dataesr/scientific-paragraphs-categorization huggingface dataset:
Central concept(s) of NEUBIAS TSs Bioimage analysis is an emerging research field, which has progressed alongside the fast-paced development of bioimaging techniques of the last couple of decades  (Meijering 2007; Schneider et al. 2012; Meijering et al. 2016; Miura & Tosi 2016; Peng et al. 2012). However, the incorporation of new BIA techniques has lagged behind, leading to a bottleneck in the progress of image-based research, for which there are several reasons. First, most life-scientists who work with bioimage data lack the formal training on BIA; although imaging and bioimage data recording are now common practice in the life-sciences, training with image data analysis is still practically nonexistent in most life-sciences courses. Secondly, there is often a communication barrier between BIA algorithm developers and most end-users (so far, these have been two separate communities), which limits the assimilation of novel techniques and leads to gaps in BIA.
Compared to the conventional "image analysis" in Computer Science, which aims is to mimic the human visual perception and interpretation, i. e. Artificial Intelligence, BIA aims at the objective and quantitative measurement of biological phenomena avoiding human perception bias  (Miura & Tosi 2016). As visual recognition per se in most cases does not yield quantitative data, a new type of training that reaches beyond just the usage of software packages is required. Thirdly, modern scripting languages are necessary not only for automating, but also for properly and reproducibly documenting image data processing and quantitative measurements. For these reasons, more than just training about coding, the NEUBIAS TSs were designed to focus on training researchers about the "Art of Bioimage Analysis", its scientific concepts and how to properly build and document effective workflows, using representative hands-on exercises.
The uniqueness of these courses consisted in the training aimed at the quantification of biological processes, more than simply tutoring the usage of image analysis software packages and plugins. Through the financial support from H2020 COST Action CA15124 from 2016, NEUBIAS was able to expand the reach and attendees-throughput of these training efforts, and prepare a new programme for TSs targeting three main audiences and perceived levels of proficiency: the i) early career researchers, who need a solid and practical understanding of BIA concepts and techniques to carry on their biological research, and an overview of the existing tools and of the limits of ethics in BIA; the ii) imaging facilities staff, whose daily tasks involve supporting researchers in BIA and who require solid skills at designing and scripting effective   Bordeaux, Feb.
2020 (25+35 trainees). Map chart contains the locations of the different venues, the origin of the participants and a bar chart with the distribution of applicants based on their main expertise. See also Table  1  for more information about the schools, and online at http://eubias. org/NEUBIAS/training-schools/ for more information about the programme and venues. Authorization was obtained from the participants to capture and publicize group photos or photos taken during the event. and reproducible workflows; and the iii) bioimage analysts who need insights into advanced utilization and scripting of BIA resources, as well as the structured designing of novel workflows. More information about the concepts, content and training cohort of the NEUBIAS TSs can be found online 3,4. The schools included sessions explaining the theoretical concepts behind the BIA techniques, a detailed explanation of a workflow exemplifying an application of the techniques, and fully documented practical exercises (with presentation slides, image datasets and exercise solutions/code).
Choosing topics, trainers and trainees The topics were selected by the organizers based on perceived needs of life-scientists, the trends on publications and results of TS evaluation surveys. The TS promoted the use of open-source tools and workflows, not just to promote FAIR principles but also to guarantee easier access and applicability for all participants. The programme privileged the exploring of different tools, including learning on the advantages of one toolbox vs others; for example alternatives to visualization and rendering of 3D datasets, which is still limited in the popular ImageJ/FIJI toolbox, but better in Icy  (de Chaumont et al. 2012)  or Drishti (Limaye 2012); or the handling of pyramidal-multi-megapixel image data easily done with QuPath (Bankhead 2017); or preparing pipelines for recursively analysing thousands of images using CellProfiler  (Carpenter et al.
2006) , just to name a few. For those less familiar with scripting languages, TSs also showcased visual programming in KNIME  (Berthold et al. 2008)  or Icy. The schools also included sessions where trainees learned how to integrate different toolboxes to tackle complex workflows which spanned the capabilities of one particular toolbox (for example, how to integrate ImageJ/FIJI with QuPath or Imaris or Icy). During the unrolling of the programme there were shifts in the relative perceived importance of topics (seen also in the applications). For example, MATLAB became less requested and BIA with Python libraries and tools became a common request, which coincided with a significant increase in publication of machine/deep learning applications to BIA  (Meijering 2020). Handling of "big-data" appeared also on increasing demand, and both topics became prominent in TS after 2017-2018.
The choice of topics was sometimes influenced also by the coincidence with the NEUBIAS Symposia and the availability of high-profile speakers. The schools also counted with the input from the NEUBIAS taggers, a community of BIA enthusiasts who gathered during the TS events to catalogue information about software and workflows and develop online tools to help bioimage analysts, which then fed into the TSs programme. Applicants provided information that was used to match with an "ideal" profile outlined for each level of the TSs, as presented in Table  1. A committee of five evaluators read all applications anonymously and voted for acceptance based on the adequacy and preparedness of each candidate. This committee was typically composed of organizers and trainers of the current and previous events which helped maintain consistency in the selection process, and helped organizers tailor the priority topics and content of materials.
For this reason, we made a simple assumption that the minimal common denominator of Bioimage Analysts is the ability to code proficiently in at least one of major programming languages used in bioimage analysis (for example, ImageJ macro, R, Java, Python, MATLAB, C, C++). This requirement also arises counterintuitively from the high quality of current BIA software, open-source or commercial. For many well established analysis workflows there are implementations in software that can be used readily by biologists. These tools are often accompanied by useful documentation and a good userinterface, and several new tools of this desirable quality emerge every year. We reasoned that the core added value of a bioimage analyst would be the ability to address analysis tasks for which an end-user accessible tool does not exist yet.
(3) New algorithms, where we invited the developer of the state-of-art bioimage analysis components and asked for an in-depth lecture. (4) Benchmarking, where participants were asked to benchmark currently existing implementations. In the last Analyst school (2020), an additional 5th concept was introduced: a module for utilizing the latest statistical tool sets, such as the Tidy Data Handling (Bercowsky Rama, 2021) and the post-P value statistics (Gómez-de-Mariscal, 2019). With the recent refinement of high-content imaging technologies (e. g. High Throughput Screening), statistical analysis of rich multidimensional image data has increasingly become an important aspect of analysis workflows, and still requires to be further developed and integrated so as to become a solid part of the Bioimage Analysts training concepts.
With the collaboration of the trainers and speakers, NEUBIAS developed a collection of TS materials, which includes over 50 unique training sessions, fully documented with i) presentation slides ii) practical exercises with example data and iii) exercise solutions with instructions and code ("catch-up"). Currently, these materials are stored on an internal platform and are being catalogued and made accessible via the BioImage Informatics Index webtool 5  , part of the recommended interoperability resources of ELIXIR 6  developed by NEUBIAS and whose content is maintained by the community as a crowd-sourced platform. These materials were openly shared among all participants during the TSs, who have reported to use them in their research and in local training efforts afterwards. Anyone interested in accessing and re-using these materials is encouraged to search in the Biii webtool, where NEUBIAS will keep the catalogue centralized and the most up-to-date links to the final repositories of these materials (see Data availability).
There was an effort to try to create a consistent format and style for the presentation of the materials, including the slide formats, whenever possible. The organizers also experimented with video recording of some training sessions, when it was possible to fulfil GDPR compliance. Learning from this effort, "NEUBIAS Academy" was created in 2020 to offer recorded, yet compact, training modules for the community (more below). Some of the TSs content was further refined and prepared in the form of book chapters included in the recently edited Bioimage Data Analysis Workflows books, with the first edition 7  freely available since 2019, and a second edition expected in 2021, while further publications based on the TSs training materials are envisioned to appear in the NEUBIAS F1000Research Gateway 8.
Community building, follow-up and future perspectives The NEUBIAS TSs, beyond the primary take-home training for selected participants, were highly regarded as a venue for getting to know "who is who" and what is "state-of-the-art" in BIA. They were also strategically organized, when possible, together with the NEUBIAS Symposia that most TSs trainees attended, and where they were further exposed to the latest innovations in techniques and networking opportunities. Overall, the level of trainer support during TSs was highly praised and quite unusual for this type of training, even though some students commented that the pace was at times fast or overwhelming, owing to the diversity of topics addressed (Table  2 ). The organizers tried to balance the importance of exposing trainees to multiple tools and techniques, and of demonstrating how to explore the many existing software frameworks to tackle BIA problems and construct workflows.
This is arguably another key aspect of NEUBIAS TSs, in contrast to other training initiatives with BIA modules but more oriented to specific biology-driven applications (e. g. 3D developmental imaging courses 9 ) or imaging-technology (e. g. Lightsheet EMBO courses 10 ). It was often explained that there is no "single tool for all tasks", and that a well-prepared analyst should be ready to employ different tools. The recruitment of trainees to serve as trainers in ensuing TSs is a significant outcome and has led also to several local training efforts that followed using the concept, methods and materials of the NEUBIAS TSs. Several trainees have reported becoming professional staff members or analysts themselves, and on the strong impact the attendance of the TS had on their career decision and CV (see This promotes international cooperation, standardized methods and recognized levels of expertise, broader dissemination of training efforts, empowers local initiatives to contribute and benefit from the resources of a more global community, and will build on the spirit of a community-gathering that has become a hallmark, and highly praised, characteristic of NEUBIAS events.
Data availability NEUBIAS TS materials are catalogued and made accessible via the BioImage Informatics Index webtool (http://biii. eu), where NEUBIAS will keep the catalog centralized and the most up-to-date links to the final repositories of the materials (Zenodo and GitHub, for those that are complete and ready to be shared publicly via a CC-BY 4. 0 Creative Commons License). The authors present an important aspect of the NEUBIAS, the European Network of Bioimage Analysts, the training schools. These schools were a central educational tool of NEUBIAS and were done as 15 events around biomaging analysis. During the four year term of the program, over 400 trainees attended, and involved over 100 invited organizers, trainers and speakers. The paper does a nice job of outlining the rationale behind the innovative three-level program of the schools, the curriculum, the trainer recruitment and turnover strategy, the outcomes for the community and the career path of analysts, including some success stories.
Acknowledgements Besides the funding bodies, the organizers of the NEUBIAS training schools would like to thank all school organizers and trainers, and their host institutions for supporting them, including the Carpenter and the  Eliceiri Labs  for supporting trainers from the  US , and also all contributors of training materials and data -their names and contributions can be found online at the NEUBIAS website. We particularly acknowledge all in kind and financial contributions of hosting institutions, entities and local organizers of the eight TS events, as well as their administrative, technical and catering staff:  Universitat Pompeu Fabra in Barcelona ;  Fundação Calouste Gulbenkian in Lisbon and Instituto Gulbenkian de Ciência and Bioinformatics Training (Pedro Fernandes ) in Oeiras;  Center for Cellular Imaging and Sahlgrenska Academy at University of Gothenburg , The  University of Szeged ,  Institute of Informatics  and the  Biological Research Center of the Hungarian Academy of Sciences in Szeged; MRC Centre for Regenerative Medicine, Univ.
Administration, Resources, Software, Supervision, Writing -Original Draft Preparation, Grant information: NEUBIAS TSs were made possible through  COST (European Cooperation in Science and Technology) Action NEUBIAS  ( CA15124 ). Some TS were further supported by the  Swedish Foundation for Strategic Research  ( RIF14-0079 ), the  Swedish National Microscopy infrastructure  ( VR-RFI 2019-00217 ) and the  Luxembourg National Research Fund  (project  RESCOM/17/11762506 ). RH acknowledges support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy -EXC2068 -Cluster of Excellence Physics of Life of TU Dresden. FPC, PPG, JYT, ML acknowledge  France-BioImaging infrastructure supported by the French BioImaging national infrastructure  ( ANR-10-INBS-04 ).
Habitability is central to Mars science because developing planet-wide models for the evolution of surface and subsurface conditions governing habitable environments places specific constraints on the geological evolution of the Mars system. Habitability is inherently linked to fundamental planetary processes, including the dynamo, volcanism, impacts, hydroclimate, and the atmosphere. The importance of a continued focus on habitability is reflected in the MEPAG goals document, as habitability and aqueous environments remain one of the central themes for proposed investigations across all of the goals (1). Resolving these questions is also critical for understanding the factors that control habitability of terrestrial planets in our solar system and beyond. Additional data from Mars will enable us to move past the simple model of the Habitable Zone to a more robust understanding of planetary evolution and its influence on the habitability of Earthlike planets over time  (2).
Finally, finding good analogs is challenging, so we recommend that NASA support the development of a central repository for analog sites, data, and materials, such as the Terrestrial Analogs Data Portal in the Terrestrial Analogs Program at USGS. Current missions. Current and upcoming missions will make important progress in the areas above, but only if those missions are adequately funded. We strongly recommend that the extended mission for MSL be funded as recommended in its external mission review. The extended mission is essential to explore the transition from wet to dry environments in Mt. Sharp -an exceptional environmental transformation preserved in an accessible sedimentary rock record. Recent budgets have threatened the continuance of MSL and at best would severely cripple operations, which will waste a valuable landed asset designed to interrogate past habitability.
Visuotactile stimulation An Oculus CV1 HMD was used for stimulation. The software was designed using Unity 2018. 2. 8 for displaying a 235-degree prerecorded video portraying the first-person perspective of a real male or female sex person. The software ran on a computer (Nvidia Geforce GTX 1080 8 GB; 16 GB RAM; Intel Core i7, 3. 2 GHz) connected to the HMD. The videos were respectively filmed from the perspective of an actor and an actress, using a monoscopic Kodak SP360 4K camera at a resolution of 2,160 × 2,160 pixels at 30 frames per second. Another sex-matched person was recorded from a third person perspective interacting with them, this person synchronized their movements and actions to a previously recorded set of audio instructions to achieve consistent timing between videos. The same audio was later heard by the experimenter during the stimulation procedure to imitate those movements in synchrony with the video (see Figure  1 ).
The ball thrown at the belly was presented only once. Specific SCRs were measured in microSiemens (µS) and analyzed offline using Ledalab software (v3. 4. 9,  Benedek & Kaernbach, 2010). For each trial, SCRs were scored as the peak-to-peak amplitude difference in skin conductance of the response falling within -2 to 5 s of the trigger onset. The minimal response criterion was set as 0. 01 µS and only trials where there was a single response in the temporal window of interest were included. The raw SCR scores were square-root-transformed to normalize the distributions. Because the inclusion of zero responses (i. e. , using SCR magnitude) is subject to confounds between the response strength and the frequency  (Dawson et al. , 2016; Preuss & Ehrsson, 2019; Prokasy & Raskin, 1973)  and the large number of stimuli and repetitions in our setup, we here excluded zero responses from analysis (i.
Statistical analyses Statistical analyses were conducted in R version 3. 5. 1 (R Core Team, 2020) and inspected for normality through both visual inspection and the corresponding statistical tests. Two-tailed comparisons are reported. Data from the embodiment and odor perception questionnaires were analyzed with separate aligned rank transformation (ART) ANOVAs using the package ARTool  (Kay & Wobbrock, 2020)  for each item. We used ART ANOVAs rather than conventional ANOVAs because of the nonparametric nature of the questionnaire data and the robustness of ART ANOVAs for nonparametric analyses  (Wobbrock et al. , 2011). Statistically significant findings were followed up with multiple Bonferroni-corrected Wilcoxon signed-rank for the comparisons of interest. We report median and interquartile range (IQR) as descriptive statistics for nonparametric data, as well as partial eta-squared (η p 2 ) as an estimate of effect size together with the corresponding 90% confidence intervals (CI).
e. , the fourth component explained 0% of the random-effects variance). The final model was built as follows (in lme4 syntax): sqrtSCR ~ (congruency * odor. type) + (1 + congruency + odor. type|participant). We used the 'bobyqa' optimizer and set the number of model iterations to 200'000 to fit the model. Follow-up comparisons were computed with the emmeans  (Lenth, 2020)  package when appropriate and Bonferroni correction was applied to correct for multiple testing. We report Cohen's d for linear mixed-effects models (see  Brysbaert & Stevens, 2018; Westfall et al. , 2014)  as an estimate of effect size and their 95% CI.
Acknowledgements We want to thank  Jasmin Schilling  for her support with data collection and management, and  Irene Madoka Scheiwiller  for her help with piloting and preparation of the experiment. Anna Khazina ,  Kelly Gibbs  and  François-Rémy Ringeval  and  Tonino Rizzo  for their participation in the immersive videos. The authors also thank all the members of the  Human Perception and Bioresponses Department of the Research and Development Division of Firmenich ,  S. A. , for their invaluable advice and technical competence. This project was funded by the  University of Geneva-University of Zurich  Joint Seed Money Funding 2018. M. R. L. and B. L. were funded by the  Swiss National Science Foundation  ( PP00P1_170511 ), and Y. S. was supported by an  Early Postdoc. Mobility fellowship  from the  Swiss National Science Foundation  ( P2GEP1_187911 ).
General calibration of the simulations More generally, the simulations are calibrated with observational constraints of galaxies, either by directly using the fits of these constraints (EAGLE), or by simple agreement with the constraints (Illustris, IllustrisTNG). IllustrisTNG was not directly calibrated by eye with any new observational constraints, but instead the sub-grid models of the previous Illustris were adapted to better match the Illustris galaxy properties to observations used for the calibration of the Illustris simulation. The EAGLE simulation was calibrated to fits of observational data representing the galaxy mass function at z = 0, galaxy sizes at a function of galaxy mass at z = 0. 1, and the local MBH -M relation  (Schaye et al. 2015; Crain et al. 2015). The Illustris simulation was calibrated based on cosmic star formation rate density, galaxy mass function at z = 0, stellar to halo mass ratios at z = 0, as well the gas metallicity mass relation at z = 0, and the local MBH -M relation  (Vogelsberger et al.
ACKNOWLEDGMENT We thank the anonymous referee, Volker Springel, Joop Schaye, and Rainer Weinberger for useful comments on the paper, and Dalya Baron for sharing her recent observational sample. We acknowledge the support from the The Flatiron Institute, which is supported by the Simons Foundation. YRG acknowledges the support of the European Research Council through grant number ERCStG/716151, and RB the support of the Science and Technology Facilities Council [ST/P000541/1]. DAA was supported in part by NSF grant AST-2009687.
Simulation Methodology The simulation architecture embraces three different software tools: Zemax (now OpticStudio), MATLAB, and MAO (MAOR Adaptive Optics code). Although the former two are licensed software, the latter is a numerical simulation tool developed in an IDL environment for the AO modeling. 22 The workflow followed by the simulation is reported in Fig. 6  and listed as follows. 1. Generate MSF patterns (raster, periodic, and epicyclic) with MATLAB. All of the steps involving Zemax and MATLAB are developed in a MATLAB environment using the ZOS-API functions that allow the direct communication and data/command exchange from MATLAB to Zemax. MAO is an IDL-based numerical tool developed specifically to support the simulation of the MAORY AO loop correction. In this analysis, we avoid introducing the atmospheric turbulence that is otherwise a common component of a simulation.