Lucie Naert
email: lucie.naert@univ-ubs.frlsf.irisa.fr

Caroline Larboulette
email: caroline.larboulette@univ-ubs.frlsf.irisa.fr

Sylvie Gibet
email: sylvie.gibet@univ-ubs.frlsf.irisa.fr

LSF-ANIMAL: A Motion Capture Corpus in French Sign Language Designed for the Animation of Signing Avatars

Keywords: Corpus, Motion Capture, French Sign Language, Marker Set, Annotation, Perceptual Evaluation

Signing avatars allow deaf people to access information in their preferred language using an interactive visualization of the sign language spatio-temporal content. However, avatars are often procedurally animated, resulting in robotic and unnatural movements, which are therefore rejected by the community for which they are intended. To overcome this lack of authenticity, solutions in which the avatar is animated from motion capture data are promising. Yet, the initial data set drastically limits the range of signs that the avatar can produce. Therefore, it can be interesting to enrich the initial corpus with new content by editing the captured motions. For this purpose, we collected the LSF-ANIMAL corpus, a French Sign Language (LSF) corpus composed of captured isolated signs and full sentences that can be used both to study LSF features and to generate new signs and utterances. This paper presents the precise definition and content of this corpus, technical considerations relative to the motion capture process (including the marker set definition), the post-processing steps required to obtain data in a standard motion format and the annotation scheme used to label the data. The quality of the corpus with respect to intelligibility, accuracy and realism is perceptually evaluated by 41 participants including native LSF signers.

Introduction

Virtual characters, or avatars, are a promising solution to convey information in Sign Language (SL). Avatars make it possible to preserve the signer's anonymity and to gain in interactivity: the speed, appearance and 3D point of view of the signed sequence can easily be changed by the user. In addition, unlike videos, the signed production can potentially be edited to create new signed content. However, in order to be accepted by the Deaf, the avatars must be fully animated with natural, realistic and meaningful motions. Those motions can be (i) generated from hand-crafted keyframes [START_REF] Braffort | Démonstrations d'elsi, la signeuse virtuelle du limsi (French) [Demo of elsi[END_REF][START_REF] Mcdonald | An automated technique for realtime production of lifelike animations of american sign 9 language[END_REF], (ii) generated procedurally [START_REF] Kennaway | Experience with, and requirements for, a gesture description language for synthetic animation[END_REF][START_REF] Nunnari | Animating azee descriptions using off-the-shelf ik solvers[END_REF], or (iii) generated with datadriven techniques through concatenation of pre-captured motions [START_REF] Gibet | The signcom system for data-driven animation of interactive virtual signers: Methodology and evaluation[END_REF] or machine learning processes [START_REF] Brock | Deep jslc: A multimodal corpus collection for data-driven generation of japanese sign language expressions[END_REF]. In the first case, the quality of the produced animation can be very high but the process of manually creating the keyframes is fastidious and time-consuming. In the second case, the generated motions are often robotic and unrealistic but any sign can be produced as long as it can be described using a SL representation such as SigML [START_REF] Kennaway | Avatar-independent scripting for real-time gesture animation[END_REF], or Azee [START_REF] Filhol | Synthesising sign language from semantics, approaching" from the target and back[END_REF]. In the third case, the avatar's motions come from human data and are thus more natural and smooth but the variety of the signs that can be synthesized is limited by the initial motion capture (MoCap) data set. As we considered the acceptance of the avatar to be a prime issue, we chose to use pre-captured motions but, to overcome the limitations of this approach, we seek to enrich the initial corpus by generating new signs through the recombination of motion segments on the different body channels such as the hand configuration or the wrist orientation. The initial corpus must be defined precisely so that the signs and motions present in the corpus can be used to study the sign formation mechanisms and to generate new signs. Be-sides, the MoCap corpus must be fully annotated in order to be used both to analyze and to synthesize signs and utterances. Moreover, for the synthesis results to be correct and accepted by the Deaf, the initial signs and movements present in the MoCap data set must satisfy requirements in terms of precision and realism that can only be assessed by a qualitative evaluation of the data. This paper presents the LSF-ANIMAL corpus, a motion captured corpus in French Sign Language (LSF) focused on the manual aspects of SL. Section 2. reviews existing video and motion capture data sets for different sign languages. Section 3. describes the objectives and the content of the LSF-ANIMAL corpus. Section 4. presents the data acquisition process and the detailed marker set used. Section 5. details the post-processing and annotation steps. Finally, Section 6. presents the design and results of a perceptual evaluation of the quality of the data set.

Related Work

Sign languages are visual-gestural languages. Given the lack of exhaustive and widely accepted written representations of SL, only video cameras or motion capture technologies can provide sign languages recordings accurate enough to be used for analysis or synthesis. Video recordings constitute the most common source of data. The subjects are filmed from one or more points of view, data is stored using a standard video format and is annotated a posteriori following a pre-defined annotation template. Video corpora are often the base material for statistical studies to highlight a particular gestural phenomenon or to verify a given hypothesis on human motion. Various video databases have been designed by linguists in order to study a specific linguistic feature (e.g., directional verbs and signing space [START_REF] De Beuzeville | The use of space with indicating verbs in auslan: A corpus-based investigation[END_REF], coarticulation [START_REF] Ormel | Coarticulation of handshape in sign language of the netherlands: A corpus study[END_REF], iconicity and role playing in the LS-COLIN corpus [START_REF] Cuxac | Corpus ls-colin[END_REF], classifier predicates in narratives/stories [START_REF] Millet | Le jeu syntaxique des proformes et des espaces dans la cohésion narrative en lsf[END_REF]); to compare different SL and different dialects of the same SL (Schembri [START_REF] Schembri | Sociolinguistic variation in auslan (australian sign language): A research project in progress[END_REF][START_REF] Hanke | Detecting regional and age variation in a growing corpus of dgs[END_REF]; or simply to archive and preserve signed utterances [START_REF] Crasborn | The corpus ngt: an online corpus for professionals and laymen[END_REF]. Other SL resources have been designed to perform natural language processing [START_REF] Efthimiou | Gslc: Creation and annotation of a greek sign language corpus for hci[END_REF] or automatic recognition [START_REF] Camgöz | Bosphorussign: a turkish sign language recognition corpus in health and finance domains[END_REF][START_REF] Ebling | Smile swiss german sign language dataset[END_REF] tasks. Videos can be used for the study and analysis of SL but do not provide reusable data for SL synthesis. The cost of a video recording session is quite cheap as a single video camera commercially available may be sufficient. However, a video recording alone eliminates the third dimension of space. Pose estimators such as OpenPose [START_REF] Cao | OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields[END_REF] or additional cameras are needed to compute the depth information during the realization of the signs. Besides, video recordings rarely possess a spatial resolution and a frame rate high enough to allow a precise data segmentation and analysis. Motion Capture (MoCap) technologies offer a higher spatial and temporal resolution than 2D video cameras in exchange for the need of a greater technical expertise and a rigorous post-processing. Data resulting from a motion capture session can be used for SL analysis: precise quantitative motion descriptors can be computed from the 3D data to confirm or reject existing linguistic hypotheses or motion laws. As the 3D positions of the human skeleton joints can be inferred from the MoCap data, avatars can also be animated from the captured data which constitutes a major advantage compared to video data. So, while a vast majority of video corpora are designed for linguistic analyses and computer vision tasks, motion capture corpora purposes are evenly distributed between analysis (e.g., coarticulation analysis [START_REF] Ormel | Coarticulation of hand height in sign language of the netherlands is affected by contact type[END_REF] or kinematic analysis [START_REF] Benchiheub | Collecting and analysing a motion-capture corpus of french sign language[END_REF]) and data-driven synthesis (e.g., concatenative synthesis for French Sign Language [START_REF] Gibet | Building french sign language motion capture corpora for signing avatars[END_REF] or deep neural network approach for Japanese Sign Language [START_REF] Brock | Deep jslc: A multimodal corpus collection for data-driven generation of japanese sign language expressions[END_REF]). In American Sign Language, the CUNY corpus [START_REF] Le Naour | Cuny american sign language motion-capture corpus: first release[END_REF] is used to study linguistic mechanisms which are exploited to build a linguistic representation that can be used to animate an avatar. However, even though their number is steadily growing, MoCap databases for SL studies are still rare and a very small portion of them are made available. Besides, Mo-Cap databases are small compared to video databases: they rarely exceed one hour of data and contain the sign utterances of few different signers (often only one signer), while a video footage can last hundreds of hours and gather the data of various persons. As a consequence, each of the existing MoCap corpus has been designed for a specific purpose. In our case, we aim to synthesize realistic LSF signs and utterances by analyzing, editing and/or recombining the captured motion segments on the different manual channels. For example, we plan to create a sign A by extracting and possibly modifying a feature (e.g., the hand configuration) of a sign B (see example on Figure 1). We use the LSF-ANIMAL corpus to study some linguistic mechanisms, the motion kinematic features on the different manual channels and the synchronisation between the channels, and as raw material to create new signs and utterances.

Figure 1 -Two different LSF signs using the same motion but different configurations: snail with the 'Y' configuration (left) and slug with the 'U' configuration. The small offset between the two arms is due to the retargeting process and the presence of markers on the signer's arms.

Corpus Definition

Because sign languages do not contain a finite number of signs due to mechanisms such as classifier-predicates, and because capturing data is costly, covering all the signs in all the possible contexts is not a viable design solution. Defining a corpus specifically suited to the studied phenomenon and the objectives to be achieved is thus more relevant.

Objectives

The objective of our corpus is dual. On the one hand, it constitutes the material to be analyzed in order to highlight motion laws, invariants and LSF phenomena. In this case, the data can be considered as ground truth and is used to make observations and to evaluate our synthesis results. On the other hand, the data becomes the synthesis material. We aim to generate new, natural and realistic LSF utterances based on the observations of the ground truth, and editing of the captured motions. This analysis/synthesis complementarity is paramount for our research work and the corpus is designed to handle this duality. More precisely, we wish to study and synthesize three manual LSF parameters [START_REF] Stokoe | Sign language structure: An outline of the visual communication systems of the american deaf[END_REF] (sometimes referred to as "phonological elements" [START_REF] Johnston | Auslan corpus annotation guidelines[END_REF]): 1 -Hand configurations (HC) of LSF. Keeping our synthesis goals in mind, we aim to study two phenomena: the transition from one configuration to another and the synchronisation of the HC with respect to the other channels such as hand orientation and placement. The corpus must then incorporate these HC in various linguistic constructions: in signs containing a change in the manual configuration -e.g., the sign [SALON] (living room) begins with an 'O' and ends with a 'C' configuration -, as well as in full utterances in which the chosen HC appears in a natural and contextualized way. Moreover, for synthesis purposes, the isolated HC must be captured to serve as a basis to our synthesis system. 2 -Placement of the two hands in the signing space. The placement of the hands can designate both (i) the global area where the sign is produced which does not change during the sign production but which can change depending on grammatical inflections [START_REF] Millet | Grammaire descriptive de la langue des signes française: dynamiques iconiques et linguistique générale[END_REF][START_REF] Moody | La langue des signes, Tome 1 : Histoire et grammaire (French) [French Sign Language -First Volume: History and grammar[END_REF], and (ii), at a lexical level, the discrete area or the specific coordinates where the hand is positioned at a precise time. Both are interesting for our study. We need to capture instances of the same sign placed at different locations of space and signs in which the hands are not static and whose position vary. The capture of full utterances will naturally provide various placement features.

3 -Hand movement. Hand movements with different trajectories (straight line, circular, waves, etc.), and with zero or more repetitions must be incorporated into the database.

In addition, we wish to study coarticulation mechanisms in full utterances. In order to measure the impact of the sign N -1 and N + 1 on the sign N , it is necessary to record natural LSF utterances with various combinations of the same signs.

Finally, we want the corpus to be used to test automatic annotation algorithms with different levels of granularity (e.g., gloss, hand configuration, placement). For this purpose, the presence of various types of data streams, such as isolated HC, isolated signs and full sentences, is beneficial.

Content of the LSF-ANIMAL Corpus

To meet the requirements detailed in the previous section, the LSF-ANIMAL corpus contains four subsets (see Table 1).

The first subset constitutes a list of the most common hand configurations of LSF. 41 hand configurations have been chosen with care by comparing five sources of different nature: (i) a LSF teacher, (ii) the hand configurations annotated in the Sign3D Corpus [START_REF] Gibet | Interactive editing in french sign language dedicated to virtual signers: requirements and challenges[END_REF], (iii) the International Visual Theatre book which is a reference for LSF grammar and vocabulary (Moody, 1983a), (iv) the research book of [START_REF] Cuxac | La langue des signes française (LSF) : les voies de l'iconocité (French) [French Sign Language: the iconicity ways[END_REF] and (v) a textbook to learn LSF [START_REF] Amauger | Langue des signes Française -A1 (French) [French Sign Language -A1[END_REF]. This part contains all the letters used for fingerspelling in LSF1 which can be used to spell some words and names, and 22 isolated configurations. All those configurations were executed with both hands.

The second subset is composed of isolated signs. Three types of signs have been chosen to address three types of needs. (i) 11 signs containing a change of hand configuration within the sign (like the sign [WEEK-END] in LSF which begins with the 'W' and ends with the 'E' hand configurations). Those signs can be used as examples and ground truth to synthesize the passage from one configuration to another and to study the coarticulation of the hand configuration channel within a sign. (ii) 9 question words (where?, when?, what to do?, how?, how old?, what?, why?, who?, how much/many?). Those signs are crucial in LSF: in addition to their function as interrogative pronouns, they are used to explain the context of a situation in relative clauses. The sign where? can thus introduce the place of the action and the sign why? can mean "because" in an affirmative sentence. (iii) 47 names of animals (e.g., ostrich, duck, whale) and 25 animal descriptors (e.g., mammals, feather, blue). Each animal name was performed twice to ensure the presence of two almost identical signs in the resulting data. Having a choice between different instances of the same motion segment is beneficial to add realism in a synthesis context. Animal names present a great range of contextualized hand configurations. Those configurations are therefore performed in different locations in the signing space. Animal names and descriptors can also have recreational applications and can be used, in serious games, to teach the signs corresponding to animals to a French hearing or deaf population.

The third subset consists in 26 descriptions of animals in four categories (6 dogs, 5 cats, 11 birds, and 4 mammals with horns). The color, type of skin (fur, plumage, etc.), food preferences and habitat are described for each animal. Then, the animal is identified with a name (seagull, black dog, cow, etc.). This task was inspired by a LSF lesson for beginners. It provides a natural and authentic flow of LSF utterances. Transfers of person in which the signer impersonates the character that he/she is talking about [START_REF] Sallandre | Iconicity in sign language: A theoretical and methodological point of view[END_REF] are numerous in such descriptions as the signer will naturally imitate the animal he is describing. In addition, the resulting production contains various different hand configurations, placements and types of motion.

At last, the fourth subset focuses on three grammatical mechanisms of LSF (and of SL in general): size and shape specifiers, pointing gestures and classifier predicates. The particularity of those three mechanisms is that only one manual feature (resp. movement amplitude, hand placement and hand configuration/movement) changes to modify the meaning of the sign/utterance2 . This property is very interesting for the synthesis of new content by recombination of the body channels. 1 -Size and shape specifiers consist in using the standard sign of an object with a different amplitude of movement to accurately represent the size/shape of the object [START_REF] Sallandre | Iconicity in sign language: A theoretical and methodological point of view[END_REF][START_REF] Supalla | The classifier system in american sign language. Noun classes and categorization[END_REF]. A big bone, for example, will be signed by doing the sign [OS] (bone) with a larger amplitude than for a normal bone. It is very interesting as only one feature changes (the amplitude of the motion) when the same object is described with different sizes. We chose to capture some examples of size specifiers based on the text of a popular fairy tale, namely Goldilocks and the Three Bears in which a young girl finds herself interacting with various objects of three different sizes. 2 -Pointing gestures are paramount in sign languages as they can be used to designate the subject(s) of an action (You, This theater or This man over there) or to associate virtual objects to 3D locations in the signing space. Those objects can then be referred to using a pointing gesture on the 3D location. To capture various pointing gestures, we designed a task in which the signer had to point with his index to various places on its body and in the signing space.

Other types of pointing gestures exist, involving the gaze, the shoulder or torso movements but we limited this study to index pointing gestures following the definition of Blondel [START_REF] Blondel | Acquisition bilingue lsf-français: L'enfant qui grandit avec deux langues et dans deux modalités. Acquisition et interaction en langue étrangère[END_REF].3 3 -Classifier predicates consist in using a particular hand configuration to represent an object (e.g., a flat hand for a car) or a person (the index finger raised for a standing person, bent for a sitting person, etc.) and a movement of the hand to show the movement performed by the object/person. For example, two flat hands moving forward with one hand behind the other one will depict two cars driving in a row. Classifier predicates are used to describe a scene more vividly and with fewer signs. In our case, classifier predicates have two interesting features: they take full advantage of hand configurations and show a wide range of movement trajectories that can be reused in different contexts. We chose to record 18 utterances describing different situations involving vehicles and pedestrians classifiers (moving forward, crossing each other, etc.).

Acquisition of the data

The capture must follow a strict protocol to collect clean and usable data. Technical considerations, the signers' profile and the elicitation protocol are presented hereafter.

Technical considerations

In addition to the definition of the corpus, it is necessary to prepare the capture room and to define a marker set in accordance with the task.

Motion Capture Room

The capture of French Sign Language utterances can be performed in a limited space as the signer does not move during the linguistic production but it also brings important technical constraints [START_REF] Courty | Why is the Creation of a Virtual Signer Challenging Computer Animation ?[END_REF]): (i) the need to accurately capture gestures with small but meaningful variations (the finger motions particularly), (ii) the temporal dynamics (velocity, acceleration, jerk) must be preserved which requires a high sampling rate and (iii) the whole body is involved in sign language production: facial expressions, gaze, torso motions and manual characteristics must be captured simultaneously.

For the capture, we used a Qualisys environment composed of sixteen infrared cameras (eight OQUS 400 and eight OQUS 700) and one video camera. To preserve the dynamics of the language, the capture was performed at a sampling rate of 200Hz.

Marker Set

The choice of the position and size of the optical markers attached to the signer's body are very important to capture the linguistic production with precision. A trade-off must be made between the quality of the capture and the intrusiveness of the equipment. Markers with a large radius will be visualised more easily by the infrared cameras than markers with a smaller radius but will impede the signer's motion and will thus impact the quality of the resulting data. Bigger markers were placed on body locations which are not prone to collide with other body parts while smaller markers were attached to the fingers and the face. We used 123 optical markers in total for our capture (see Table 2). For the body (without considering the hand and facial markers), the locations described in [START_REF] Carreno | Marker-set specification for magician's motion capture database recording[END_REF] were chosen. It consists in putting two markers of large radius (12.7 mm) around each joint position (elbows, knees, wrists, ankles) and at other strategic places (sternum, back, feet, etc.) (see Figure 2, left). Facial markers are paramount to capture the facial expressions which are meaningful in LSF utterances but also, and most of all considering our objectives, to capture the areas where the hand touches the face in some signs. To capture subtle deformations and to interfere as little as possible with the signer, those facial markers have a small diameter (4 mm). Given that the hands and their movements are at the center of our study, only 16 markers have been placed on the face; they are a subset of the facial marker set of [START_REF] Reverdy | Optimal marker set for motion capture of dynamical facial expressions[END_REF] and form a coarse cartography of the face and its main elements (nose, forehead, mouth, cheek, chin) that serve to indicate the position of the hand with respect to the face. A more thorough study was performed to determine the location of the hand markers. In order to capture accurately the complexity of the hand motion, it is necessary to use numerous small-sized markers. The performance of reduced hand marker sets (down to six markers on the hand) to produce natural motions were compared in [START_REF] Hoyet | Sleight of hand: perception of finger motion from reduced marker sets[END_REF]. The authors of the article mainly sought to obtain a realistic motion for simple tasks. However, in addition to realism, sign languages require the avatar motions to be identical or very similar to the source motion. Besides, the location of the markers on the hand is very important to subsequently 4 reconstruct the hand skeleton from the data. The right part of Figure 2 shows a skeleton of the hand. A marker was placed on each of the MCP joints, the closer to the bone as possible, and two markers were put on each PIP joints. One marker was added on the extremity of the second and third phalanges. This way, every first and second phalanges are defined by isosceles triangles. It gives an indication of the finger width and direction and simplifies the recognition of phalanges in post-processing work (in particular during the labeling of unidentified trajectories of markers).

Signers and Elicitation

The four subsets of the corpus were captured on two deaf LSF instructors fluent in written French (called Signer 1 and Signer 2 in Table 1). The instructions for each task were displayed on a screen in front of the signers. Before each new task, the instructions were clarified in LSF by a member of the lab. The signers knew in advance the global content of the corpus but they discovered the precise tasks during the capture session. A trade-off was found between precisely controlling the corpus content and giving the signers enough freedom to have the most natural and realistic sign language production. To take this into account, three of the tasks were precisely controlled with instructions written explicitly while the signers were given some leeway in the third part in which he/she has to describe various animals. For this task, we needed the signers to be able to sign in the manner they see fit. We decided to give the signers an image of the animal to be described next to some words underlining the important information that the signers must provide in their description. No sentences were imposed. The signers could add as many details as they wanted.

The two capture sessions lasted 4h30 each. We obtained around 1h of raw data in total (≈ 30 min for each signer).

Post-processing

General Post-processing

We ensured that the markers were correctly identified by manually correcting the errors and labeling the markers that were not identified by the Qualisys software (Qualysis, 2019). On some frames, the occlusion of some optical markers led to the absence of motion data for those markers. The position of each occluded marker on each frame was reconstructed by interpolation following the work of (Le Naour et al., 2018), thus ensuring that the 123 markers were visible on each frame. MotionBuilder (Autodesk, 2018) was used to derive the position and orientation of the actual joints of a human skeleton from the positions of the markers. This lead to the creation of motion files in FBX, a standard motion format. To visualize and evaluate our data, we have then rigged a character to the skeleton defined in the motion files. However, for our work which consists in studying and editing the MoCap data, it is also important to annotate the data.

Annotation

The data annotation process consists in associating to each frame of the captured data one or more labels describing the movement performed during this frame. Annotation consists of (i) dividing a continuous stream of movements into smaller segments and (ii) labeling these segments. These tagged segments will then be retrieved to be studied or to animate an avatar. As the final animation of the avatar depends on these labels, it is essential to have a precise and shared definition of the movement segments and labels.

The content of the LSF-ANIMAL corpus was annotated on the ELAN software (MaxPlanckInstitute, 2017) after the post-processing. Our annotation followed a structural scheme adapted to data-based synthesis, and was achieved at different levels. The glosses/signs were annotated manually by one person with knowledge of LSF on different annotation channels (left hand, right hand and both hands). To remove the main biases of the manual annotation of glosses, it was automatically refined using motion features [START_REF] Naert | Coarticulation analysis for sign language synthesis[END_REF]. Moreover, given that the focus of our work is the study of the different manual parameters of sign languages, 12 other channels, corresponding to the configuration of each hand, the placement of the hands, the motion type and orientation were created. Hand configurations were automatically annotated using machine learning methods [START_REF] Naert | Per channel automatic annotation of sign language motion capture data[END_REF]. The placement channels were also automatically annotated by computing distances between the hands and the body/facial markers. To this day, the orientation and motion channels are only partially manually annotated.

Our corpus is therefore composed of two dependent data sets: the captured motions and the annotations. To manipulate motion segments, the annotation data set can be queried and the corresponding motions are retrieved from the Mo-Cap data set [START_REF] Gibet | The signcom system for data-driven animation of interactive virtual signers: Methodology and evaluation[END_REF].

Perceptual Evaluation of the Corpus

As the initial corpus serves as the core material for the analysis and synthesis of movements, the quality of the synthesis will depend on the quality of the corpus. It is thus necessary to assess whether the signs and motions of the corpus are accurate and realistic. We therefore evaluated the quality of the data present in the corpus using a perceptual evaluation on a subset of the corpus.

To this end, we formulated the following hypotheses : H 1 : The captured data is intelligible.

H 2 : The captured data is accurate.

H 3 : The captured motions are realistic. H 4 : No information is lost when post-processing the data.

Design of the Evaluation

To validate or reject our hypotheses, we created videos by varying independent parameters. We then randomly showed the videos to the participants and asked them to recognize the video content and grade the realism and accuracy on a 5-point Likert scale.

Evaluated videos

The LSF-ANIMAL corpus is a compound of isolated signs, signs in context and utterances. The physical descriptions of various animals constitute a large part of the corpus. It is difficult and irrelevant to segment these descriptions into discrete signs because, since they are not standard, they would not have a meaning without their context. We therefore decided to evaluate the corpus by presenting two types of sequences: (i) isolated standard signs (animal names) and (ii) whole utterances corresponding to animal descriptions. Participants were asked to find the meaning and to evaluate the accuracy and realism of the sequences. The isolated signs were chosen in order to test if small differences in the manual parameters of LSF were visible. For example, we chose the signs bird, goose and duck which are identical in movement and placement but different for the hand configuration. In a similar way, tiger and zebra are identical except for the hand placement (on the head for tiger and on the torso for zebra).

In addition, in order to be able to validate hypothesis H 4 , it was necessary to show different types of data representations at different stages of the processing. Three representations were selected: (A) the points in space linked by segments given by the Qualisys software after identification of the trajectories, (B) the skeleton with the position of the joints calculated by MotionBuilder, and (C) the avatar controlled by the skeleton (see Figure 3). We chose to evaluate 18 sequences : 9 isolated signs and 9 descriptions (see Table 3) multiplied by 3 types of representations (54 videos in total). To avoid overloading the participants, we separated the evaluation into 3 sub-evaluations of 18 videos. Each sub-evaluation was composed of two parts (Part 1: isolated signs, Part 2: descriptions) and each part contained 9 videos of 9 different sequences. No sequence was evaluated more than once by the same participant, not even with two different representations but they all saw the 3 representations. The videos could be played back as many times as the participants wanted. One additional video per part was used as a training session. 6.1.2. Questions Three questions per video were asked to the participants. A question testing the intelligibility of the sign or description (H 1 ), a question on accuracy (H 2 ) and a question on the naturalness of the realization (H 3 ). All questions and their possible answers were signed in LSF and the resulting videos were subtitled in French. At first, only the question testing intelligibility was visible.

In the case of a sign, the question asked was: "What sign was made?" The answer was in the form of a drop-down list of animal names containing about fifty animals including the correct answer, with, at the end, two additional lines: "I did not recognize the animal" and "The animal I recognized is not present in the list" (chance level of 2%). In the case of a description, the question asked was: "Which image best matches the description that has been made?". Nine answers were proposed: the correct image, 7 images close to the description but that did not match exactly the description + the answer "No image matches the description" (chance level of 11%). The suggestions were close enough so that the answer was not obvious. When the participant had validated his/her answer to this first question, the correct answer appears (e.g., "It was the sign DOG") and the following questions are made visible. The second question concerns the accuracy and precision of the sign or description: "Do you think that the sign/description was done correctly?". The third question concerns the naturalness of the movement: "Do you think that the sign/description in LSF is natural/realistic/spontaneous (does it seems to be the movement of a real person)?". In both cases, possible responses are presented on a Likert scale ranging from 1 (most negative) to 5 (most positive).

Results

We released the questionnaire online and collected the results from 41 participants, 12 men and 29 women with an average age of 39.46 years old (min = 19, max = 72). Among the participants, 22 were hearing people ("entendant"), 3 were hearing-impaired ("malentendant"), 2 had become deaf during his/her lifetime ("devenu sourd") and 14 were deaf since birth ("sourd de naissance"). In addition, the participants were asked to assess their level of French Sign Language (beginners: 13 participants, quite good: 6, good: 5, very good: 8 or natives: 9)4 .

Recognition Rate

The recognition rate with respect to the level of French Sign Language of the participants is shown in Figure 4. In a logical way, the very good and native signers achieve a better recognition rate for isolated signs than participants with a lower level of LSF. The significant difference between the recognition rate of isolated signs of the beginners and the natives (p-value of 6.65e -05 with the unilateral Mann-Whitney test) shows the non-triviality of the task. More generally, people with a good level and below have a better recognition rate for descriptions than for isolated signs. This can be due to several reasons: (i) a random response on the description part is more likely to be correct as there are fewer possible answers in the description part than in the isolated signs part, (ii) unlike animal names which are isolated standard signs, the descriptions of animals are contextualized and iconic sequences: even people not knowledgeable in LSF can have an idea of the animal described just with the impersonation of the signer (e.g., the behaviour of the signer when describing the dachshund's walk or the labradoodle's curly hairs), (iii) participants can learn signs as they watch the videos: the vocabulary to describe the type of fur, for example, is repeated on different descriptions while, in the case of signs, learning was useless because no two signs were identical.

Isolated signs and descriptions were correctly identified with an average recognition rate of 76.068% by the good, very good and native LSF signers. Figure 5 shows their recognition rate per sequence. We can see that 9 out of 18 sequences have a recognition rate higher than 80% even though we intentionally chose similar signs (e.g., bird, duck, eagle and goose).

Figure 5 -Recognition rate per sequence for the "good", "very good" and "native" LSF signers (22 participants).

For the isolated signs, goose has the lowest results. 23% of the good, very good and native LSF signers mistook goose for turkey which shows the same hand configuration at a slightly different location (on the mouth for the goose and the nose for the turkey). The participants who replied turkey had the avatar representation, the only representation with the head visible: the placement may be slightly off on the avatar.

For the descriptions part, persian cat and duckling were not associated with the correct picture in a majority of cases. For both of them, the picture that was chosen instead represented an animal with a color specified in the description (an orange cat instead of an orange-eyed persian cat and a brown duck instead of a brown duckling).

However, participants can make mistakes and it is therefore important to analyze the answers to the following questions concerning the accuracy and the realism of the signs.

Accuracy and Realism

We considered that only the good, very good and native LSF signers were legitimate to answer the accuracy and realism questions. So we exclusively took into account their answers in this section. Table 4 shows the mean accuracy and realism scores while Figure 6 details the answers of the participants.

Isolated Signs Descriptions Accuracy 3.545/5 (0.703) 3.701/5 (0.584) Realism 3.667/5 (0.398) 3.957/5 (0.425) With more than 3.5 out of 5, we considered the realism of the movement to be acceptable. The reconstruction of the movement thus provides realistic human motions.

As for the accuracy of the signs, some signs, such as horse, were not recognized, not because of a problem in our processing but due to the original movement. Indeed, horse is usually done with both hands but, in our database, it is done with only one hand which can explain the poor results. As its form is not the standard form of the horse sign, it will be discarded from the database. Apart from those signs, the median of the results in accuracy are high (equal or above 4) for a majority of sequences (12 out of 18 sequences).

Figure 6 -Accuracy and realism score per sequence (the median is the orange line, the mean is the dotted green line, the whiskers go to 1.5 multiplied by the interquartile range). For example, bird, eagle and duck have an accuracy of 5 according to half of the participants.

Except for some sequences that should be removed from the corpus, we consider that H 2 and H 3 are verified which means that the data can be used for synthesis work.

Impact of the Type of Representation

To verify the H 4 hypothesis, the accuracy and realism scores were grouped by type of representation (Qualisys, Skeleton or Avatar, see Figure 3) for participants with a LSF level greater than or equal to good. Each participant rated between 9 and 18 sequences depending on whether or not he/she did the second part (among the 22 participants with a level greater than or equal to good, 13 responded to the two parts). We therefore gathered 105 (22 × 3 + 13 × 3) realism ratings per representation (same for accuracy, see Figure 7). As the data do not follow a normal distribution, we used the Kruskal-Wallis test for non-parametric data to determine if the type of representation had an impact on the ratings. Whether for accuracy or realism, the results of the statistical test do not allow us to rule out the H 4 hypothesis (for accuracy: p-value = 0.10 and, for realism: p-value = 0.65). We performed unilateral Mann-Whitney tests for the accuracy results and obtained a result that was close to being significant between the skeleton and the avatar representation (p-value = 0.022).

Ideally, we could have benefited from a higher number of results from LSF experts but, as it stands, we can consider that the type of representation had no significant impact on the ratings. The quality of the data was preserved in terms of realism from the raw MoCap data (Qualisys representation) to the animated skinned avatar. For the accuracy, there might be a small loss between the skeleton and the avatar representations but the current results do not allow us to reject the H 4 hypothesis.

Comments

At the end of the questionnaire, we allowed participants to express their feelings in a free text space. Out of the 41 participants, 9 commented on the lack of facial expressions, 6 expressed their preference for the skinned avatar, justifying it by the presence of placement information of the hands with respect to the face and by the fact that the avatar had a human appearance, 3 said they preferred the skeleton for the precision of the gestures, 3 others preferred the Qualisys representation for the same reason and 3 said they were enthusiastic about the precision and fluidity of the gestures. One participant expressed his surprise in understanding the two avatars without heads.

Conclusion

We presented the LSF-ANIMAL corpus, a new MoCap corpus of French Sign Language for sign language analysis and synthesis applications. The captured data has been post-processed so that it can directly be used to animate virtual signers. This task is still on-going and, at this time, the corpus has not yet been made available to the community. The corpus is composed of four subsets, each of them containing various signs and grammatical mechanisms to meet different synthesis objectives. Some manual parameters including hand configuration, placement, movement and orientation were captured and annotated. Therefore, our corpus, that includes semantically meaningful data, can be used for concatenative synthesis but can also be enriched by combining the different motion segments present in the data set and/or by editing the motion signal in order to create new content not limited to animal names and descriptions. And, given the exhaustive annotation scheme, this corpus can be used in other applications including LSF analysis. Besides, except for some specific signs, the results of the perceptual evaluation show that the movements were considered accurate and realistic by the participants and that the post-processing of the MoCap data did not impact the quality of the data in a significant way.

In the future, we hope to assess the precision of the motion capture process with respect to the manual parameters of LSF by taking advantage of the similarities between some animals (for example bird, duck and goose, whose only difference is the number of fingers used in the hand configurations to express the beak of the animals).

The perceptual evaluation of the corpus presented in this paper will serve as a baseline for future evaluations of the synthesis work.

Figure 2 -

 2 Figure 2 -Left: body marker set. The red markers are used for the definition of the segments and for the tracking whereas green markers are only used for tracking. Pink markers are situated on the back. Right: skeleton and marker set of the right hand.

Figure 3 -

 3 Figure 3 -The different combinations of sequences and types of representations.

Figure 4 -

 4 Figure 4 -Recognition rate of isolated signs (blue bars) and descriptions (hatched bars) with respect to the level of LSF of the participants (the number of people in each category is specified between parenthesis).

Figure 7 -

 7 Figure 7 -Number and nature of the accuracy ratings per type of representation for the good, very good and native LSF signers.

Table 1 -

 1 Content of the LSF-ANIMAL corpus.

	# Task name	Content	Purposes	Duration per signer
	1 Hand configu-	Fingerspelling alphabet + 22 iso-	Training set for automatic annotation and synthe-	Signer 1: 2 min
	rations	lated configurations	sis of the hand configurations.	Signer 2: 3 min 30
	2 Isolated Signs	11 signs with a change in the hand	Analysis of hand configuration transitions, pres-	Signer 1: 9 min
		configuration + 9 question words +	ence of words reusable in various contexts, recre-	Signer 2: 5 min
		47 animal names and 25 descriptors	ational/educational purposes.
	3 Continuous	26 descriptions of animals in 4 cat-	Study of transfers of person, of coarticulation and	Signer 1: 20 min
	signing	egories	of the impact of the manual parameters of LSF.	Signer 2: 9 min
	4 Grammatical	Size and shape specifiers + pointing	Recombination of the manual parameters of LSF	Signer 2: 10 min
	mechanisms	gestures + classifier predicates	for synthesis purposes.

  Marker set Nb of markers Marker diameter

	Head	16	12 × 4 mm 4 × 12.7 mm
	Hands	26 (×2)	19 × 4 mm 7 × 6.5 mm
	Body	55	12.7 mm
	Table 2 -Our marker set: number, diameter and location of our
	123 markers.

Table 3 -

 3 The 18 signs and described animals (plus the 2 training sequences).

Table 4 -

 4 Accuracy and realism average scores of the good, very good and native participants. The standard deviation in specified inside parenthesis with respect to the scores per sequence.

Not each of the

letters of the fingerspelling alphabet is a hand configuration. The 'N' letter, for example, possesses the same configuration as the 'U' letter (index and middle fingers in the up position while the other fingers are folded). However, the alphabet alone represents 19 different configurations.

The information conveyed by the face and gaze are also an important part of these structures but they are not the focus of this study.

Note that, with our synthesis engine, we are capable of associating any hand configuration to those pointing gestures.

Among the 14 deaf since birth, only 9 indicated a native level of LSF.

Acknowledgments

We want to thank M. Irdel and C. Gendreau-Touchais for their commitment in the capture and evaluation of our work.


Haley Flom

Maja Adamska

Raphaël Lami

Eve Gazave

Salvatore D'aniello

Bernd Schierwater

Agnès Boutet
email: agnes.boutet@sb-roscoff.fr

DigitalMarine: an online platform for blended learning in a marine experimental biology module, the Schmid Training Course

For over 20 years, the Schmid Training Course (STC) has offered unique opportunities for marine biology students from European universities to learn about marine model organisms. While the topics of the course have continuously changed over the years with the advent of new research techniques and discoveries, the pedagogical approach has remained largely the same -a combination of lectures, lab practicals, and field excursions. Several life science researchers, who have taught in the STC for many years, sought to bring the course's pedagogical approach into the 21st century, and with the support of Erasmus+ Programme of the European Community funding, the DigitalMarine project was developed. DigitalMarine began in 2018 as an international partnership between the six research centers from which the STC instructors hail, and its main objective was to introduce a flipped, blended approach to learning and teaching with respect to established and emerging marine biological model systems. The DigitalMarine platform, which covers 12 marine model organisms, is now publicly available.

The Schmid Training Course (STC)

The Station Biologique de Roscoff, a marine station of Sorbonne Université (SU), in France (Northern Brittany) was founded in 1872 by Henri de Lacaze-Duthiers with the aim to develop a laboratory for both observational and experimental zoology. Since then, research has extended into additional fields, including ecology and oceanography, and several training courses linked to these areas have been established. One of these courses is the Schmid Training Course (STC) that focuses on experimental biology using marine organisms. The STC was named after the Swiss zoologist Volker Schmid, who was an iconic teacher for the course, and who passed away in 2008 [1] .

The STC allows students to explore the diversity of marine model organisms and train them in using these models in experimental biology. More precisely, the goal is to show students the benefits of various marine organisms as alternative or complementary systems to the conventional model organisms (i.e. Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Saccharomyces cerevisiae) to address fundamental scientific questions.

Thus, the course covers regeneration and neurosciences as well as evolution, developmental biology and cell cycle regulation and all their interdependencies. This has been and continues to be a unique interdisciplinary scientific as well as a multidisciplinary pedagogical approach.

Historically, the STC has been defined by certain characteristics that differentiate it from other marine biology courses. One of these characteristics is the student experience of immersion in a marine station environment. Classes taught in marine stations benefit from the availability of marine model organisms on-site, and students are invited to collect, observe, and experiment on them, as if they were conducting their own research. In other words, the hands-on practical approach is a fundamental part of the STC experience. Another characteristic of the STC is the large quantity of time dedicated to lecturing, due to the significant amount of theoretical knowledge needed to fully benefit from learning the experimental approaches. The number of lectures means that students are confronted with an enormous amount of information in a relatively short period of time, which is not always evident. Additionally, another characteristic that has proved to be particularly challenging for students is the multidisciplinary aspect of the course, as it requires to integrate knowledge in morphology, physiology, genomics, marine biology, evolution, cell biology, and developmental biology. Due to their heterogeneous backgrounds, students frequently lack basic knowledge in one or more of these areas, which sets them up for a more difficult learning experience and necessitates extra efforts to attain all of the learning objectives.

Our team of instructors belonging to six research centers [Sorbonne Université (SU), Università del Salento, Université de Fribourg (UF), Stazione Zoologica Anton Dohrn (SZN), Stiftung Tieraerztliche Hochschule Hannover (TiHo), and the Australian National University (ANU)] has been very motivated to alleviate these challenges and to allow students to take full advantage of the intensive research course environment at the marine station. As such, we have chosen to experiment with flipping and blending the course to facilitate students' learning prior to the course, allowing them more time to work at the bench, work in groups, and design individual projects. Before we present the flipped and blended approaches that have been put into place, we will define and describe them in the following section.

Flipped and blended approaches

The terms "flipped classroom" and "blended learning" are sometimes confused or used interchangeably, which is incorrect. "Flipping" a classroom refers to changing the classic modality and roles of teaching and learning, that is to say a professor lecturing in class and students acquiring most of the knowledge here in a synchronous manner (Fig. 1a). In a flipped class, part of the learning content is acquired before the class (thus asynchronously) as a required preparation, and as such, more in-class time can be dedicated to higher-level and more active forms of learning (applying concepts, questioning, discussing, analyzing, and so on) in order to deepen the understanding of the subject matter [2] . As the students are given greater responsibility for their learning ahead of the course, their in-class experience becomes more active and they become agents of their own learning during the entire course. It should be noted that a flipped classroom by definition does not necessarily require the use of online materials for learning at home, which is where blended learning comes in. "Blended learning" refers to the use of both online and in-person spaces for the learning to occur. In contrast to a course in which learning supposedly happens only in the classroom, in a blended course students learn in-class as well as outside of the classroom using an online platform that hosts most of the learning material [3] [4] . A blended classroom is often flipped too, as online materials are generally being used by students before in-person, in-class interaction with teachers.

The interaction of these two pedagogical approaches means that the students can acquire knowledge before class at their own rhythm (asynchronously) through a variety of online learning material (videos, animations, readings, and so on) instead of learning all the course material through in-class lectures. This way students are able to see how well they have understood the material before coming to class, thanks to tools like online formative evaluations that provide feedback to students. For these reasons, setting up a flipped and blended approach is a judicious choice for the STC: the basic required knowledge for each scientific field can be assimilated by students at their own pace and before they arrive at the marine station. Once present at the station, much more time can be dedicated to the experimental work or to set up students' projects and interact with teachers. We thus developed the DigitalMarine platform to elaborate these relatively new learning approaches in the STC (Fig. 1b).

How does the DigitalMarine platform work?

Content of the platform. The platform consists of an open-access website (https://digital-marine.sorbonneuniversite.fr/index.php/digitalmarine) that hosts the majority of the learning material, as well as a private Moodle server that hosts more restricted-access material (research articles, evaluation materials). A total of 12 marine model systems (species and phyla) are presented: acoels, amphioxus, annelids, bacteria, brown algae, chondrichthyans, cnidarians, crustaceans, echinoderms, placozoans, sponges and tunicates (Fig. 2). Among the digital resources, the platform hosts lecture videos, 2D animations, interactive diagrams, marine organism collection and maintenance videos, experiment methodology videos, texts, interviews with guest researchers, links to their scientific communities and links to additional resources for students seeking to learn more about the different model organisms. These online resources constitute the starting point for flipped learning in the course, preparing students ahead of time for in-class activities. In addition to the pages dedicated to each of the 12 marine organisms, the platform has a front page with a presentation video of the platform and short clips of previous STC in Roscoff (SBR) and Napoli (SZN), a page explaining the link with the Erasmus+ programme, a page about the history of the STC, and a presentation page of the researchers involved in both online and in-person phases of the course. The Moodle server also serves as host for other learning resources, such as formative evaluations and provides means of asynchronous communication with the instructors and other students through discussion forums.

Use of the platform. Before the on-site course at the marine station, the platform is freely available for students to access the learning content and familiarize themselves at their own pace. This corresponds to a total amount of 8.30 hours of work for students to fully explore the content of the platform. Afterwards, students come to the marine station for the second phase of their learning. As they have acquired most of the fundamental knowledge related to the different marine organisms, more time can be devoted to hands-on lab practicals and discussions with their teacher. As the course relies on both online and in-class learning, we can say it now has a blended approach. Students are able to access the platform during the course as well, allowing them to re-access material, refresh their memory, review concepts, deepen their understanding, etc.

During the in-person session led by their teacher, students also have exercises where they have to "put themselves in the shoes of a researcher". This is prepared before the in-person class through the platform which gives students background information on the biology of the organisms as well as experimental approaches that are used for their study. Then, at the marine station, the students are given a biological question to investigate and using their prior knowledge, the students suggest several experiments to explore this question; in other words, they have to elaborate a short research project. This is an example of how the classroom is flipped: the knowledge acquisition is done at home and the application and deepening of understanding is done in classroom.

It is worth mentioning that knowledge acquired by the students during autonomous learning through the online platform is evaluated using various formative evaluations (quizzes, questionnaires, written evaluations) provided by teachers. These evaluations are done before the in-person phase of the course using the restricted-access Moodle server (only available to STC students and teachers) or at the beginning of the in-person session. These assessments are fundamental to verify student's learning comprehension in order for them to be able to fully participate in both research exercises and practical lab work during the in-person session.

In future editions of the STC, the Moodle will also serve as a means to share students' experimental projects (designs, results) with the group. After the course, the platform constitutes a reference that can be very useful for students during their research internships, should they need to go back to review material that is pertinent to their research field. Note that the DigitalMarine page of a specific marine organism can also be useful for all trainees hosted in the corresponding lab, not only for students doing the STC.

Feedback from students. Students in the STC were able to test a nearly-complete version of the platform in May 2021. Although the platform was not fully finished at the time, the feedback of the students was encouraging. As an example, regarding the question "Did the preparatory work seem coherent with the course objectives, and did you feel well-prepared for the in-person course?" 100% of the students answered positively. A similar score was showed for the question "Did the learning materials provided online (DigitalMarine website and Moodle pages) facilitate your learning during the in-person course?" These questions and other written and spoken feedback from the students indicate that the resources provided by the DigitalMarine platform were clearly beneficial to the students' learning experience.

Besides the advantages of these teaching and learning methods for the students, it is worth mentioning that this kind of immersive courses in marine stations or in research institutes constitutes a fantastic opportunity for students to start scientific networking and prospect for future internships or PhDs. Most of the researchers teaching at the STC have hosted a former STC student in their lab. This is also true for other iconic courses in Biology such as the Embryology course at MBL (Marine Biological Laboratory, Woods Hole -USA) or EMBO (European Molecular Biology Organization, Heidelberg -Germany) practical courses.

Why choose the theme of marine organisms in Life sciences for teaching experimentation?

Firstly, marine stations are in general excellent places to explore new teaching methods as they are environments where student-teacher interactions are easier and more intense, since research, teaching, and experimentation are much more closely connected than in a huge campus with thousands of students. Secondly, courses in marine stations give extra attention to practical lab work, thus any strategy designed to increase time dedicated to experiments is well received. Thirdly, research on marine organisms has contributed to some great advances in Life Sciences but students are not generally aware of the interests of marine models. For example, the green fluorescent protein (GFP, Chemistry Nobel Prize in 2008 [5] ) was isolated from a jellyfish and is now the most widely used expression reporter for molecular and cellular biology analysis. The pioneering study of the siphon withdrawal reflex in Aplysia [START_REF] Pinsker | Habituation and dishabituation of the gill-withdrawal reflex in Aplysia[END_REF] led to the 2000 Nobel Prize in Physiology and Medicine to Eric Kandel. The neural circuitry of this sea slug still serves as the major model of research aimed to decipher the cellular basis of behavior. Finally, the sea urchin model was crucial to discovering cyclins [START_REF] Evans | Cyclin: a protein specified by maternal mRNA in sea urchin eggs that is destroyed at each cleavage division[END_REF] , essential proteins regulating the cell cycle (Nobel Prize in Physiology and Medicine in 2001). These proteins are conserved in vertebrates and allowed to understand mechanisms responsible for tumor growth in humans, which is a prerequisite to develop therapeutic strategies against human malignancies. These examples illustrate how biotechnologies and biomedical applications rely on basic model system research and how important marine model organisms are.

Lastly, marine organisms are part of cutting-edge research such as managing live imaging datasets. Digitalization of datasets like those scored from quantitative analysis of ascidian embryogenesis strengthened the concept that morphogenesis relies on a contact area-dependent cell communication as much as on morphogen gradients [START_REF] Guignard | Contact areadependent cell communication and the morphological invariance of ascidian embryogenesis[END_REF] . In the field of genomics, marine species such as amphioxus have been targeted to examine not only genome sequences but also DNA structures to study regulation patterns that correlate with the transition from non-vertebrate to vertebrate chordates [START_REF] Marlétaz | Amphioxus functional genomics and the origins of vertebrate gene regulation[END_REF] . We have just seen the beginning of marine models entering the scientific stage to develop new biological concepts and ease high throughput analyses.

Conclusion

The STC continues to train future researchers in experimental biology on marine organisms at a multidisciplinary level. The DigitalMarine online platform for the STC has been designed to change the way of teaching and to develop a blended learning approach and this approach will evolve over the course of the different iterations of the STC in the years to come as the instructors experiment with different learning activities, experiments, evaluations, and so on. The use of the platform before coming to the course in marine station is a mean to free up instructional time for a greater emphasis on practical sessions and conceptual discussions with the instructors. This design also provides students with the possibility to learn more efficiently and test their understanding before coming to the in-person class and well before their final evaluation. Ultimately, this teaching and learning approach increases student empowerment and engagement with the course material and learning activities, better preparing them to use marine organisms in biological research in their professional futures.

Acknowledgements

The authors thank Hanna Hertwig, Vyara Mouget (SU), Giosuè Zurolo (SZN), Veronika Favre (UF), Kristin Fenske (TiHo) for administrative and logistical support during the DigitalMarine project, all the researchers who participate to the DigitalMarine platform set up and to the STC, people from the different support services in the marine stations of Roscoff (marine, teaching and aquarium falicities) of Banyuls (informatics department) and SZN (Filomena Caccavale, Giovanni Annona and Lorena Buono) without whom running the STC would not be possible. The authors declare no conflict of interest.

Author contributions

Haley Flom was the educational engineer for the DigitalMarine project in charge of module storyboarding for each marine organisms. Maja Adamska, Raphaël Lami, Eve Gazave, Salvatore D'Aniello, Bernd Schierwater and Agnès Boutet are part of the instructor team teaching in the STC. They defined the content of their respective modules present on the DigitalMarine platform. The complete list of the instructors is available on the platform. Haley Flom and Agnès Boutet wrote the first draft of this manuscript and all the authors gave significant feedback on it. Figure 1 has been designed by Haley Flom with the input of all the authors. Figure 2 has been designed by Eve Gazave. References [1] Gehring, W., Galliot, B., and Piraino, S. (2008). Volker Schmid (1939-2008). In memoriam. Int. J. Dev. Biol., 52, 1013-1014. [2] Flipped Learning Network (FLN). (2014). The Four Pillars of F-L-I-P™ (https://flippedlearning.org/ downloaded November 2021). [3] Osguthorpe, R. T., and Graham, C. R. (2003). Blended learning environments: Definitions and directions. Quarterly Review of Distance Education, 4, 227-233. [4] Bryan, A., and Volchenkova, K. (2016). Blended learning: Definition, models, implications for higher education. Bulletin of the South Ural State University, 8, 24-30. Their activity is a blend of knowledge acquisition through the online learning material, active note-taking, reading, and formative evaluations allowing them to assess their understanding of the material. Once at the marine station, some time is allotted for lecturing (building on the previously learned content), but a substantial amount of time is allotted to lab work, independent work, plus the same amount of time as before allotted to the field work excursion and summative evaluation. Photo and logo: courtesy of David Wahnoun.

Figure 1 .

 1 Figure 1. Schematic representation of the STC's learning design before and after the DigitalMarine project a) In the historical organization, no preparatory work is given. Students' days at the marine station are comprised of a more or less equal proportion of lectures and lab work, with a half-day fieldwork excursion and (cumulatively) two halfdays of evaluation at the end of the course. All revision of material learned during the day and preparation for the summative evaluations is done in the evening and is typically doubled during the second week. b) In the new organization, students are given preparatory work several weeks ahead of the course via the DigitalMarine platform.Their activity is a blend of knowledge acquisition through the online learning material, active note-taking, reading, and formative evaluations allowing them to assess their understanding of the material. Once at the marine station, some time is allotted for lecturing (building on the previously learned content), but a substantial amount of time is allotted to lab work, independent work, plus the same amount of time as before allotted to the field work excursion and summative evaluation. Photo and logo: courtesy of David Wahnoun.

Figure 2 :

 2 Figure 2: "Gallery of portraits" of marine model organisms developed during the course. a) Symsagitifera roscoffensis (Acoela), photo taken during the STC in Napoli by the students. b) Branchiostoma lanceolatum (Amphioxus; Cephalochordata) -focus on the anterior part. Photo taken during the STC in Napoli by the students. c) Platynereis dumerilii (Annelida) -Female, photo courtesy of E. Gazave, CNRS. d) Scanning Electron Microscopy (SEM) picture of Vibrio sp. (Bacteria), photo courtesy of L. Intertaglia, SU/OOB. e) Ectocarpus sp. (Brown algae, Phaeophyceae), photo courtesy of B. Charrier, CNRS. f) Scyliorhinus canicula (Chondrichthyes), photo courtesy of W. Thomas, CNRS/SU/SBR. g) Cassiopea sp. (Cnidaria), photo courtesy of M. Mammone & M. Bilan, University of Salento. h) SEM picture of Artemia franciscana (Crustacea) -male, photo courtesy of N. Rabet, SU/UMR BOREA. i) Sphaerechinus granularis (sea urchin, Echinodermata), photo courtesy of W. Thomas, CNRS/SU/SBR. j) Trichoplax adhaerens (Placozoa) -haplotype H1, photo courtesy of B. Schierwater, TiHo. k) Sycon capricorn (Porifera), photo courtesy of S. Newson, Crest diving. l) Phalusia mammillata (Tunicata), photo courtesy of S. Darras, CNRS.