dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The  Galaxy  Project has grown in large part thanks to the contributions of time and effort by numerous individuals over the years. Contributing individuals include members of the Galaxy user, developer and administrative communities and organizers of Galaxy Community Conferences. We are indebted to these helpful people. Public Galaxy site is located at the  Texas Advanced Computing Center (TACC at the University of Texas ). We are extremely grateful to both TACC and CyVerse for enabling Galaxy to serve thousands of researchers worldwide.
GENDER EQUITY AT SCIENTIFIC EVENTS data from three large scientific societies as a reference current baseline of women in the field (i. e. , constituting the pool from which potential invited speakers would be drawn). Depending on the type of event, the proportion of women among invited speakers is either comparable (conferences), significantly lower (courses), or significantly higher (congress symposia) than the current baseline. A higher proportion of women among the organizers has a positive, significant effect on the proportion of women among the invited speakers across all types of events. Similarly, we find that gender awareness among the organizers and presence of explicit equalopportunity guidelines also increase the proportion of invited women. Our results suggest that changes in the proportion of invited women, if desired, require deliberate actions.
Over 18 months, we collected data on invited speakers and organizers from all scientific events advertised on "EvolDir" (Evolution Directory, a listserv compiling, among others, announcements of conferences and courses within evolutionary biology), and from all symposia at three large evolutionary biology congresses that took place in 2017. To elucidate factors affecting the proportion of female invited speakers, we sent a questionnaire to the organizers, asking whether they had taken gender or specified Equal Opportunity (EO) guidelines into account when choosing speakers. In parallel, we obtained information on gender and career stage of members from three important evolutionary biology societies, allowing us to evaluate the current baseline proportion of female researchers likely to be invited to speak at scientific events.
Estimation of the current baseline proportion of women We contacted scientific societies within the field and asked about the demographics of their members, to estimate the available pool of female researchers that could potentially be invited to speak at scientific events. Three societies agreed to share their membership data: the European Society for Evolutionary Biology (ESEB), the American Society of Naturalists (ASN), and the Society for the Study of Evolution (SSE; data are summarized in Table  1 ). While the ESEB dataset only separates Student and Non-Student members, the ASN and SSE datasets could be divided into Student, Postdoc and "Faculty" members, the latter comprising all membership categories that are not Student nor Postdoc (i. e. , Regular, Life, Complimentary members, etc.
). While some scientists are members of multiple societies, we do not a priori expect their genders to be biased. Finally, we also obtained membership archives for ESEB (since 2011, Table  S3 ) and  SSE (2008 and 2009, Table   S4 ). Conference and course data  (2016) (2017)  Each month, from April 2016 to September 2017, we downloaded all emails sent to the EvolDir mailing list (http://life. mcmaster. ca/evoldir. html) under the categories "Conferences" and "WorkshopsCourses. " Announcements were excluded if the event had been announced previously (duplicate), did not have invited speakers, was not about evolutionary biology (e. g. , general computing course), or if the event was run by a private company. From this dataset, we also excluded ads corresponding to symposia at the 2017 Evolution ASN and SSE Spotlight Sessions, Society for Molecular Biology and Evolution (SMBE) and ESEB congresses; they were included in a second dataset ("Congress symposia" dataset, cf.
section below). In total, we screened 752 ads, of which 249 were included in our study. For each included event, we counted the number of announced invited speakers, organizers, and women among them, using information from the EvolDir announcement and the event's website. Gender was inferred using the researchers' first names if they were transparent and unambiguous (e. g. , John, Jane); otherwise, we searched for pictures on institutional or personal webpages. For simplicity, only binary genders were considered. We focused on the announced invited speakers (i. e. , researchers who had accepted the invitation) to evaluate the general stage presence of men and women, i. e. , what the audience experiences. For this reason, we did not exclude speakers who were also organizers. Focusing on announced invited speakers rather than people originally contacted by organizers made data collection relatively straightforward, and, importantly, independent of whether organizers had kept a record of all invitations they originally sent.
A text box was available for comments (Q4; not included in the shared dataset), and lastly the organizer was asked for consent to share their answers (Q5, "Yes"/"No"). If organizers declined to share their answers, which happened in 3 out of 208 events overall, Q1-3 were blinded in the shared dataset. A reminder email was sent to each organizer who had not replied within three weeks, resulting in 65. 8% of all emailed organizers responding to our questionnaire. Team 2015). We used a binomial error distribution and a logit link function that accounts for differences in the total number of invited speakers among events. All continuous fixed effects (cf. below) were scaled to a mean of zero and standard deviation of one prior to analysis  (Schielzeth 2010). We performed backward stepwise model selection using Chi-squared likelihood ratio tests  (Bolker et al.
2009). For each analysis, the 95% confidence intervals based on parametric bootstrap (n = 500 replicates) of all tested effects  (Bates et al. 2015; Canty and Ripley 2016)  are presented in Tables  S5-S13. For conciseness, we concentrate on significant effects in the Results section. All datasets and R scripts necessary to reproduce the analyses are available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. nm35n. For each dataset, we analyzed the proportion of female invited speakers using generalized linear-mixed models (GLMMs). Potential overdispersion  (Harrison 2015)  was accounted for by introducing an observation-level random effect. We tested for an effect of the gender composition of the organizers by including the proportion of female organizers as a continuous covariate.
The analysis of the Conference, Course, and Congress symposium datasets included two additional continuous covariates: the total number of organizers (to test if more organizers results in more diverse views and more invited women), and the total number of invited speakers (to test for saturation effects on the proportion of female speakers in large events). We then analyzed the replies to the questionnaire. First, we tested whether the proportion of female invited speakers affected the organizers' probability of replying to our survey, using a generalized linear model (GLM). Then, we subsetted each of the three datasets to only include events for which a reply was obtained, and tested the effects of the aforementioned covariates as well as the factors corresponding to the answers to survey questions Q1, Q2, and Q3.
We compared the proportion of female invited speakers within each contemporary dataset to our estimate of the current proportion of female researchers in the field using generalized linear models (GLMs). We combined each dataset with the membership data and included a factor with a two-level fixed effect (collected data vs. society membership data). This test takes into account sampling error in our estimation of the baseline proportion of women by considering the total numbers of society members. For the longitudinal congress symposium datasets, we used the same procedure to compare the proportion of female invited speakers to the proportion of female non-student members of the corresponding society at the time, whenever membership data were available. Finally, we used the longitudinal congress symposium dataset to investigate a potential increase in the proportion of female invited speakers over time (GLM, with year as a continuous covariate).
CURRENT BASELINE PROPORTION OF WOMEN IN SCIENTIFIC SOCIETIES Pooling 2016-2017 membership data across the SSE, ASN, and ESEB societies, we obtain an estimated percentage of 32% women potentially invited to speak (this estimate excludes the students from the three societies as well as SSE and ASN Postdocs, see Table  1 ). This is the current baseline used for comparisons. Note that by including the SSE and ASN Postdoc categories, the estimated percentage of women increases to 34%, while including all categories gives a percentage of 40% women.
S2B ; GLM, replied to survey; χ 2 1 = 4. 4, P = 0. 036, but with a 95% bootstrap confidence interval of the estimate overlapping zero, Table  S12 ), suggesting that the subsample of events with replies might have been slightly biased. Contrary to the Conferences dataset, we do not find an effect of whether gender was taken into account when choosing whom to invite (GLM, Q2; χ 2 1 = 0. 3, P = 0. 57), but this might be because only five respondents replied "No. " Unlike the other datasets, the Congress symposia dataset is rather homogeneous, in that all symposia of a given congress adhere to the same Equal Opportunity guidelines (if present; the available Equal Opportunity guidelines are listed in Appendix B. 1). Yet, the replies revealed a great disparity in the perception of Equal Opportunity guidelines (Fig.
ESEB ESEB congresses are usually biennial, and have a large number of symposia (on average 31 symposia per congress in our dataset) with few invited speakers (on average 2. 3 per symposium in our dataset). None of the original ESEB calls for symposia available in the EvolDir archive (2005-2017) mention speaker diversity. However, prior to the 2015 congress, an email was sent on EvolDir to encourage gender balance among speakers, and in both 2015 and 2017, diversity statements were published on the congress website (see Appendix B. 2). This followed a study pointing out a low proportion of female invited speakers at ESEB congresses  (Schroeder et al. 2013) , which led to the formation of an Equal Opportunity Committee at ESEB. The increase in the proportion of female invited speakers is likely linked to these events (Fig.
Evolution -SSE Evolution congresses are annual, with a small number of symposia per society (whereof only SSE were included in this part of our study), but a higher number of invited speakers per symposium than ESEB congresses (on average 7. 5 per symposium in our dataset). All calls for SSE symposia found in the EvolDir archive mention speaker diversity, and all but one also explicitly mention gender diversity (see Appendix B. 2). No temporal trend in the proportion of female invited speakers was detected (GLM, year effect, χ 2 1 = 0. 3, P = 0. 58; see Fig. 4  B), and the proportion of female invited speakers was not different from the proportion of women among non-student SSE members in the years for which we obtained membership data (GLMs, event vs society data, 2008: χ 2 1 = 0. 2, P = 0.
The use of public lists of female scientists (e. g. , "Anne's list" of 350 female neuroscientists  (Churchland 2017) ; "Diversify EEB," containing over 1200 female ecologists and evolutionary biologists  (Duffy and Baucom 2016) ; "AcademiaNet" with 2445 female academics across all disciplines) could be a way to avoid such a saturation effect. The Conference dataset reveals that the proportion of female invited speakers is higher when organizers take gender of invited speakers into account. This result shows that the proportion of female invited speakers can be changed through deliberate, conscious measures. Indeed, conference organizers who did not take the gender of invited speakers into account invited on average significantly lower proportions of women than the baseline estimated from society membership data.
• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Equal Opportunity guidelines may be a remedy to help organizers consider the gender, and more generally, the diversity of the scientists that they invite. However, the results from the Symposia dataset indicate that organizers were not always aware of guidelines edicted by the congresses and ultimately by the scientific societies. To be effective, guidelines should be systematically mentioned in calls for symposia, and be published at a visible location on congress websites. Unfortunately, we cannot exclude that the discrepancy is caused in part by an unclear formulation of Question 3; it asked for the existence of guidelines imposing or suggesting a given proportion of female speakers, while the diversity statements published by the scientific societies merely encouraged organizers to consider (gender) diversity.
This technical issue could explain why we found no effect of reported guidelines awareness on the proportion of female speakers in our Symposia dataset. Another reason could be the internalization of guidelines: organizers who are aware of the need to consider diversity may not necessarily pay attention to the existence of specific diversity statements. The results from the longitudinal symposia dataset corroborates the overall positive effect of Equal Opportunity guidelines specified by scientific societies. All available SSE calls for symposia contain diversity statements, and the proportion of women is not different from the 32% baseline (2016-2017 estimate). ESEB congresses saw a major increase in the proportion of female invited speakers in symposia in 2015 and 2017. Importantly, at least in 2017, this effect was not due to selective acceptance of symposium proposals with higher proportion of females among the suggested invited speakers: the increase was visible even in the submitted symposia  (Hannah Dugdale, pers.
ACKNOWLEDGMENTS We thank all the contacted organizers who took the time to fill in our survey. We are grateful to the  ESEB (Ute Friedrich) ,  SSE  ( Sally Otto  and the  SSE council) , and  ASN  ( Trish Moore ) for sharing membership data, and to  Julia Schroeder  and  Hannah Dugdale  for sharing the 2001-2011 ESEB data. We wish to thank  Brian Golding  for managing the EvolDir mailing list. Francois Blanquart,  Sally Otto , and  Anne Charmantier  provided useful comments on a previous version of the manuscript. Shermin de Silva  initially helped us collect data and suggested using of society membership data. This work received funding from the  ESEB Equal Opportunities Initiative Fund. Additionally, we were funded by  CeMEB LabEx/University of Montpellier (NOR) ; the  French National Research Agency , grant  ANR-14-ACHN-0003-01  (FD); the  Independent Research Fund Denmark , grant  DFF-4090-00032 , and  L'Oréal-UNESCO for Women in Science Award (LVU).
While a certain level of epistemic opacity is inevitable if we want to delegate computations to a machine, there are also many sources of accidental epistemic opacity that can and should be eliminated in order to make scientific results as understandable as possible. As an example, a major cause for non-reproducibility is the habit of treating an executable computer program, such as the Firefox navigator or the Python interpreter, as an abstraction that is referred to by a name. In reality, what is launched by clicking on the Firefox icon, or by typing "python" on a command line, is a complex assembly of software building blocks, each of which is a snapshot of a continuous line of development. Moreover, a complete computation producing a result shown in a paper typically requires launching many such programs.
Motivation The topics I will cover in this article will probably seem rather abstract and theoretical to most practitioners of computer-aided research. The two personal anecdotes in this section should provide a more down-to-earth motivation for the analysis that follows. Readers who do not need further motivation can safely skip this section. In 1997, I wrote an implementation of the popular AMBER force field for biomolecular simulations  [6]  as part of a Python library that I published later  [16]. A force field is a function U (X, Φ, G) expressing the potential energy of a molecular system in terms of the positions of the atoms, X, a set of parameters, Φ, and a labelled graph G that has the atoms as vertices, the covalent bonds as edges, and an "atom type" label on each vertex that describes the chemical environment of the atom.
Force fields are the main ingredients to the models used in biomolecular simulation, and the subject of much research activity, leading to frequent updates. The computation of a force field involves non-trivial graph traversal algorithms that are habitually not documented, and in fact hardly even mentioned, in the accompanying journal article, which concentrates on describing how the parameter set Φ was determined and how well the force field reproduces experimental data. I quickly realized that the publication mentioned above plus the publicly available parameter files containing Φ with their brief documentation were not sufficient to re-implement the AMBER force field, so I started gathering complementary information by doing test calculations with other software implementing AMBER, and by asking questions on various mailing lists.
However, the feature wasn't documented anywhere else than in the source code of a piece of software, which was never peer reviewed at all. Over the years, I have mentioned this feature to many colleagues, who were all as surprised as I was, and often believed me only after checking for themselves. To the best of my knowledge, no paper and no software documentation mentions this behavior. I can think of only three ways to stumble on it: by experimenting with the atom order in input files, by reading the source code of a simulation program, or by talking to someone who happens to know. In fact, I am not even sure that all software implementing AMBER handles this feature in the same way, given that is in general impossible to obtain identical numbers from different software packages for many other reasons.
The popularity of computational notebooks, introduced in 1988 with the computer algebra system Mathematica [37] and later implemented for a wide range of programming languages by the Jupyter project  [20] , shows that the re-unification of informal and formal reasoning into a single document corresponds to a real need in the scientific community. The computational notebook is a variant of the earlier idea of literal programming  [22] , which differs in that the formal element embedded into the narrative is not a piece of software but a computation with specific inputs and outputs. Its popularity is even more remarkable in view of the restrictions that today's implementations impose: a notebook contains a single linear computation, allowing neither re-usable elements nor an adaptation of the order of presentation to the structure of the surrounding narrative.
Both restrictions are a consequence of the underlying computational semantics: the code cells in a notebook are sent, one by one, as input to a command-line interpreter. Non-linear control flow can only happen inside a cell, and there is no way to refer to another cell, or sequence of cells, in order to re-use its contents. Notebooks can therefore capture only the top layer of the software stack shown in Fig. 1 , and even that only for relatively simple cases. Another example for the re-unification of informal and automated formal reasoning is given by textbooks that include short computer programs to explain scientific concepts. Most of them deal specifically with computational science and use the code as examples, e. g. (author?)  [24] , but some aim at conveying more fundamental scientific concepts using executable code for preciseness of notation  [35, 36].
Digital Scientific Notations A digital scientific notation is a formal language that is part of the user interface of scientific software. This definition includes, but is not limited to, formal languages embedded into informal scientific discourse, as in the case of computational notebooks. Another important category contains the file formats used to store scientific datasets of all kinds. As I have explained in section 5, the scientific information to be expressed in terms of digital scientific notations includes everything that is relevant at the user level, i. e. everything that has an impact on the results of a computation. This includes in particular scientific models and computational methods, but also more traditional datasets containing, for example, experimental observations, simulation results, or fitted parameters.
For example, the Python language has three distinct data types for sequences: lists, tuples, and arrays. There are good technical reasons for having these separate types, but for scientific communication, the distinction between them and the conversions that become necessary are only a burden. Another disadvantage is that the source code of a programming language reduces all scientific knowledge to algorithms for computing specific results. This process implies a loss of information. For example, the predator-prey equations from Eq. 5 contain information that is lost in a floating-point implementation of its discrete approximation because the latter can no longer be used to deduce general properties of exact solutions. Domain-specific languages (DSLs) are another increasingly popular choice for representing scientific knowledge.
In contrast to general-purpose programming languages, DSLs are specifically designed as digital scientific notations, and usually avoid the two main disadvantages of programming languages mentioned above. Most scientific DSLs are embedded in a general-purpose programming language. A few almost arbitrarily selected examples are Liszt, a DSL for mesh definitions in finite-element computation, embedded in Scala  [9] , Kendrick, a DSL for ODE-based models in epidemiology, embedded in Smalltalk  [5] , and an unnamed DSL for micromagnetics, embedded in Python  [2]. The choice for an embedded DSL is typically motivated by simpler implementation and integration into an existing ecosystem of development tools and libraries. On the other hand, embedded DSLs are almost impossible to re-implement in a different programming language with reasonable effort, which creates a barrier to re-using the scientific knowledge encoded using them.
A stand-alone DSL is independent of any programming language, as is illustrated by Modelica  [13] , a general modeling language for the natural and engineering sciences for which multiple implementations in different languages exist. However, each of these implementations is a rather complex piece of software. Looking at how scientific DSLs are used in practice, it turns out that both embedded and stand-alone DSLs end up being a user interface for a single software package, or at best a very small number of packages. Adopting an existing DSL for a new piece of software is very difficult. One obstacle is that the existing DSLs can be too restrictive, having been designed for a narrowly defined domain. For embedded DSLs, interfacing the embedding language with the implementation language of the new software can turn out to be a major obstacle.