dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical Application As previously said, any image able to be converted in Bruker, DICOM, PAR/REC, or NIfTI format can be processed in MP3, no matter its nature. For instance, MP3 is used in a non-published study aimed to predict the evolution of brain injuries following a traumatic brain injury (TBI) using CT-scans acquired at the admission in the hospital, and at respectively 1 and 3 days after. A pre-processing pipeline for CT-scans, defined and described by  Muschelli et al. (2015)  was integrated as part of our own post-processing pipeline. This process resizes, clips, filters and extracts the brain from CT-scans. For our study, we added a module of coregistration (between the timepoints of a patient) from the SPM toolbox (Software, 2020) and a homemade module able to process a local entropy map, as well as some other texture images.
All those modules are available at https://github. com/nifm-gin/MP3_User_Modules_Repository. The pipeline applied to a patient and its three timepoints is described on Figure  4A. This patient is part of a cohort whose data acquisition was allowed by the French institution "Comité de protection des personnes" and respects the patients written inform consent obligation. The CT-scans and entropy maps, respectively inputs and outputs of the pipeline, are displayed on Figure  4B. One can see that entropy scans are co-registered and skulls were well-removed. Such pipeline may be easily applied to a large sample of patients in order to follow the evolution of the brain entropy, used as a potential prognostic biomarker of neurological outcome.
LIMITATIONS Despite all the work done to make it accessible and useful to the needs of the greatest number of people, MP3 does not yet offer all the performance that can be found in the best software in research fields such as physics or astronomy, which are more advanced on these aspects of interoperability and data reuse. MP3 does not meet all the FAIR (Findable, Accessible, Interoperable, Reusable) principles, which aim at improving the sharing of digital resources as described by  Wilkinson et al. (2016). Indeed, MP3 is not based on a real database that is easily adaptable, nor on a standardized data architecture. Since standards definition in the medical imaging field was only at its beginning when we started the development of MP3 we did not integrate them deeply in the software. However, we decided to make MP3 compatible with recently defined standards, such as the BIDS convention (through our import/export functions).
Another limitation concerns the execution of jobs on an external cluster that is not available today. Although this is conceptually possible, it would require additional development and testing. However, a multi-core execution is easy to use from a local server or computer. For example, we have tested MP3 on a project containing 450 subjects, 1,100 sessions for a total of 10,000 scans (3D, 4D, or even 5D). In this project, we successfully executed (on 32 CPU in parallel), onto the entire database a complex processing pipeline of 18 jobs per session which represented around 20,000 jobs.
CONCLUSION This article exposed a new open source software able to support an end-to-end research study on a large amount of data. Thanks to three graphical interfaces, MP3 offers to convert and visualize medical images and to interact with them by comparing or analyzing them. Based on a pipeline execution system called PSOM, MP3 also enables the creation, management, and execution of complex pipelines on heterogeneous cohorts described in databases, which handle time dependencies and multiparametric data. MP3 can be used either by end-used (nondevelopers) or by developers which can improve it or develop their own modules. MP3 has been tested on several studies, from preclinical to clinical, from MRI to CT data, and allowed us to process a preclinical cohort of more than 450 animals. MP3 can be downloaded on github: https://github.
com/nifm-gin/MP3 and modules developed by our lab (more than 60) are available on: https://github. com/nifm-gin/MP3_User_Modules_Repository. To conclude, we believe that, despite its limitations, MP3 makes it possible to facilitate large cohort analysis (preclinical/clinical) while improving the robustness and reproducibility of medical imaging studies. Indeed, one can think that in a near future every scientific publication will have to make both the raw data but also the processing pipeline used available and software such as MP3 will be able to facilitate this step.
REQUIREMENTS MP3 can be run on MATLAB 2017b and higher and needs the Image Processing Toolbox. In order to fully enjoy the software, we recommend installing the Statistics and Machine Learning Toolbox and the Parallel Computing Toolbox. Since the converter GUI is developed in Java, Java 8 is also required. Any of the three main Operating System (OS) can handle this software. MP3 is open source, open development and available on github (Software, 2019). To develop new modules, one needs to know MATLAB programming.
General description The Color Game app was a gaming app designed to investigate the creation of an artificial language on a large scale, while allowing large numbers of participants to interact synchronously as often as they choose and with a wide variety of partners. At its core is a standard referential communication task (sensu  Yule 2013). The task was piloted in the lab and used to generate results published in  (Müller, Winters, and Morin 2019 ) before we turned it into an online game. The task involves two players. The first player (the Sender) is presented with a "target colour", and his goal is to communicate with the second player (the Receiver) to help them pick the target colour in an array of four, thereby earning points. Senders communicate using black and white symbols that bear no straightforward association with any single hue of colour (Figs.
1  & 2 ). These symbols have been experimentally tested to make sure that they would be neither too easy (evoking too narrow a range of colours), nor too difficult (allowing no colour associations whatsoever). Laboratory experiments show that the Color Game symbols are as ambiguous as desired, since different pairs of participants can use them to solve the communication task above chance, but distinct pairs will associate the same symbol with different colours  (Müller, Winters, and Morin 2019). Independently of any communication task (in the lab or with the app), we have collected data on prior associations between the game's 35 symbols and its 32 colours, by asking 960 participants to freely associate one given symbol with a colour, or the reverse, in one-shot tasks. When confronted with the data generated by the app, this prior association data will allow us to observe and quantify the extent to which communicative conventions may strengthen or override a symbol's pre-existing meanings.
In the black bar on top, both players can see the identity of the Sender or Receiver in the language they chose, as well as two symbols, "!" and "?". Either player can tap of one of these symbols. This will highlight and enlarge the symbol for both players and produce a sound. In asynchronous mode, the set-up is similar, but the "!" and "?" symbols are not available. Unlike most language evolution experiments, our app does not provide players with trialby-trial feedback on the success or failure of communication. A block of 10 trials must be played by both Sender and Receiver for either of them to earn the corresponding points. After every block, the Receiver is told how many of the 10 they got correct, but not given specific information about which ones. Our reason to avoid trial-by-trial feedback is that it would let Receivers know instantly which symbol their Sender associates with which colour, allowing Receivers to learn a Sender's code by mere association.
Preregistration process The Color Game project and the six studies that constitute it were preregistered on the Open Science Framework on April 11 th , 2018. The full registration can be found here: https://osf. io/9pdzk. It includes the source code for the app, a plan for data collection along with criteria for including and excluding data points, and the specific predictions for each of the six projects. Since we did not want the app's users to be aware of our expectations, the hypotheses and methods for our six studies were not made public when the app was launched. They were listed on the Open Science Framework with nicknames that we also use for cross-referencing in the registration documents: FRIENDS, INFORMATION, LANGUAGE, PRIORS, SALIENCE, TREES. At the time the registration took place, we did not communicate with the public about our particular project, but did a presentation of the Color Game as a whole in  (Morin et al.
The projects For each project we provide a link to a public OSF repository, which contains -The project's preregistration (or preregistrations) (under "Registrations"); -A link to a preprint of the paper taken from the project, the preprint itself, or a report (except for the PRIORS project, still in progress); -The data and code needed to replicate the preregistered analyses. 2. 2. 1. FRIENDS (https://osf. io/y2vak/). (Authors:  Morin, Müller, Morisseau, Winters. )  This project tested the hypothesis that the precision of symbol-colour mappings would increase through time. Symbols' meanings were defined as the distribution of colours a given symbol was associated with for each Sender. We measured the precision of symbol meaning using entropy (more precisely, a circular measure of entropy specifically designed for this project by Winters).
However, we also found that this effect was crucially modulated by the position of the colour target inside the array (whether it was peripheral or central), something that our model did not predict. This project became a report, not currently submitted to a journal. The goal of this project was to investigate a possible impact of the colour categories present in the players' native language on the categorization of colours through symbol use within the Color Game. Using data from a separate survey on colour naming, we determined the semantic structure of three languages with respect to colours. Confirming our predictions, native and artificial language structure overlap at least moderately. Communicative behaviour and performance were influenced by the shared semantic structure, but only for English-speaking pairs.
)  This project tested the prediction that focal colours (focal meaning here, centrally associated with basic colour terms) are easier to communicate. The association of focal colours with basic colour terms in five languages was asserted with an online survey independent of the app. We predicted that focal colours would be easier to communicate, and that codes indicating focal colours would be more stable through time. These predictions were validated. This project became a paper that is currently submitted to a journal. 2. 2. 6. TREES (https://osf. io/r7n32/). (Authors:  Morin, Morisseau, Winters, Müller, Greenhill. )  The goal of this project was to simulate the evolution of distinct dialects inside the Color Game, and to retrace their emergence with a phylogenetic algorithm. A necessary first step in this project was to "grow" different dialects inside the Color Game.
The Color Game dataset repository The open Color Game dataset is available on the Open Science Framework (https://osf. io/9yc25/). This page contains all the information necessary to explore, use, and cite the data. We provide both the raw data as outputted by the app, and the cleaned-up dataset which we used for all the analyses involved in our six projects (the exact same dataset was used systematically). We provide the code that we used to get from the raw version to the cleaned-up one, and a comprehensive description of all the deletions we made, and why they were made.
Exclusion and inclusion criteria: preregistered rules We preregistered a number of rules to create, select, or exclude data points. They are copied below: "The data collection period runs for one year from the official launch of the game. After this period has passed, the data collected by the app will no longer be taken into account for the purpose of testing our original preregistered hypotheses. The app may no longer be maintained or advertised, although it will still be possible to download it for a period of time, and it will keep recording data. The following players and trials will be excluded from the data: -IP addresses that show cues of bot behaviour: for instance, if an IP plays an unnaturally high number of trials per day; or if a number of machines enter the game at the same hour from one single city, in a country known to host anomalous behaviour on the cyberspace, each machine playing one or a few trials.
Such incidents will be signalled in the diary. -Trials where Sender did not send any symbol will be excluded (this may happen in speed mode). -All players have the legal right to request the removal of their data from our dataset without giving a reason (see the document on data protection attached to the registration, also here: https://colorgame. net/downloadables/data-privacy-full. pdf). -Our technical report, finalised after the end of the one-year period, may uncover sources of corrupt data that warrant further exclusion measures. These exclusions will be decided on a technical basis and will apply to all six studies (regardless of what they predict). ".
Other datasets In addition to the main dataset, we constituted two other independent datasets to be used for specific projects (PRIORS, SALIENCY, and LANGUAGE). These tasks were carried out online with completely independent pools of subjects recruited through Prolific or SurveyMonkey. The PRIORS project used a task that asked participants to pair colours and symbols according to their intuitions, independently of any communicative interactions, to obtain data on prior associations between symbols and colours. The SALIENCY and LANGUAGE projects both made use of data from an online experiment whose participants had to name colours in their native language. These datasets are available on the page of their respective projects.
We have been investigating this issue with the developers since then, but the issue, which affects only a small minority of users, proves difficult to replicate on the developers' machines. On the 30th of August, this issue concerned 51 players. We are waiting for the developers to solve this issue. We decided we would not use this data for any analysis. [written 21/09] 05. 09. 2018: We completed our first analysis of the app's data, concerning the TREES project. We found that the proper conditions to carry on the project, split the two halves into sub-groups, and provoke the growth of distinct dialects, were not met. The project has been aborted. [written 21/09] 15. 09. 2018: We rolled out version 1. 10 of the app for iOS and Google Play. It includes Portuguese (Brazil), Japanese, and Russian language versions of the game.
Descriptive and exploratory analyses The accompanying R scripts for this section can be found on this page: https://osf. io/rywsd/. This document records simple exploratory analyses on players' performance and frequentation, which were not part of preregistered studies. They are presented in the form of a research diary. No preregistration was made, but analyses were recorded as exhaustively as possible. The analyses were carried out on preliminary versions of the dataset, not on the complete and cleaned-up Color Game dataset. The code we provide should nevertheless be sufficient to replicate our analyses approximately with the final version of the dataset. 22/06/2018. Players' performance in the Color Game through time. We looked at player performance to see whether it improved with time.
Looking at the whole dataset from 26/04 to 22/06, this means 4074 pairs, made up of 1412 distinct players. Fig. 9  shows the result. The full figure was made with plotly and has scroll-over features not accessible here (but see R code). It clearly shows that joint experience is correlated with a performance increase. This could be due to a tendency for players to keep playing with the partners they achieved high scores with. But this seems unlikely: the pairs with very high joint experience perform at extremely high levels (80% success rates being common) which pairs with less joint experience seldom reach. The experience of players outside the pairs, in contrast, does not seem to matter as much, even though the variance for this variable is much greater. This is shown in Fig. 10 , which plots performance against a pair of players' general experience, gained together or separately.
Most performance scores cluster around round values (0. 1, 0. 2, etc. ), because many pairs only play 10 or 20 trials together. The odd shape of the data in the 10K -20K trials range is probably due to the fact that a few highly experienced players engaged a lot of players with very little experience. This was also a good occasion to see what share of the game's activity could be attributed to what number of players, and identify possible problems with the data. We mapped the frequentation of the app: the number of players, number of trials, when they play and how often, how they perform, and their favourite time of day for playing. Players are asked to fill in a consent form allowing their player data, collected through the app and entirely anonymised, to be used by researchers for scientific purposes.
The data collected during the period of time where the app will be in use is to be made publicly available to other researchers in the future, after an embargo period. Any player wishing to have their data removed from the dataset may ask us to do so (at colorgame@shh. mpg. de) and their request will be immediately fulfilled. (Since the app's anonymised IDs do not allow us to identify individual players, the players will need to provide us with a way to identify them: for instance, the exact date and time of their first connection, or the exact date and time of their last five puzzles played. ) This study has been approved by the ethical committee attached to the president of the Max Planck Society (advice n° 2017_05). The advice is attached to this document, followed by our official request for authorisation.
Acknowledgements The Color Game app was designed by the  Minds and Traditions Research Group within the Department for Linguistic and Cultural Evolution of the Max Planck Institute for the Science of Human History in Jena , in collaboration with Etter studios (Zurich). The basic principle of the color game was an original idea of  Olivier Morin  and  Tiffany Morisseau  in 2015. The game was then developed and investigated experimentally by  Olivier Morin ,  Thomas Müller ,  James Winters ,  Lidiia Romanova  at the  Minds and Traditions Research Group of the Max Planck Institute for the Science of Human History. The blueprint for the app was conceived by  Olivier Morin ,  James Winters ,  Thomas Müller ,  Tiffany Morisseau ,  Lidiia Romanova , and  Christian Etter. We are grateful to  Helene Kreysa  and  Simon Greenhill  for their helpful comments.
The game's promotion is assured by the  Grafikerorg agency (Jena). We are grateful to  Aditya Kumar Lankapalli ,  Eirini Skourtanioti ,  Hiba Babiker ,  Cosimo Posth , and  Xintian Li , who acted in the advert. Inside the  Max Planck Institute for the Science of Human History , we are especially grateful for the help of  Lidiia Romanova ,  Lisa Jeschke ,  Anne Gibson ,  Petra Mader ,  Christian Nagel  &  Barbara Pavlek. The work leading to this paper has received funding from the " Frontiers in Cognition" EUR  grant,  ANR-17-EURE-0017 EUR.
Data Processing The sequence data was aligned to the reference human genome (GRCh37/hg19) using the Ion Torrent Mapping Alignment Program (ITMAP, ThermoFisher Scientific, Waltham, MA, USA). The Torrent Variant Caller (TVC) plugin was used for the genotyping of multi-allelic substitutions and indels (ThermoFisher Scientific, Waltham, MA, USA). Annotation of the variants was performed using ANNOVAR (http://www. wannovar. usc. edu/) and the sequence reads were visualized using the Integrated Genomic Viewer (IGV, http://www. broadinstitute. org/igv/).
Primer Design and Variant Confirmation Gene Runner (version 5. 0. 69 Beta, Hastings, NY, USA) software was used for primer design (NDST1; Forward primer: 5 -ACCAGAGAAAGGCAGAGACC-3 Reverse Primer: 5 -GTCACTGCACTTTGTCCCTG-3 , METTL23, Forward primer: 5 -TAGATTGGAGCTGGAGTGAG-3 and Reverse Primer: 5 -GGACCTTGGGATTCTTGTGC-3 ) and Sanger sequencing was performed using an ABI3730xl Automated Sequencer to verify the co-segregation of the identified variants with the disease phenotype (Thermo Fisher Scientific, Waltham, MA, USA). The Sanger sequencing results were examined and compared with Chromas Lite (http://technelysium. com. au/wp/) and Codon Code Aligner (https://www. codoncode. com/aligner/).
NDST1 (p. Asp656Asn) The crystal structure of the NDST1 protein was retrieved from the protein data bank (PDB ID: 1NST; 2. 3 Å resolution), while 3-O-sulfotransferase (3-OST-1, PDB ID: 3UAN; 1. 84 Å resolution) was used to model the dimerization effect of NDST1. 3-O-sulfotransferase shares high structural similarity with NDST1: the two domains of 3-O-sulfotransferase have 0. 761 and 0. 813 Å Root-Mean-Square Deviation (RMSD) from NDST1. The 1NST structure was superimposed to the two domains of 3UAN to model the dimer structure of NDST1.
METTL23 (p. Phe104Leu) We used the METTL21C protein (PDB ID: 4MTL, resolution 1. 6 Å), which shares a sequence identity of 39. 4% with METTL23, as a structure template to model the structure of METTL23. There are other human methyltransferase-like proteins, which have high sequence similarity to METTL23, including METTL21A (PDB ID: 4LEC, resolution 2. 3 Å), METTL21B (PDB ID: 4QPN, resolution 1. 3 Å), METTL21D (PDB ID: 4LG1, resolution 1. 8 Å). All these structures are highly similar in structure, sharing RMSD values within 0. 9 Å. This confirms that the structure modeling of METTL23 using these templates is highly reliable. SWISS-MODEL  [13]  was used to build the 3D model, while the 'mutagenesis' plugin in PyMOL (The PyMOL Molecular Graphics System, Version 2. 0 Schrödinger, LLC, New York, NY, USA) was used to model the single residue mutation.
Molecular Evaluation By the exome sequencing of the probands (IV-2 in family A and V-2 in family B), we identified two homozygous missense variants: one in NDST1 (c. 1966G>A; p. Asp656Asn) in family A (Figure  2B ) and one in METTL23 (c. 310T>C; p. Phe104Leu) in family B (Figure  1B ). In both families, in addition to the patients, both parents and all the non-affected siblings were Sanger sequenced for segregation analysis. The identified variants in NDST1 and METTL23 segregated with the disease phenotype in a recessive manner (Figures  1B  and 2B ) and none of them were present in 200 ethnically matched control individuals. The METTL23 variant (c. 310T>C; p. Phe104Leu) has not been reported in public databases whereas the NDST1 variant (c. 1966G>A; p. Asp656Asn) has a frequency of 0. 000016 in the GnomAD dataset, but the homozygous form of the variant was found to be absent.
Citations to his report are systematically missing in articles that present Secchi disk data or discuss Secchi disk visibility theory. It seems that his report has largely been forgotten. Here, I endeavor to delineate and understand Secchi's seminal work from a modern perspective. Considering his 1865 cruise report in the context of his full scientific career and the context of the time should provide a more complete understanding of his rationale and judgment. For this reason, a short biographical note is also included.