 2 , δ 3 ) T.
, n is defined in Equation (  14 ) with y = y i. The estimates of the parameters can be obtained by maximizing the total log-likelihood function directly using MATLAB, MATHEMATICA and R software. In this study, the R software is used  [28]. Alternatively, the estimates of the parameters can be obtained by equating the first partial derivatives with respect to the parameters to zero and solving the resulting system of equations simultaneously. However, since the resulting system of equations does not have a closed form, the nonlinear system of equations ∂ℓ * n ∂α , ∂ℓ * n ∂λ T = (0, 0) T is solved numerically to obtain the estimates of the parameters.
Applications Three applications of the BTCPE distribution are illustrated in this section, and its performance is compared to other competitive distributions defined in the unit interval. The performance of the BTCPE distribution was compared with that of the beta, unit Burr-III (UBIII)  [29] , bounded M-O extended exponential (BMOEE)  [30] , unit Gompertz (UG)  [18] , unit Lindley (UL)  [17] , unit Weibull (UW)  [20]  and unit-improved second-degree Lindley (UISDL)  [31]  distributions. The Akaike information criterion (AIC), Bayesian information criterion (BIC), Anderson-Darling (AD) test, and Cramér-von Mises (CVM) are the model selection techniques employed in arriving at the best model. For these selection techniques, the best model is the one with the smallest test statistic. The datasets represent the mortality rate of COVID-19 patients in Canada and the United Kingdom (UK), and the recovery rate of COVID-19 patients in Spain.
The first two datasets were recently reported by  [8]. The first dataset is the mortality rate for UK from 1 December 2020 to  29   The third dataset constitutes the recovery rates of COVID-19 patients in Spain from 3 March to 7 May 2020. The dataset can be found in  [1]  and are: 0. 6670, 0. 5000, 0. 5000, 0. 4286, 0. 7500, 0. 6531, 0. 5161, 0. 7895, 0. 7689, 0. 6873, 0. 5200, 0. 7251, 0. 6375, 0. 6078, 0. 6289, 0. 5712, 0. 5923, 0. 6061, 0. 5924, 0. 5921, 0. 5592, 0. 5954, 0. 6164, 0. 6455, 0. 6725, 0. 6838, 0. 6850, 0. 6947, 0. 7210, 0. 7315, 0. 7412, 0. 7508, 0. 7519, 0. 7547, 0. 7645, 0. 7715, 0. 7759, 0. 7807, 0. 7838, 0. 7847, 0. 7871, 0. 7902, 0. 7934, 0. 7913, 0. 7962, 0. 7971, 0. 7977, 0. 8007, 0. 8038, 0. 8289, 0. 8322, 0. 8354, 0. 8371, 0. 8387, 0. 8456, 0. 8490,0. 8535, 0. 8547, 0.
8564, 0. 8580, 0. 8604, 0. 8628, 0. 6586, 0. 7070, 0. 7963, 0. 8516. The ML estimates of the parameters are estimated using the bbmle package in R  [32]. The initial values of the parameters of the fitted distributions used for the optimization are obtained using the GenSA package in R  [33]. Table  4  displays the descriptive statistics for COVID-19 mortality for the UK and Canada, as well as the recovery rate for Spain. The datasets are platykurtic due to the negative excess kurtosis. The UK mortality is rightskewed and that of Canada is left-skewed. The recovery rate for Spain is also left-skewed. This is affirmed by the boxplot of the datasets shown in Figure  7.
UK COVID-19 Mortality Table  5  presents ML estimates of the parameters and their corresponding standard errors in brackets, the log-likelihood (  ), AIC, BIC, AD, and CVM for the fitted distribu- tions. Given that it has the lowest values for the AIC, BIC, AD, and CVM and the maximum log-likelihood, the BTCPE distribution offers the best fit to the UK mortality dataset. Figure  9  is the probability-probability (P-P) plots of the fitted distributions. Figure  9  once more shows that the BTCPE distribution fits the UK drought mortality well because the points cluster along the diagonal. The profile log-likelihood plots for the estimated parameter values of the BTCPE distribution for the UK mortality data are shown in Figure  10. From the plots, it can be observed that the estimated values are the true maxima.
Canada COVID-19 Mortality Table  6  presents ML estimates of the parameters and their corresponding standard errors in brackets and model selection criteria for the fitted distributions. The BTCPE distribution again provides the best fit to the Canada mortality dataset since it has the highest log-likelihood and the lowest values of the AIC, BIC, AD, and CVM. Figure  9  is the probability-probability (P-P) plots of the fitted distributions. Fi once more shows that the BTCPE distribution fits the UK drought mortality well b the points cluster along the diagonal. Figure  9  is the probability-probability (P-P) plots of the fitted distributions. Figure  9  once more shows that the BTCPE distribution fits the UK drought mortality well because the points cluster along the diagonal. The profile log-likelihood plots for the estimated parameter values of the BTCPE distribution for the UK mortality data are shown in Figure  10.
Canada COVID-19 Mortality Table  6  presents ML estimates of the parameters and their corresponding standard errors in brackets and model selection criteria for the fitted distributions. The BTCPE distribution again provides the best fit to the Canada mortality dataset since it has the highest log-likelihood and the lowest values of the AIC, BIC, AD, and CVM. Figure  11  shows the empirical and fitted PDFs and CDFs of the various distributions used to model the Canada drought mortality dataset. The figure gives an indication that the BTCPE distribution provides a better fit to the drought mortality for Canada than the other models, as it mimics the empirical PDF and CDF of the dataset better than the other models. Figure  11  shows the empirical and fitted PDFs and CDFs of the various distributions used to model the Canada drought mortality dataset.
The figure gives an indication that the BTCPE distribution provides a better fit to the drought mortality for Canada than the other models, as it mimics the empirical PDF and CDF of the dataset better than the other models. Figure  12  shows the P-P plots of the fitted models. Figure  12  gives an indication that the BTCPE distribution provides a good fit to the Canada mortality as the points cluster along the diagonal. Figure  12  shows the P-P plots of the fitted models. Figure  12  gives an indication that the BTCPE distribution provides a good fit to the Canada mortality as the points cluster along the diagonal. Figure  12  shows the P-P plots of the fitted models. Figure  12  gives an indication that the BTCPE distribution provides a good fit to the Canada mortality as the points cluster along the diagonal.
Spain COVID-19 Recovery Rate The ML estimates of the parameters and their corresponding standard errors in brackets and model selection criteria for the fitted distributions are shown in Table  7. Because it has the lowest values for the AIC, BIC, AD, and CVM and the maximum log-likelihood, the BTCPE distribution again offers the best fit to the Spain recovery rate dataset. The empirical and fitted PDFs and CDFs of the various distributions used to model the Spain recovery rate dataset are shown in Figure  14. It can be seen that the BTCPE distribution provides a better fit to the recovery rate data than the other models. The P-P plots of the fitted models for the recovery rate data are displayed in Figure  15. The plots indicate that the BTCPE distribution provides a good fit to the recovery rate data as the points cluster along the diagonal.
Residual Analysis Model diagnostics are very essential when fitting a model to a dataset. Often, the behavior of the model residuals is examined to see if the model really provides a good fit to the data. In this study, the randomized quantile residuals are used to assess the adequacy of the regression model. The randomized quantile residuals are defined as r i = Φ -1 (F Y (y i ; α, θ)), i = 1, 2,. , n, where Φ -1 (•) is the quantile of the standard normal distribution. The randomized quantile residuals are expected to be distributed as the standard normal distribution if the models provide a good fit to the data.
Application The application of the quantile regression model is demonstrated in this section using a real dataset. The data are taken from  [34]  and are also available at http://www. leg. ufpr. br/doku. php/publications:papercompanions:multquasibeta (accessed on 30 August 2022). The data consist of body fat percentage (response variable) measured in five regions: android, arms, gynoids, legs and trunk. The data are comprised of 298 observations and the independent variables are: age (in years), body mass index (in kg/m 2 ), sex (female or male) and IPAQ (sedentary (S), insufficiently active (I), or active (A)). In this study, the response variable body fat percentage at arms is regressed on age (z i1 ), body mass index (z i2 ) and sex (z i3 , 0 for female and 1 for male). The response variable is regressed on the covariates using the relationship logit(ρ i ) = θ 0 + θ 1 z i1 + θ 2 z i2 + θ 3 z i3 , i = 1, 2,.
Conclusions In this study, the BTCPE distribution is proposed for modeling datasets that are defined on the unit interval. The PDF of this distribution exhibits left-skewed, right-skewed, reversed J, and approximately symmetric shapes. The HRF displays increasing and bathtub shapes. This makes the distribution a suitable candidate for modeling datasets that exhibit these traits. Nine estimation methods were proposed for estimating the parameters of the distribution, and simulation results revealed that most of these estimates were consistent when it came to the estimation of the parameters of the distribution. The applications of the BTCPE distribution were illustrated using datasets on the mortality rate and recovery rates of COVID-19. The results revealed that for the three datasets, the BTCPE model provided a better fit than the other competing models.
We present a study of the demographics of major conferences in heavy ion physics. We look at the distribution of talks by gender for Quark Matter, Strangeness in Quark Matter, Initial Stages, and Hard Probes between 2011-2022. We find that women are often underrepresented among plenary speakers and usually underrepresented among parallel speakers. At Quark Matter, women are more likely to be given a poster presentation in lieu of an oral presentation. The Quark Matter summary talk has never been given by a woman. We discuss the collection of data and possible approaches to make the field more equitable and, therefore, more scientifically productive.
There is even an online calculator to estimate the probability of underreprepresenting women  [2] ,demonstrating that the observed results are highly improbable if talk allocation were equitable. However, it is difficult to infer the state of the field from data on these isolated conferences. We compiled data on major conferences from heavy ion physics listed in in tab. I using the steps outlined below: 1. Use a python script to scrape participant data from Indico timetable 2. Upload data to database 3. Remove chairs, student lectures, non-physics talks  1  , and flash talks  2 4. Separate plenary talks, parallel talks, and poster presentations 5. Organize all ∼ 2500 names in a central database and identify gender identity and whether a theorist or experimentalist. Crowd source to assist with identification.
6. Determine uncertainties by assuming all unidentified presenters (excluding known non-binary physicists) are either male or female. We recognize the limitations of identifying gender through crowd sourcing rather than through self-identification, however, this study would not be possible otherwise. We did not attempt to study geographical location or career stage, as these change over time and would be trickier to study. We focus on major conferences, but include some minor conferences in the database of all speakers to counteract possible biases in speakers at major conferences. People removed in step (3) are also included in this database when they are physicists in the field. We can then use these data to get a good sample of the field, look at the distribution of talks over time for different conference series, and track an individual's presentations over time.
Demographics of the field Our database of people in the field is approximately 40% theorists and 60% experimentalists. Women are approximately 32% theorists and 68% experimentalists and men are approximately 42% theorists and 58% theorists. The statistics from the database, however, could be skewed because it represents people who are already giving talks at high profile conferences and would not include people who do not get the opportunity to speak at conferences. Overall, approximately 19% of all speakers are women, and our data indicate that the number of women has increased during this time period. There is a greater fraction of female experimentalists than female theorists. We investigate alternate measures of the fraction of women in the field. Each experiment maintains a list of authors, so it is fairly straightforward to estimate the fraction of experimentalists who are women.
Policies such a body could consider include: 1. Double blind review for first round of abstract review 2. Use a rubric for evaluation of abstracts 3. Use a multi-stage process for determining candidate plenary speakers for major conferences 4. Use a database of speakers to ensure that a wider range of plenary speakers is considered 5. Open call for (anonymous) speaker nominations 6. Require conference hosts to address procedure for allocating talks in host proposal 7. Explicit rules for how frequently someone can give a plenary talk 8. Increase the number of talks and posters Double blind review has been shown to lead to more papers accepted by women  [8]  and double blind applications for telescope time not only increased the fraction of women recieving time but also increased the fraction of first-time users  [9].
This should translate to consideration of conference speakers, so a data based can be used to help ensure that a complete list of candidates is considered. Inspirehep. net maintains a database of all researchers in high energy physics. Features which would enable searches for possible speakers in different topical areas would be useful for multiple conferences, as well as for job searches. Particularly for high profile conferences, there could be an open call for speaker nominations, including anonymous and self-nominations. This would also help ensure that a complete pool of speakers is considered. Furthermore, conference hosts should be required to address how talks will be allocated in their host proposal. This would help prevent surprises and mistakes repeated over multiple conferences.
6 Acknowledgements This work was supported in part by funding from the  Division of Nuclear Physics of the U. S. Department of Energy  under Grant No. DE-FG02-96ER40982. C. A. has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  893021  ( JQ4LHC ).