dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Availability Participant data, experimental software, and analysis scripts are available from: https://github. com/smathot/mind-writing-pupil. This repository also includes a ready-to-use package for using our HCI with supported systems (currently tested with EyeLink and EyeTribe eye trackers, and Windows and Linux operating A screencast of our method is available online: https://youtu. be/cGfkD2opTz4.
Software and Apparatus Eye position and pupil size were recorded monocularly with an EyeLink 1000 (SR Research, Mississauga, ON, Canada), a video-based eye tracker sampling at 1000 Hz. The right eye was recorded, unless the left eye provided a better signal. Stimuli were presented on a 21" ViewSonic p227f CRT monitor (1280 x 1024 px, 85 Hz) running Ubuntu Linux 14. 04. Testing took place in a dimly lit room. The experiment was implemented with OpenSesame  [35]  using the PsychoPy back-end  [36]  for display control and PyGaze  [37]  for eye tracking.
Acknowledgments The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement  n°622738  awarded to  Sebastiaan Mathôt  (http://cordis. europa. eu/project/rcn/187709_en. html). In addition, this research was funded by a  VIDI Grant   452-13-008  from the  Netherlands Organization for Scientific Research  to  Stefan Van der Stigchel  (http://www. nwo. nl/en/research-and-results/programmes/Talent+Scheme/awards/vidi+awards/vidi+awards+2013). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement  n°622738  awarded to  Sebastiaan Mathôt  (http://cordis. europa. eu/project/rcn/187709_en. html). In addition, this research was funded by a  VIDI Grant   452-13-008  from the  Netherlands Organization for Scientific Research  to  Stefan Van  without looking at them) attended [8]. funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data reduction and analysis method The data used in this work have been acquired by VIRTIS-M on 2015 February 28, March 15, April 27 and August 26. A list of the observations with all the relevant information is provided in Table  1. We list the observations in the VIS and IR channels of the instrument. The VIS and IR image cubes were taken at essentially the same time, with the VIS exposure sequences starting 9. 01 s after those for the IR. The second and third columns give file names according to the rules of planetary data system (PDS). To make the description of our data easier, we have assigned simple letter and number designations to these file names in the first column. The fourth column lists the image size in number of samples per line, number of scan lines and spectral bands. The fifth and sixth columns show the starting and stopping times (UT), while the following column indicates the total time of the observations.
In all the acquisitions, and in the images shown, the Sun illumination is always from the top of the figure. Also listed in the table are additional self-explanatory parameters such as the sub-solar longitude and latitude, which serve as an indicator of the orientation of the comet. Additionally, we list the distance from the surface, the phase angle and the heliocentric distance (r h ). The geometry information for each VIRTIS-M observation is calculated by a SPICE routine  (Acton 1996)  using the spacecraft's trajectory/orientation reconstructed kernels and 67P/CG SHAP5 shape model  (Jorda et al. 2016). We choose the March 15 data set because it allowed us to investigate the coma above the illuminated nucleus during a complete rotation of the comet. For this data, the FOV of the VIRTIS-M instrument is pointed at a roughly fixed location about 1 km above the illuminated surface of the comet.
The data set covers 11. 8 h, almost a complete rotation period of 67P/CG. The slit extends horizontally for 256 pixels (∼4. 5 km) and is scanned vertically for 66 lines (∼1. 15 km) using the scanning mirror of the instrument  (Coradini et al. 2007 ). We analysed a set of two image cubes obtained on February 28 at a distance from the comet of 103 km and at a heliocentric distance of 2. 21 au. The spacecraft was essentially looking down on the north pole of the comet. For April 27, a set of seven image cubes was obtained at a distance from the comet of 130-140 km, giving us an FOV of about 9. 0 × 7. 7 km with the comet centred roughly in the centre of the FOV. The spacecraft is approximately in a terminator orbit so that one side of the comet is illuminated by the Sun and the other side is in darkness.
For August 26, we analysed one image obtained at a distance from the comet of 410 km at a cometary heliocentric distance of 1. 2 au, post-perihelion. In view of large distance, the spatial resolution is about 100 m pixel -1 and the FOV shown is 10 × 26 km 2. The analysis of the dust continuum at certain locations can be limited by in-field stray light, which results from the presence of the nucleus within the slit of instruments. Many different causes are responsible for the stray light such as light scattered by optical elements, diffuse reflection from mirror surface imperfections and ghost orders caused by the diffraction grating  (Filacchione 2006). As a consequence of these optical effects, for the data of February 28 and April 27, when the instrument's slit is partially filled by the bright nucleus, a sizeable fraction of the incoming photons are spread into the adjacent coma pixels.
The April 27 data set On April 27, VIRTIS-M collected a set of seven hyperspectral cubes showing the full nucleus and its surrounding coma spanning 70 per cent of the full rotation period. The advantage of these observations lies in the capability of studying the coma components at all local times (above sunlit and unilluminated regions). The results of our analysis are shown in Figs  7  and 8 , where only the hyperspectral cubes a, d, e and g (Table  1  for details) are displayed as an example; the intermediate ones show the same behaviour. The maps are composite images where the comet nucleus image (taken as an average in the wavelength range between 4. 05 and 4. 15 μm) is superimposed on the maps of the dust continuum at 1. 1 μm (left-hand panels), the water vapour (middle panels) and the CO 2 band intensities (right-hand panels).
The Sun is always at the top of each image. The dust and gas intensity contour lines are in units of radiance (W -1 m -2 sr -1 μm -1 ). The presence of the nucleus in the acquisition introduces some stray light and affects the retrieval of the dust continuum profile. In the spatial regions where this is true, the dust maps are covered by semitransparent strip overlays (see Section 2). As for the March 15 data set, the temporal coverage allows study of the behaviour of the three components of the coma for various local times and imaged regions of the nucleus. Dust and water vapour have a reasonable and predictable behaviour correlated with the illumination conditions, and their intensity decreases moving away from the nucleus. The anticorrelation of CO 2 with respect to water vapour is most strikingly exhibited in this data set, as demonstrated in all four acquisitions shown.
The growth in scientific production may threaten the capacity for the scientific community to handle the ever-increasing demand for peer review of scientific publications. There is little evidence regarding the sustainability of the peer-review system and how the scientific community copes with the burden it poses. We used mathematical modeling to estimate the overall quantitative annual demand for peer review and the supply in biomedical research. The modeling was informed by empirical data from various sources in the biomedical domain, including all articles indexed at MEDLINE. We found that for 2015, across a range of scenarios, the supply exceeded by 15% to 249% the demand for reviewers and reviews. However, 20% of the researchers performed 69% to 94% of the reviews. Among researchers actually contributing to peer review, 70% dedicated 1% or less of their research worktime to peer review while 5% dedicated 13% or more of it.
Methods summary We used a mathematical modeling approach, informed by empirical data in the biomedical domain, to compare the quantitative peer-review demand and supply. We estimated the annual demand as the number of reviews and reviewers required to produce the observed annual number of published articles. The numbers of published articles were derived from MEDLINE for 1990 to 2015  (Fig 1A). We then estimated the corresponding total number of submissions. In fact, an article may be resubmitted multiple times, thus requiring additional reviews. We used the empirical distribution of the number of times papers are resubmitted from data for the biomedical domain in the 2009 Peer Review Survey, an international survey of 4,037 researchers  [8]. Moreover, we assumed that 20% of submissions ultimately remained unpublished.
We then estimated the corresponding total number of peer reviews (demand for reviews). Some submissions do not require any review, if they are "deskrejected" after in-house editorial screening. We assumed that the average proportion of deskrejected papers was 25%. Otherwise, we considered an average of 2. 5 reviewers per peer-review round and that 90% of the peer-reviewed submissions went through a second round of peer review  [11]. Finally, we estimated the total number of reviewers (demand for reviewers) by using the empirical distribution of individual contributions to the peer review effort (ie, the proportion of reviewers who reviewed 1, 2, 3 etc. papers in a given year) from data for 2015 in the Publons reviewer recognition platform (Fig  1B ). We estimated the annual peer-review supply as the number of potential reviewers and the number of reviews they could perform.
Considering that editors typically invite past authors to be peer reviewers, we assumed that potential reviewers in a given year were researchers who coauthored at least one paper that year (Scenario 1). We also considered more stringent scenarios (in terms of co-author consideration to be a potential reviewer) in which candidate reviewers were the first or last authors of any article during the previous 3 years (Scenario 2); the first, second or last authors for the same year (Scenario 3); and the first or last authors for the same year (Scenario 4). For Scenario 2, we arbitrarily chose a time window of 3 years, which however may reflect changes in the databases that editors use to find reviewers. For each scenario, we estimated the number of potential reviewers (supply for reviewers) by counting the unique author occurrences each year from all journal articles indexed in MEDLINE from 1990 to 2015 (Fig  1C ).
Finally, we estimated the total number of reviews they could perform (supply for reviews) by using the empirical distribution of individual contributions to the peer-review effort. We estimated the distribution of the proportion of research work-time devoted to peer review. For each researcher, we estimated the total time spent on peer review by using the empirical distribution of the time taken to perform each review from data for the biomedical domain in the 2009 Peer Review Survey (Fig  1D )  [8].
If N u 0 = 100, then the total number of submissions which did not result in a publication is 370. In reality we do not know both P I i¼1 R 0 i Â i and N u 0 and it would be impossible to obtain reliable data for them. However, we know P I i¼1 R i Â i and we can represent P I i¼1 R 0 i Â i in terms of it using a constant α. Then, we can group α and N u 0 into a single constant N u and work with Eq 1. We estimated the annual demand for reviews N reviews as: N reviews ¼ ð1 À dÞÂr s Â ðN s þ P I i¼1 S i Þ ð3Þ where d is the proportion of desk-rejected submissions, r s the number of reviewers per peer review round and S i the amount of papers that went to a second round of peer review in their i th (re)submission. We defined S i as follows: S i ¼ b Â ðN p þ N u Þ Â R i ð4Þ where β is the probability of a second peer-review round per submission that is not deskrejected.
clinicalepidemio. fr/peerreview_burden/). We programmed our simulations by using MATLAB (MATLAB and Statistics Toolbox Release 2014b, The MathWorks, Inc. , Natick, MA, USA). The code is available at https://github. com/kovanostra/global-burden-of-peer-review. We used data pertaining to the biomedical domain, except to estimate r s and the distribution of peer-review effort ( P J j¼1 P j ), for which we used data pertaining to all scientific disciplines. We extracted all records indexed as "journal articles" by MEDLINE from January 1, 1990 to December 31, 2015. We downloaded the xml files for each year separately and parsed them by using a script written in Python (also available on github). We excluded all records with no author name (e. g. , less than 0. 001% of all articles for 2015) and indexed all authors based on their "Last-Name", "ForeName" and "Initials".
For Scenario 2, we arbitrarily chose a time window of 3 years, which however may reflect changes in the databases that editors use to find reviewers. For each scenario, we repeated the same procedure of identifying the unique occurrences of authors as described above. For each scenario, the number of authors obtained was considered to represent the potential supply of reviewers (N reviewers-supply ) in any given year. We did not account for individual interactions between authors, editors and reviewers which may influence the potential supply of reviewers. We estimated the potential supply of reviews by using the relation N reviewsÀ supply ¼ N reviewersÀ supply Â P J j¼1 P j Â j. We obtained P I i¼1 R i and the empirical distribution of the time taken to perform each review from the 2009 Peer Review Survey, an international survey of 4,037 researchers  [8].
Data corresponded to the biomedical domain. We considered r s to be equal to 2. 5 reviewers per peer-review round  [11]. We obtained the empirical distribution of individual contributions to the peer-review effort ( P J j¼1 P j ) for 2015 from the Publons reviewer recognition platform. In Publons, reviewers mainly self-report the reviews they have completed (ie, by forwarding review receipts to them). Publons was launched in 2012 and thus we could not obtain data for all unique years of our analysis. We assumed that the distribution for 2015 was identical for every year from 1990 to 2015. To our best knowledge, reliable data pertaining to β, N u and d do not exist. We assumed that 90% of the peer-reviewed submissions went through a second round of peer review (β = 0. 9), the percentage of the finally unpublished papers was equal to the 20% of the total submissions (N u = γT s , γ = 0.
20) and that the average proportion of papers desk-rejected was 25% (d = 0. 25). Table A in S1 Appendix presents the values of the previously mentioned parameters. For each researcher, we estimated the total amount of time available for research t res , taking into account whether the researcher was full or part time. We used empirical data provided by the National Institute of Health and Medical Research of France (INSERM), which pertains to all its researchers. The total time spent in peer review was estimated by sampling the respective empirical distribution over the amount of reviews (j) completed by each reviewer. For example, if 65% of reviews required 1 to 5 hours to complete, 22% of them 6 to 10 etc. , then for each review that a reviewer performed we first drew at random the duration range: between 1 and 5 hours with probability 65%, between 6 and 10 with probability 22%, etc.
Sensitivity analyses We performed 25 sensitivity analyses in addition to our main analysis (Table A in S2 Appendix). We used distributions of peer-review effort other than Publons 2015. Under the same conditions, we obtained the respective distributions from Publons for the years 2013 and 2014. We also used a review effort distribution from only a single journal (Nature Materials 2002-2012). Publons data concerned in total about 70,000 researchers and more than 10,000 journals, whereas data from Nature Materials concerned about 4,500 researchers and a single journal. Finally, we varied the values of the parameters (β, γ, d). We summarized the results of all sensitivity analyses by using the relative difference between the annual number of potential reviewers and the annual demand.
Sensitivity analyses When using data from Publons 2014 and 2013, all scenarios to define potential reviewers produced a peer-review supply greater than the demand, except under the most stringent scenario (first or last authors for the same year), in which the demand was higher than the supply before When using data from Nature Materials, the scenarios in which all co-authors for the same year and first or last authors for the last 3 years produced a peer-review supply greater than the demand (the second after 1999). As compared with the most stringent scenarios (first, second or last authors and first or last authors for the same year), these data produced a peer-review demand greater than the supply (Fig A in S2 Appendix). For 2015, the supply exceeded the demand by about 30% for both less-stringent scenarios and the demand exceeded the supply by 120% for the most-stringent scenario.
Instead, we explored this possibility by varying the definition of the pool of potential reviewers according to the ranks of co-authors. Finally, we have not modeled the peer review system as a competitive market economy. In particular, we did not consider the price for peer review and how market forces would apply  [38]. Second, we focused on the biomedical literature and our results may not apply to other domains. Even though each discipline has its own characteristics, the biomedical domain accounts for about 44% of the global scientific publications in 2015, and our findings may have implications for domains beyond biomedical research. Third, we acknowledge that the reliability of our results depends on the data used to inform the modeling. Publons data may not be representative of the true distribution of the peer review effort; registered researchers, who self-report their reviews, may be more intrinsically motivated and more likely to do more reviews than unregistered researchers.
To our best knowledge, Publons is the only large-scale source of data about the peer review effort, with data for more than 70,000 reviewers and more than 10,000 journals. We have no data to exclude confidently any selection bias in registered Publons researchers, if any; however, the distribution in Fig  1B  shows that 42% of reviewers have reported a single review in 2015. Moreover, Publons has partnered so far with 13 publishers (including Wiley and SAGE), for which registered users automatically receive credit for the reviews they performed (86,910 reviews from 2,676 journals). This finding goes against an overrepresentation of more active reviewers. We have conducted sensitivity analyses based on data from one specific journal (Nature Materials)  [39]. We observed surplus potential reviewer supply when all authors and when the first or last authors across the last 3 years were eligible as reviewers; under more stringent assumptions (1st, 2nd or last and 1st or last author within 1 year), we found a deficit in the reviewer supply.
However, researchers are likely to be invited and review for more than one journal; as a consequence, that distribution probably underestimates the effort distribution. As well, we have used data from the Peer Review Survey to inform the distribution of resubmissions before publication. Although these are the only data about the whole resubmission pattern, they are also limited by self-reporting and a response rate of about 10%. Calcagno et al. previously documented the late submission history of 80,748 articles in biological sciences (self-reporting data with a response rate of 37%) and found that about 75% of published articles were submitted first to the journal that published them. [30]. Another limitation is that our analysis relied on assumptions. However, we restricted these assumptions only to cases when empirical data were, to our best knowledge, not available.
Culture collections contain indispensable information about the microorganisms preserved in their repositories, such as taxonomical descriptions, origins, physiological and biochemical characteristics, bibliographic references, etc. However, information currently accessible in databases rarely adheres to common standard protocols. The resultant heterogeneity between culture collections, in terms of both content and format, notably hampers microorganismbased research and development (R&D). The optimized exploitation of these resources thus requires standardized, and simplified, access to the associated information. To this end, and in the interest of supporting R&D in the fields of agriculture, health and biotechnology, a pan-European distributed research infrastructure, MIRRI, including over 40 public culture collections and research institutes from 19 European countries, was established.
A prime objective of MIRRI is to unite and provide universal access to the fragmented, and untapped, resources, information and expertise available in European public collections of microorganisms; a key component of which is to develop a dynamic Information System. For the first time, both culture collection curators as well as their users have been consulted and their feedback, concerning the needs and requirements for collection databases and data accessibility, utilised. Users primarily noted that databases were not interoperable, thus rendering a global search of multiple databases impossible. Unreliable or out-of-date and, in particular, non-homogenous, taxonomic information was also considered to be a major obstacle to searching microbial data efficiently. Moreover, complex searches are rarely possible in online databases thus limiting the extent of search queries.
Curators also consider that overall harmonization-including Standard Operating Procedures, data structure, and software tools-is necessary to facilitate their work and to make high-quality data easily accessible to their users. Clearly, the needs of culture collection curators coincide with those of users on the crucial point of database interoperability. In this regard, and in order to design an appropriate Information System, important aspects on which the culture collection community should focus include: the interoperability of data sets with the ontologies to be used; setting best practice in data management, and the definition of an appropriate data standard.
1990). MINE led to the construction of the first major multinational model for a common catalogue that covered all types of microorganisms, including viruses. This project facilitated a number of different CCs in Europe, including the Centraalbureau Schimmelcultures (CBS, The Netherlands), Deutsche Sammlung von Mikroorganismen und Zellkulturen Gmbh (DSMZ, Germany), CAB International (CABI, United Kingdom), Collection de Levures d'Intérêt Biotechnologique (CLIB, France), as well as others from 10 European countries to produce common interoperable catalogues. A key feature in this Information System was the definition of common minimal datasets comprising approximately 60 characteristics, including, e. g. , taxonomy, physiological and biochemical properties, geographical origin, substrate of isolation, isolator, depositor  (Gams et al.