dataesr/scientific-paragraphs-categorization huggingface dataset:
Physical data model (PDM) The comprehensive PDM and the data dictionary are respectively provided as Appendices A and B. Though the overall number of tables exceeds one hundred, the use of the database is rather simple (see Fig. 1 ) with six "core tables" hierarchically joined. Most other tables are reference tables connecting primary keys to labels and associated features. The complexity of the PDM is a direct consequence of the richness of the data stored with diverse sampling protocols. In summary, these core tables contain the following information: -"station": sampling site (name, location -"point_prelevement": sampling point (name, location). Each fish data is related to a georeferenced sampling point. Each "point_prelevement" is not necessarily related to a "station" (sampling site), so, the sampling point is the scale to consider for most of the spatially non-aggregated analyses.
-"operation": sampling survey (date, protocol, objectives, funder, field coordinator). Each survey is related to a point and has a date. -"prelevement_elementaire": elementary sample. Depending on the protocol, the elementary sample pools together the catches of a pass, of a set of spots, or of a stratified sampling (see details in Sect. 3. 1). -"lot_poissons": fish batch (species, length extrema, batch weight, type of measured length, e. g. fork length). -"mesure_individuelle": individual measurements  (length, weight). This table alone represents over 50% of the whole database volume.
Content Summary statistics of the database are provided in Table  1. A total of 47,869 surveys were carried out between 1966 and 2021. They report 358,157 taxa occurrences on 14,486 sampling points (1084 of which surveyed at least 10 years) located in 6584 sampling sites (1073 of which surveyed at least 10 years). Hence, there is considerable potential for time-series analyses. Total or fork length was measured in the field from 8,668,520 individuals out of the 20,304,413 ones reported. The surveys are not spread evenly throughout France (Fig. 2 ). The variability in the number of surveys between hydrological units is related to unit size and river network density. For example, in southwestern France, the high-permeability sandy soils do not  allow sufficient runoff to feed a permanent hydrographic network.
Electrofishing protocols The field sampling protocols have evolved throughout years due to (1) shifts in the objectives pursued, (2) scientific improvement of sampling techniques, (3) attempts to standardise sampling at national and European scales to gain comparability and (4) sampling increase of large river systems (Fig. 3 ). Long-term network monitoring of fish assemblages requires standardised methods that provide comparable quality data. All the surveys reported here were carried out by electrofishing following standardised procedures. Two kinds of survey are reported in the database: (1) species-specific surveys (the "abundance indexes", see Fig. 3 ) that should not be used for the interpretation of other taxa (a taxon not reported does not indicate its absence); (2) multi-species inventories.
As required by the European Norm CEN-EN-14011 on sampling of fish with electricity, and to minimise the gaps between the threshold values proposed, the minimal length of sampling point was set after  Belliard et al. (2012) , (Table  2 ). The Table "operation_description_peche" documents the conditions of the surveys including the gear type and tuning, along with measures of water conductivity and temperature. Over 90% of the surveys were carried out with direct current. The "elementary sample" (table "prelevement_elementaire") is the finest grain of sound faunistic list entered in the database. Depending on the protocol implemented in the field, it pools together the catches from a set of fish batches and individuals according to different rules: (1) In complete single-pass sampling or in banks continuous sampling, the elementary sample is the whole catch for the survey.
Fish-based bioassessment The dataset encompasses two tables containing bioassessment information, "operation_ipr" and "operation_ipr_plus", respectively associated to fish-based indices of "river health" initially described in  Oberdorff et al. (2002)  and  Marzin et al. (2014). These indicators aggregate sets of complementary metrics that measure distances between observed calculated from the catches) and theoretical (i. e. expected in reference conditions) assemblage features (richness or abundance by functional groups). These two tables contain groups of variables describing, for each survey, habitat condition, metrics observed values, metrics theoretical values, as well as the value of the final index. In the ASPE database, the calculation of the two indices is triggered by a first-level validation of the survey and implemented via a web service.
Using the dataset 4. 1 Licence and ownership The dataset can be freely downloaded from a Zenodo repository https://10. 5281/zenodo. 6480315 and used, given that the present article is properly credited. The dataset is published under the Open Licence 2. 0 (https://www. data. gouv. fr/fr/pages/ legal/licences, SPDX identifier etalab-2. 0) and thereby compatible with any open license requiring at least crediting, including Creative Commons Attribution (CC-BY) and Open Data Commons Attribution (ODC-BY) by the Open Knowledge Foundation.
Formats The two open formats proposed for the dataset were chosen to keep the volume reasonable (around 200 Mo) and to address a wide array of users. The files' names include suffixes indicating the date and time when they were created. Office suites users can open the. csv files in a compressed archive named "aspe_ csv_yyyy_mm_dd_hh_mm_ss. zip". However, it should be acknowledged that disentangling the PDM (Appendix A) can be challenging even with the data dictionary (Appendix B). The Table "mesure_individuelle" of individual measurements, with around 20M rows, can not be opened in a spreadsheet. R users will go for the two RData files provided. The first file, named "mei_yyyy_mm_dd_hh_mm_ss. RData", contains the Table "mesure_individuelle". The second file, named "tables_sauf_-mei_yyyy_mm_dd_hh_mm_ss.
Pitfalls The complexity of the PDM gives the chance to a wide array of errors when joining the Tables to assemble datasets. Hence it is recommended to take great care in checking the output of the queries. This complexity can, to some extent, be overcome by using the {aspe} R package (see Sect. 4. 5). Among the potential pitfalls when using the database, we draw attention on the following peculiarities: -One survey can contribute to several networks. Hence, adding the "objective" to a set of "operation" leads to duplicate them. -When a data table includes individual measurements as well as the number of fishes in the batch (field "lop_effectif" of table "lot_poissons"), the latter should not be summed up because being only a replicate. -Depending on the networks, some sites can be sampled at different frequencies.
-The distinction between a sampling site (Table "station") and a sampling point (Table "point_prelevement") can be tricky (but see Sect. 2. 2). A sampling site can encompass several sampling points. For most uses, it is recommended to carry out analyses at the point scale. However, it can be useful to aggregate the points within sites, to maximise, for example, the length of time series when analysing temporal trends. -The taxonomic classification used here is inherited from the 80 s. Recent studies revealed that what was earlier thought of as a species could have to be split into several ones, e. g. in the genus Esox  (Denys et al. , 2014) , Phoxinus  (Denys et al. , 2020)  or  Barbatula (Gauliard et al. , 2019). Unfortunately, there is no correspondence included in the database with TAXREF, the French national taxonomic register  (Gargominy et al.
, 2021). However, the gateway Table "passerelle_taxo" of the {aspe} package allows joining the two nomenclatures. The species scientific names included are not all up-to-date nor standardised, so great care is advised for joining external data, such as those imported from Fishbase (www. fishbase. us). -The naming rules applied to the internal elements of the database (tables and fields) refer to French language, which may cause understanding problems to not French speaking users.
Coordinates reference systems (CRS) Several CRS appear in the database. All the sampling points, as well as most of the sampling sites, are georeferenced. Their longitude and latitude coordinates can be found in the "station" ("sta_coordonnees_x" and "sta_coor-donnees_y") and "point_prelevement" ("pop_coordonnees_x" and "pop_coordonnees_y"), respectively. Each pair of coordinates is associated to a CRS in fields "sta_typ_id" and "pop_typ_id". The correspondence between the CRS Id, its name (e. g. WGS 84) and its EPSG code (4326 for WGS84) is provided in the Table "ref_type_projection".
Supporting analytical tools Two R packages  (Wickham, 2015)  are developed to foster the analysis of the data. Though still not definitive, they allow easy exploitation of the database without having to memorise the PDM. These packages have been used extensively to produce the present manuscript. The first package ({aspe} package) provides a suite of tools for most of the common processing of the ASPE database including parsing a dump of the ASPE PostgreSQL database, calculation of abundances, densities, size distributions, along with graphical output and spatial processing. It is available at: https://github. com/ PascalIrz/aspe. This package includes three useful dataframes named "liste_rouge", "passerelle_taxo" and "traits_bio", respectively giving, for each taxa, (1) its global, European and national IUCN Red List status  (IUCN et al.
, 2019 ; see Table  3 ), (2) a taxonomic gateway joining scientific and common names with the ASPE, TAXREF  (Gargominy et al. , 2021)  and SANDRE Ids and (3) a few species life-history traits. The Github page of the package indicates several links to tutorials as well as explanations on the functions' naming rules. Assembling a custom 'dataframe' is assisted by a series of functions allowing joining the ASPE tables. The function "mef_creer_passerelle()" starts by joining the six "core" tables Ids, then other functions with the "mef_" prefix allow joining other Tables to add information. The second package ({aspeQual} package) provides quality control tools for the database. So far it is limited to checking the likelihood of individual measurements and is available at: https://github. com/PascalIrz/aspeQual.
ASPE for research The dataset is still scientifically under-exploited. All the records in ASPE being georeferenced, the spatial joining with complementary datasets offers promising multidisciplinary perspectives. For the crayfish data have hardly been explored despite a total 158, 381 individuals reported. Only 19% of them belong to native species. The most frequent exotic ones (Pacifastacus leniusculus and Faxonius limosus) have been introduced from North America. Considering autochtonous species, the stone crayfish Austropotamobius torrentium, assessed "Critically Endangered" by the French Red List (IUCN and MNHN, 2012) was never observed, which confirms this status. The "Endangered" noble crayfish Astacus astacus was very rarely observed but the white-clawed crayfish Austropotamobius pallipes (classified "Vulnerable") was comparatively more common.
The database also offers perspectives in the field of bioassessment. The fish-based indices that have been developed on the ancestor of ASPE  (Oberdorff et al. , 2002; Marzin et al. , 2014)  did the best with the data available at that time. However, in the recent years, the dataset improved in quality, with more protocol standardisation, as well as in quantity. We also, nowadays, have access to new data on habitat, e. g. temperature extrema  (Beaufort et al. , 2021)  and anthropogenic stressors (e. g. pollution monitoring). Therefore, renewing the development procedure with up-to-date data and methods could, most likely, allow substantial improvement of the existing bioassessment tools.
Riverine fish data in France tomorrow Data collection goes on. The aims of the surveys, the sampling protocols and the networks of sites are now settled. Hence, the perspective is that of longer time series of standardised surveys of sites representative of the diversity of rivers in France. Open access to the up-to-date data should be available through an API by the end of 2022. The ASPE database will also become more interoperable with the attribution of a Universally Unique Identifier (UUID) to each observation and to each survey from the very data entry. The data dictionary (Appendix B), with the PDM, can help the users find their ways among the tables. Translating the 720 entries of the data dictionary from French to English should now be a matter of high priority to simplify access for potential foreign scientific users.
6. 3 Taxonomic identification: the Achilles' heel? Due to animal welfare considerations, taxonomic identification is carried out quickly in the field, so that individuals can be released safe and sound. So, misidentification may occur, especially for small individuals or cryptic species. The electrofishing protocols implemented do not target larvae, however, juveniles are captured and can be tricky to identify. Furthermore, many fish species can hybridise. Hybrid individuals can be difficult to spot without molecular tools, e. g. Parachondrostoma toxostoma x Chondrostoma nasus  (Costedoat et al. , 2004)  or Esox lucius x E. aquitanicus (Jeanroy and  Denys, 2019). Eventually, the last decade saw an important taxonomic review with the description of cryptic species that have only partially been implemented in the ASPE database.
EEG recording and preprocessing Scalp electroencephalogram (EEG) recording started once the participant was installed in the cabin. It was continuously acquired until the end of the experiment. A 64-channel Biosemi Active-Two amplifier system was used, with Ag/AgCl electrodes disposed according to the 10-10 classification montage (BioSemi, The Netherlands) and sampled at 1024 Hz. Reference and ground were constituted by the active electrode CMS (Common Mode Sense) and the passive electrode DRL (Driven Right Leg), respectively. Electrode offset was set below ± 15 V for all electrodes. EEG analyses were run on Letswave 6 ( https://www. letswave. org/ ) implemented on Matlab 2017 (MathWorks, USA). Continuous individual datasets were first highpass filtered at 0. 1 Hz using a 4 th -order Butterworth filter, then resampled to 200 Hz.
0417 Hz. Next, we evaluated the number of harmonics to retain for having a thorough estimation of each brain response  ( Retter et al. , 2021 ). To consider an identical number of harmonics across experimental conditions, individual data were grand-averaged across odor contexts and visual categories, and channels were pooled together. Z -scores were computed on amplitude spectra as the difference between each frequency bin and the mean surrounding noise estimated from the 20 adjacent bins (10 on each side) excluding the most extreme (minimum and maximum) and immediately adjacent bins, divided by the standard deviation of the noise. Harmonics were considered until Z -scores ceased to be consecutively significant ( Z > 1. 64, p <. 05, one-tailed, signal > noise). For the category-selective response, harmonics were significant until the 14 th harmonic (i.
Acknowledgment This work was financially supported by the  "Conseil Régional Bourgogne Franche-Comté " ( PARI grant ), the  FEDER (European Funding for Regional Economic Development) , the  French  " Investissements d'Avenir " program , project  ISITE-BFC  (contract  ANR-15-IDEX-03 ; to JYB, KD and DR), and the  French National Research Agency  (contract  ANR-19-CE28-0009  to AL). The authors are grateful to  Renaud Brochard  and the  "Institut Universitaire de France "  for financial support in performing EEG, to  Andréa Prino  for her assistance in the pilot study, to body odor donors, and to every participant of the pilot and main studies.
Structure Definition Within the OE competences framework for HE educators  (Garcia-Holgado et al. , 2020) , eight HE educational challenges that can be addressed by using OEP were identified and then eight competence areas related to those OEP were defined. In the development of the course curriculum and content that underpinned Catch the Open!, eight modules were developed that are aligned to the OE competences framework. A module presents the main path of the learning process, based on the educational practices selected in the OpenGame Handbook of Successful Open Practices  (Garcia-Holgado et al. , 2020). In addition, eight units of learning (UoL) were also defined, to widen the knowledge available within the course curriculum and content about OE. Each module is linked to two UoL based on which competences align with which OEP (see Table  1 ).
Catch the Open! The Technological Design Catch the Open! is a web-based application, available at https:// opengame-project. eu/game/. It has been developed by using the PHP framework Laravel and a MySQL database for the backend; and HTML5 and CSS with the framework Angular for the frontend. Additionally, a system to easily present the information has been developed by using json files which contain educational and playable contents in several languages. Registration is needed to access the application, allowing being able to record several data about learners, as well as information about interaction with the application and learning achievements. Moreover, with the data recorded for each learner, several analyses can be performed, based on gender, age, teaching expertise, etc. Finally, a system to automatically present the application in the learner's native tongue is included.
… The confusing part is having actual objects mixed with abstract items called L1, L2… (tester 9). Since it was an incomplete version, all the comments were considered to re-design some elements, to include some help tips when needed and to fix some bugs which were found in this test phase. The quantitative evaluation for the phase 2 can be seen in Figure  13. The answers were processed in Excel and imported into SPSS Statistics 25 (License of the University of Salamanca) to conduct the statistics test. All the items are in positive, so they follow the same Likert scale. We applied Cronbach's alpha coefficient to check the internal consistency of each dimension. The coefficient for the sample is 0. 713, which is over the recommended value of 0. 7. Question Q7-9 are related to usability. Users exposed that they knew what to do in every moment: 27 out of 74 reported agree or completely agree with the statement in Q7 (36.
ACKNOWLEDGMENTS In the research and development process of this project, all the partners in the consortium contributed to its success. In addition to the authors of this paper, we would like to acknowledge the great work done by  Lucía García-Holgado  from  University of Salamanca (Spain) ,  Patricia Bonaudo ,  Ulf-Daniel Ehlers , and  Tina Monelyon  from  Baden-Württemberg Cooperative State University (Germany ), and  António Teixeira  and  Maria do Carmo Teixeira Pinto  from  Universidade Aberta (Portugal).
Results: We developed an online tool called AutoClassWeb, which provides an easy-to-use and simple web interface for Bayesian clustering with AutoClass. Input data are entered as TSV files and quality controlled. Results are provided in formats that ease further analyses with spreadsheet programs or with programming languages, such as Python or R. AutoClassWeb is implemented in Python and is published under the 3-Clauses BSD license. The source code is available at https:// github. com/ pierr epo/ autoc lassw eb along with a detailed documentation.
Introduction In biology, high-throughput technologies (notably in genomics and proteomics) enable identification and quantification of several thousands of genes or proteins in a single experiment. To analyze such a large amount of data, from one or more experiments, clustering algorithms are widely used unsupervised machine-learning methods to group genes or proteins with similar patterns. Bayesian clustering is such an algorithm and one of its implementation in the C programming language (AutoClass C) has been developed in 1996 at the Ames Research Center at NASA  [1, 2]. The idea behind Bayesian clustering and the AutoClass algorithm is to find a classification that fits the data with the highest probability. The AutoClass algorithm provides some additional and interesting features: it handles missing data and determines automatically the best number of clusters.
AutoClass C has been used in a wide variety of applications from clustering cells of the prefrontal cortex in rats and mice  [3]  to detecting body patterns in the common cuttlefish  [4]  (see also references  [5]  and  [6]  for a detailed list of applications). However, AutoClass C, originally developed by physicists, is not user-friendly: the program is solely accessible through the command line, only 32-bit binaries are available and results files are difficult to parse for subsequent analysis. More than 10 years ago, Achcar et al. published Auto-Class@IJM  [5] , a web interface for AutoClass C. This web service drastically simplified the use of AutoClass C and widen its adoption, especially in biology  [3, [7] [8] [9] [10]. Unfortunately, this tool is not maintained anymore, and its source code is not publicly available.
Implementation AutoClassWeb utilizes AutoClassWrapper  [6] , a Python wrapper for the AutoClass C program. This wrapper facilitates the preparation and quality control of data, runs the actual classification, and eventually, prepare results in file formats that allow further analysis. AutoClassWeb is written in Python  [11]  and uses the Flask library to build the web interface users interact with. For better reproducibility and sustainability, Auto-ClassWeb is packaged in a Docker image stored in the BioContainers  [12]  registry. The web service itself has been designed to be userfriendly and straightforward. There is no user authentication and by default, results are kept 30 days before being deleted. A comprehensive help page provides all the help and guidance the user might need. Using Docker technology, AutoClassWeb can be quickly deployed on a local machine or on a public web server.
Data submission The input data must be formatted as tab-separated values (TSV) files. The first line is a header containing the names of the columns which must be unique. The first column contains the names of the objects studied (i. e. protein or gene identifiers). Missing data is supported and should be coded with an empty value (i. e. nothing). AutoClass supports three categories of data: • real location: negative and positive values such as position, elevation, microarray log ratio. • real scalar: singly bounded real values, typically bounded below at zero (i. e. : length, weight, age). • discrete: qualitative data. For instance, color, phenotype, name. If the initial input dataset contains several data types (real scalar, real location, discrete), it is recommended to split the initial dataset into several datasets of homogeneous type and submit them in the input form (Figure  1  (A)).
Limitations AutoClassWeb provides a convenient online service to cluster results from high-throughput experiments such as RNA-seq or mass spectrometry based proteomics. However, we would like to point out that the processing time required to cluster data with AutoClass is proportional to the number of genes or proteins to be clustered. A parallel version of AutoClass C that potentially reduces the processing time has been published  [14]. Unfortunately, the source code is not available, and the project has been discontinued. Another limitation of AutoClassWeb requires users to split input data by type (real location, real scalar or discrete) with a special attention to real location and real scalar which may sometimes be confused.