dataesr/scientific-paragraphs-categorization huggingface dataset:
• Avoidance, slight decrease of the stance phase on the hip OA side (D2). • No disturbance, no marked asymmetry of the gait (D0) • Not done, in case of absence of video-recordings, unsolvable disagreement between the physicians (D4) Disagreement during the classification was attempted to be solved with a consensus meeting, resulting in one classification per patients. All video files are available from figshare (For HOA patients  22  and for HEA volunteers  23  ). Video was compressed with ffdshow codec and was recorded with avi extension. Such video file could be freely read with VideoLan software (https://www. videolan. org/). However, video files in which the patients or any other person were identifiable (recognizable face) are not made freely available. Data processing. Labelling of the marker trajectories was performed in the Vicon Nexus software (Nexus 2.
Joint kinematics were then computed following the Conventional Gait Model (also called Plug-In-Gait model)  20, 21  using the Vicon Nexus software (Nexus 2. 10, Vicon, UK). This approach first computes segment kinematics (Table  5 ) then joint kinematics (Table  1 ), as well as the position of the body center of mass (CoM) and ground reaction forces (GRF) normalized by the bodyweight (Table  2 ). However, we prefer to alert potential user about the calculation of angular values to other planes than sagittal. Indeed, the PluginGait Fig. 1  Reflective cutaneous markers placed by anatomical palpation on the participants. All markers have been illustrated for the left side (red markers), right side markers (green markers) and axial markers (blue markers). The anatomical description and full name of each marker are given in Table  4.
LWRB Left wrist marker B At the little finger side of a bar attached to a wristband on the posterior of the left wrist, as close to the wrist joint center as possible. Loose markers can be used but for better tracking of the axial rotations, a bar is recommended. -. Knee and ankle joint centres. The centres are calculated using a modified chord function from the global position of hip joint centres, the THI or TIB markers and the KNE or ANK markers. Centres are found such that the KNE or ANK marker is at the mid anthropometric measured distance from the center, in a direction perpendicular to the line from the hip joint center (for the knee) to knee joint centre or perpendicular to the line from the knee joint center (for the ankle) to ankle joint centre. Arff files dataset. Processed data (C3D) were then imported and concatenated into Matlab (R2016a, The MathWorks, USA) using the Biomechanics ToolKit 26 (Tables  1, 2 , 4, 5 report all exported data).
Each trial of each patient was cropped in gait cycles and resampled at 101 points. One file was finally generated for each marker trajectory, segment kinematics, joint kinematics and ground reaction force, containing all data for each gait cycle of each participant. Each file contained a header composed of T0-T100 percentage of the gait cycle, volunteer ID, side of the Osteoarthritis limb (only for patients), trial number and cycle number (numbered by trial) followed by all the parameter values: related timeframe (to keep a time frame for each recording) or variations along an axis (i. e. X, Y, Z) (Table  3 ). Data of asymptomatic participants, as well as data of M0 and M6 sessions of hip OA participants were exported in three different folders. Each of these folders was composed of folders for Markerset data, Joints angles data, Plugin Gait Bones data, CoM and normalized GRF data.
Data records C3D files. All data records are available from figshare (For Hip OA volunteers (HOA)  22  and for Healthy volunteers (HEA)  23  ). They are all stored in c3d file format (https://www. c3d. org). This file format is a public binary file format supported by all motion capture system manufacturers and biomechanics software programs. It is commonly used to store, for a single trial, synchronized 3D markers coordinates and analog data as well as a set of metadata (e. g. measurement units, custom parameters specific to the manufacturer software application). Trial files are referenced in our dataset in hierarchical folders VLT/ID/Mx/Trial Type/GaiTrialNum with: • VLT (Folder): defining either HEA or HOA In those files, all data were merged by subject and composed by trajectories data (see Tables  1, 2 , 4, 5), analog data (see Table  3 ) and metadata of the volunteer (identical to  19  ).
Weka files. All data records are available from figshare  27, 28. They are all stored in arff file format (https:// waikato. github. io/weka-wiki/downloading_weka/). This file format is a public text file format. Trial files are referenced in our dataset as VLT_Mx_DATA_A. arff,organized by folders related to angles, markers, plugin gait bones and CoM and GRF data, with: • VLT: defining either the Healthy (HEA) or the Hip OA volunteers (HOA) • Mx: the session (single one M0 for HEA), either M0 (prior the surgery) or M6 (after the surgery) for HOA • DATA: the kind of data: markers; plugin gait bones; angles; CoM and GRF, extracted from plugin gait data (see Tables  1, 2 , 4, 5 for details about the data) • A: the name of the axis, X, Y, Z or time.
Usage Notes C3D files. The recorded data are stored in c3d file format (https://www. c3d. org) and can easily be read using c3d toolboxes such as BTK (http://biomechanical-toolkit. github. io/)  26. The software Mokka is a convenient tool for 3D visualisation (http://biomechanical-toolkit. github. io/mokka/index. html). Anthropometric and demographic parameters of each participant are stored in the metadata of the related c3d files. Based on the markerset used in this study, joint kinematics and dynamics can be computed using the 3D Kinematics and Inverse Dynamics toolbox proposed by Dumas and freely available on the MathWorks File Exchange (https://nl. mathworks. com/matlabcentral/fileexchange/58021-3d-kinematics-and-inverse-dynamics). To compute de novo the joints angles from the trajectories data, the plugin gait toolbox for matlab is available the Vicon website in the Advanced Gait Workflow (https://www.
vicon. com/software/models-and-scripts/nexus-advanced-gait-workflow/?section=downloads. Weka Files. The data are stored in weka file format (arff files) and can easiliy be read using weka workbench which is freely available on the weka website (https://waikato. github. io/weka-wiki/downloading_weka/) or toolbox for matlab on the Matlab File Exchange (https://fr. mathworks. com/matlabcentral/fileexchange/21204-matlab-weka-interface). Anthropometric and demographic parameters of each participant are stored in the metadata of the related excel files on figshare.
Introduction The overall goal of this year's BioHackathon Europe project, initiated by the ELIXIR Plant Sciences Community, was to improve shared plant data standards in terms of their interoperability. Several technologies and data formats for plant science have already been created in the past. These include in particular the MIAPPE metadata standard (Minimal Information on Plant Phenotyping Experiments), which can define a dataset of phenotyping experiment in its completeness  (Papoutsoglou et al. , 2020). Some elements require extra attention to enable FAIR reusability and interoperability of MIAPPE datasets and particularly the way biological samples are described and identified. Not only is the exact taxonomic name recorded here, but also information about the lineage, passport data which include the variety identification as well as the sampling, the developmental stage and the anatomical entity for molecular assays.
MIAPPE is implemented in databases, web services such as Breeding API  (Selby et al. , 2019) ) and file sets commonly represented as tables. The MIAPPE training template demonstrate this, as well as a MIAPPE ISA-Tab implementation using the ISA framework (Investigation-Study-Assay), either as ISA-Tab or ISA JSON  (Rocca-Serra et al. , 2010). The Breeding API allows data retrieval and transfer programmatically via RESTful services either as whole datasets or by subsellecting relevant dataset parts. In order to make plant experimental datasets findable, websites in which such datasets are described may be annotated using Schema. org. While Schema. org does not provide domainspecific keywords, several projects aim to extend Schema. org. For the life sciences, Bioschemas  (Gray et al. , 2017)  is one such effort.
The ISA framework The open source ISA framework  (Rocca-Serra et al. , 2010) , for reporting experimental work in life sciences, helps plant scientists manage a diverse range of experiments using one or a combination of data modalities and technologies. The framework is centered around three entities: Investigation (the project or dataset), Study (the experimental setup) and Assay (the description of analytical measurements or transformation of data). The idea is that data models and serialisations (tabular, JSON and RDF) help experimentalists to provide a comprehensive description of the entire experimental metadata (biological material and sample characteristics, technology and protocols used, measurement types and data transformation, relationships between samples and data) so that the results and findings generated can be reproduced and reused by other scientists.
DataPLANT Data Management The DataPLANT consortium (https://www. nfdi4plants. de), which is part of the German NFDI (National Research Data Infrastructure), adopted the ISA model to describe various types of plant experiments and built a data structure called ARC (annotated research context). It relies on ISA for experimental metadata, CWL for computational metadata, and git & git-lfs for data storage. The approach allows version control, long term reproducible workflows for 'wet' and 'dry' labs and complete user access and provenance. To collect experimental metadata, DataPLANT has developed MS Excel-based tools, available as a plug-in called SWATE. The experimental metadata is conveniently stored in Excel spreadsheets (. xlsx) and can be marked up with ontology terms thanks to Excel widget performing terminology lookups over a community-driven ontology service.
This service allows to crowdsource ontology management and therefore helps to bridge the ontology gap. During the BioHackathon, the issue of documentating recommended ISA implementation patterns was also raised. The outcome of these discussions have been consolidated in a specific github issue. Of particular interest and importance was the agreement to refer to CWL workflows from an ISA archive through ISA protocol objects, therefore providing an elegant solution to the issue of interoperation between two data standards covering different areas of concerns.
ISA applied in Plant Phenotyping An existing ISA MIAPPE mapping and config had already been published  (Ćwiek-Kupczyńska et al. , 2016)  and improved  (Papoutsoglou et al. , 2020). While it proved its efficiency, harmonisation and documentation are still needed with sample datasets (DROPS and IPK Arabidopsis). Based on those examples, the BioHackathon discussions showed the need to better formalise data files and their relationships. Indeed, MIAPPE datasets can contain (i) the "raw" data with direct measures on the microplots or plants (i. e. 2a-GrainYield_components_Plot_level. tab in the DROPS example ), as well as (ii) the derived data computed from several files of the first level (i. e. BLUEs or genotypeindices) 2a-GrainYield_components_Plot_level. tab in the DROPS example ). Current ISA specifications only describe the process on how results were obtained and not the actual result values and does not describe the relationships between datafiles, i.
e. the provenance. The discussions led to the following proposals: 1. Provenance handling 1. CWLProv seems a promising way to easily express the relationship between files  (Khan et al. , 2019). A CWLProv file could be added to the ISA archive for this purpose and referenced as a datafile property. 2. Data handling 1. Keep results in a separate file linked in the assay table (git). Best practices for the external data file organisation should also be promoted (git). 2. Extend the assay tables to include measurement values. This should be avoided for larger number of results (git).
Packaging ISA with RO-Crate For simple and lightweight archiving and packaging, the scientific community has created the RO-Crate data standard  (Soiland-Reyes et al. , 2022). It is based on Schema. org markup annotations in JSON-LD and aims to make best practices for formal metadata description accessible and workable. In this context, an RO-Crate is a structured archive of all elements that have contributed to a research result, including their identifiers and origins. RO-Crate, therefore, is very generic and can easily receive any ontology of formalism to describe its content. Thus, it would be quite feasible to create a profile for the handling of ISA and MIAPPE. However, this would increase the maintenance workload for the community and the available tools need to be adapted to handle this profile.
At the same time, such RO-Crate ISA profiles are of great interest for other use cases, such as FAIRDOM-SEEK or Galaxy. As a consequence, the proposition is to clarify and improve the MIAPPE ISA implementation and to develop multiple RO-Crate-ISA profiles that wil allow to encapsulate MIAPPE datasets through ISA, hence bringing the expected interoperability. RO-Crates ISA (either ISA JSON, ISA-Tab or ISA. xlsx) should rely on four RO-Crate profiles with a flat and disentangled ISA structure. This avoids nested data structures, which is necessary for tools such as Galaxy. As a rule of thumb, the annotation scheme used to describe the dataset should be more general at the top of the hierarchy. For ISA, this means that the Investigation object should be described using very general terms, while the Study objects are described using less general terms and the Assay objects are described using specific terms.
Formalising the MIAPPE use case in Bioschemas Bioschemas is an extension of the Schema. org project to cover life science data. Similarly to Schema. org, it aims at providing with a lightweight data semantics, which might be easier to use in situations such as annotating web pages or automatically exporting and integrating data if the data is modelled with informal semantics that go beyond traditional OWL-based ontologies. During the BioHackathon, we continued the work started in 2021, first during the ELIXIR Implementation Study on Bioschemas and then during the BioHackathon by mapping the entities and fields in the MIAPPE specifications to Bioschemas types and defining extensions and modifications for the latter where necessary. This will allow in the future to support cases like converting ISA datasets about field trials into Bioschemas-based RDF serializations (formats like Turtle or JSON-LD).
BrAPI-to-Bioschemas has also been proposed and is facilitated by the fact that the BrAPI API specification for breeding trial data is already mapped to MIAPPE and ISA-Tab. The details of this work are reported on the Agri-Schemas repository. Agri-Schemas is a project with the similar goal of using (or extending) Bioschemas to cover a wide range of agri-food related data, including molecular biology, field trials, weather data, agro-forestry. Once these data are represented by a common data standard such as Bioschemas, it will be possible to support a use case such as: 1) Building a knowledge base from web pages annotated with Bioschemas, or 2) Using a SPARQL endpoint to answer queries such as: What are the genotypes that give high yields in field trials and what biological processes are the genes involved in? Preliminary datasets on this basis have been published by the Knetminer team by importing data from Ensembl Plants  (Bolser et al.
FAIR Cookbook recipe on describing MIAPPE datasets as ISA During the discussions, we realised that several challenges related to the mapping of MIAPPEcompliant phenomic datasets to the ISA model have often caused confusion in their implementation in the past. In addition, some of the previously described mappings need to be formalised and concretised to help data producers and data curators to ensure a consistent representation of the data. Therefore, we agreed to author a FAIR cookbook recipe explaining how to describe common plant phenotypic studies in a MIAPPE-compliant way as an ISA dataset  (Rocca-Serra et al. , 2022). This can probably be a subsection or a mature variant of an "ISA" recipe. The basis for this recipe could be the existing description from the https://github. com/MIAPPE/ ISA-Tab-for-plant-phenotyping repository.
Future developments beyond the BioHackathon Europe 2022 Both MIAPPE and ISA (and the combination of both standards) will be improved through revised documentation and demonstration of best practises, including more sample datasets. The two ELIXIR data management portals FAIR cookbook and RDMkit will be used for this purpose. A dedicated recipe will be published in the FAIR cookbook and referenced in the RDMkit plant phenomics tools assembly as well as on www. miappe. org. In parallel, the necessary extensions of ISA for data processing and provenance tracability will be discussed at the end of 2022 with the aim of implementation in mid-2023.
Building RO-Crate profiles The proposed RO-Crate profiles will need to be developed and implemented in close collaboration with the Galaxy use case. Information systems such as PHIS or PIPPA are able to generate MIAPPE compliant ISA datasets via BrAPI endpoints. The same is true for FAIRDOM-SEEK. Without these systems, it is also possible to create ISA data directly using the MIAPPE file templates or as an export from an ARC. For the import of such MIAPPE compliant ISA data into analysis workflow systems like Galaxy, different RO-Crate profiles are used to describe the data and its metadata. For the persistent storage of such MIAPPE compliant ISA data, various data repositories are maintained by the community, for example e!DAL-PGP or different Dataverse alternatives (data. inrae. fr, data.
Bioschemas The MIAPPE/Bioschemas mapping has been finalised during the BioHackathon. We will demonstrate it by implementing the ISA-Tab and/or BrAPI converters mentioned above, which we will use to enrich the existing AgriSchemas-based datasets and develop use cases for data search and analysis. An example of the latter is a student's project developed within the Knetminer team, which shows the potential of Bioschemas-based knowledge graphs to extend the approach of gene-set enrichement analysis.
(v) new functions that allow to build seasonally variable boundary conditions easily; (vi) use of postprocessing procedures with OpenFOAM ® stand-alone (i. e. without mandatory use of swak4foam as it was the case in RichardsFoam2  [2] ); (vii) several other minor rewritings/cleanings of the code. Nature of problem: This software solves the non-linear three-dimensional transient Richards equation, which is a very popular model for water transfer in variably saturated porous media (e. g. : soils). It is designed to take advantage of the massively parallel computing performance of OpenFOAM ®. The goal is to be able to model natural hydrosystems on large temporal and spatial scales. Solution method: A mixed implicit (FVM in the object oriented OpenFOAM framework) and explicit (FVC in the object oriented OpenFOAM ® framework) discretization of the equation with a backward time scheme is coupled with a linearization method (Picard algorithm).
Results and analysis We first considered the acquisition of the grammars by analyzing the percentage of "yes" responses (i. e. , same schema) for grammatical and ungrammatical stimuli at the group level and individually. Responses to grammatical and ungrammatical stimuli were compared using Wilcoxon signed-rank test. We analyzed the performance of the two groups separately, and of participants individually. Then, accuracy data were analyzed using a multilevel Bayesian modeling framework developed in Westphal-  Fitch et al. (2018). We assessed whether participants performed the task by effectively using the target grammar. Finally, we directly compared the performances of deaf and hearing participants. Data and code to perform the Bayesian multilevel analysis are available in the second author's GitHub repository (https://github.
Individual random intercepts were further used to assess patterns of individual variation unexplained by the average differences among hearing and deaf participants. All analyses were done using the brms package  (Bürkner, 2018) , which facilitates Bayesian modeling using the Stan statistical programming language  (Carpenter et al. , 2017)  in the R statistical environment  (R Core Team, 2013). Weakly regularizing priors-Normal(0, 2) for fixed effects, Half -Cauchy(0, 2) for random effects, and LKJ(2) for correlation coefficients-were set on all model parameters to reduce the risk of false positives and facilitate more robust inference given our relatively small sample size  (McElreath, 2020). Following recent suggestions for more informative reporting of statistical models  (McShane et al.
, 2015). Specifically, we generated 200 random datasets assuming a small effect size for the mean difference between groups (Cohen's d = 0. 3) on the transformed scale of our logistic regression model. Fifteen participant responses were simulated across 87 trials each for a single grammar, with modest between-individual residual variance (σ 2 = 0. 25) in mean probabilities. We then analyzed these datasets with Bayesian regression models, following the approach taken for our empirical analyses. Power was assessed by the proportion of simulated datasets for which the regression models were able to recover the simulated group difference with a posterior probability of >95% (or, in other words, where the 90% credible intervals for the slope estimate did not overlap with 0). Under these conditions, statistical power was desirably high (0.
Discussion We evaluated the performance of 15 deaf and 15 hearing adults in a visual artificial grammar learning task, assessing implicit learning of three different grammars ranging in computational complexity from a regular grammar to a mildly context-sensitive grammar. Learning was assessed through participants' ability to accept grammatical strings and to reject ungrammatical foils. Test stimuli included sequences of the same length as those presented in the exposure phase and sequences of a different length, thus testing for rule generalization. We assessed visual artificial grammar learning in deaf participants in order to provide new data relevant to the evaluation of the auditory scaffolding hypothesis, which states that the ability to represent sequential patterns requires developmental support by hearing, predicting that congenital deafness should hinder the development of sequential processing in deaf individuals  (Conway et al.
Acknowledgments We sincerely thank our participants for their patience. We also thank the Editor,  Ping Li , and the two anonymous reviewers for their valuable comments on the first version of the manuscript. WTF was supported by  ERC Advanced Grant "SOMACCA" and Austrian Science Fund (FWF) DK  Grant " Cognition & Communication 2 " (# W1262-B29 ). CC was supported by the  European Union 's  Horizon 2020 research and innovation program  ( SIGN-HUB project: Grant Agreement N 693349 ).