Brigitte Juanals
email: brigitte.juanals@univ-amu.fr

Jean-Luc Minel
email: jean-luc.minel@u-paris10.fr

Analysing Cultural Events on Twitter

Keywords: circulation of information, influence, instrumented methodology, social network, Twitter, European Night of Museums

In this paper, we first present a model to represent message flows and their contents on Twitter, then a model and an instrumented methodology to describe and analyze these flows and their distribution among the various stakeholders. The aim is to explore the engagement and interactions between different types of stakeholders. We apply our methodology and tools to the 12th edition of the cultural event "European Night of Museums" (NDM16).

Introduction

Since the 2000s social networks owned by American companies (Facebook, Twitter, Instagram, YouTube, etc.) have become very popular in the cultural field. As these social networks attract the general Internet audience, cultural institutions have incorporated into their communication strategies a strong presence in these digital spaces through the dissemination of contents (news, practical or cultural information, representations of works and associated information, etc.) and the development of interaction with their public. These evolutions go hand in hand with the development of cultural marketing. Lastly, the "eventualization" of culture, following the explosion of temporary exhibitions, has amplified a "shift of patrimonial institutions to the logic of working like streaming media". It is a fact that in France and in Europe, over the last decade, temporary cultural events have multiplied, in the form of public events or festivals -such as the "European Heritage Days", "European Night of Museums", "La Nuit de l'Archéologie", "Passeurs d'images". These events have now become recurrent, generating initiatives that contribute to cultural outreach in society as well as to the development of cultural tourism.

Our methodology is based on the analysis of these events and checked on the 12th edition of the cultural event 'European Night of Museums' (NDM16), which took place in the heritage institutions that were partners in the event and was extended to digital media on the website dedicated to the operation as well as on social networks (Twitter, Facebook, Instagram). The outline of this D R A F T paper is the following. First, in Section 2, we will present the specificities and the contribution of our approach and our methodology to collect and analyze flows of tweets. In Section 3, we describe the model designed then in Section 4 we discuss the results from quantitative and lexical analysis. Finally, we conclude in Section 5.

Related Work

Nowadays, museums and cultural institutions use social media as a means to communicate and promote their cultural activities, as well as to interact and engage with their visitors, the main use of social media by museums remaining information and promotion of activities such as exhibition openings or events [START_REF] Fletcher | Current social media uses and evaluations in american museums[END_REF]. A qualitative study on the ways that museums use Twitter in this perspective shows that they choose to link resources, engage the public with new social media tools and favor a two-way form of communication [START_REF] Osterman | Museums and twitter: An exploratory qualitative study of how museums use twitter for audience development and engagement[END_REF]. Villaespesa Cantalapiedra [START_REF] Villaespesa Cantalapiedra | Measuring Social Media Success: The value of the Balanced Scorecard as a tool for evaluation and strategic management in museum[END_REF] carried out fieldwork including a series of interviews with museum professionals which showed that the term 'engagement', 'can be interpreted in a variety of ways (. . . ): From fostering inspiration and creativity in the user, originating a change of behavior, increasing the user's knowledge, receiving interaction from the user in the form of a like or a comment,' [START_REF] Villaespesa Cantalapiedra | Measuring Social Media Success: The value of the Balanced Scorecard as a tool for evaluation and strategic management in museum[END_REF]. In particular, when Langa [START_REF] Langa | Does twitter help museums engage with visitors?[END_REF] studied the building of a relationship that forty-eight museums engaged on Twitter with online users, she showed that its primary use was as a marketing tool (public relations, events announcements, fact of the day, etc.) and that it led to a lesser engagement and a low audience participation. As mentioned in [START_REF] Foucault | Automatic classification of tweets for analyzing communication behavior museums[END_REF], tweet analysis has led to a large number of studies in many do-mains such as ideology prediction in Information Sciences [START_REF] Djemili | What does twitter have to say about ideology? In: NLP 4 CMC: Natural Language Processing for Computer-Mediated Communication[END_REF], natural disaster anticipation in Emergency [START_REF] Sakaki | Tweet analysis for real-time event detection and earthquake, reporting system development[END_REF] and tracking epidemic [START_REF] Missler | Tacking dengue epidmeics using twitter content classification and topic modelling[END_REF] while work in Social Sciences and Digital Humanities has developed tweet classifications [START_REF] Shiri | Twitter content categorisation: A public library perspective[END_REF]. However, few studies aim at classifying tweets according to communication classes. An exception worth mentioning is the work presented in Lovejoy and Saxton [START_REF] Lovejoy | Information, community, and action: How nonprofit organizations use social media[END_REF] in which the authors (Twitter users) analyze the global behavior of nonprofit organizations on Twitter based on three communication classes: Information, Community and Action classes. Recently, several studies on tweet classification have been carried out in NLP [START_REF] Kothari | Detecting comments on news articles in microblogs[END_REF][START_REF]2nd Workshop on Natural Language Pro-cessing for Social Media[END_REF] but to the best of our knowledge, only [START_REF] Courtin | A tool-based methodology to analyze social network interactions in cultural fields: The use case museumweek[END_REF] has classified cultural institutional tweets in communication categories based on NLP techniques.

Designing a Methodology

Designing a Model

So far, scientific studies on the forms of digital communication engaged in by cultural institutions and audiences have analyzed the practices of institutions and audiences as well as their uses of digital platforms. Our contribution aims to design a model for the circulation of message flows on a social network platform, taking the case of Twitter. We chose an inter-institution space that corresponds D R A F T both to a social network platform widely used by institutions and to a growing communication situation in the cultural field at the present time. This choice led us to focus on the category of cultural event programmed on a national scale. This enables us to explore how information circulates and is exchanged as well as the communication relations established between the different stakeholders present in an inter-institution space. Based on the analysis of message flows on the scale of the cultural event studied, we attempt to answer questions concerning the communicative practices of the stakeholders, such as for example, what is the current strategic usage of social media conducted by different types of stakeholders (not only museums or cultural institutions) during cultural events? We expect marketing and promotional messages to be present but we also inquire into initiatives fostering audience participation, providing cultural contents and favoring interaction with users. We will specifically examine the communication policies of cultural institutions during a cultural event on Twitter in order to assess whether they are part of their mission to democratize culture for a wide audience on this platform or whether it is rather a marketing campaign to promote a place or an event. A second question concerns the identification of passing accounts (see below). Finally, two periods of time were distinguished. A period before the event and a period during the event. This distinction is an empirical consequence of our studies on Museum Week 2014 et 2015 [START_REF] Courtin | A tool-based methodology to analyze social network interactions in cultural fields: The use case museumweek[END_REF].

Terminology and Attributes. Our analysis focuses on Twitter messages (called tweets) sent by accounts of cultural institutions and by other institutional or non-institutional stakeholders who participated in a cultural event. A first step was to build a terminology to describe the objects studied according to three dimensions: the message, the stakeholders, and the forms of stakeholder participation. Concerning messages, we will call a message sent by a twitter account an "original tweet" and an original message sent by an account different from the issuing account a "retweet". The current Twitter API gives access to the original tweet (and its sending account) of a retweet. The generic term tweet includes "original tweet" and "retweet". Regarding stakeholder qualification, we distinguished Twitter accounts, accounts managed by institutions (called "organizational account", OA), and accounts managed by individuals (called "private account", PA). This distinction is based both on the official list of museums in France provided by the French open data website, and the description on the Twitter account provided by the Twitter API. The analysis of the description field is necessary because non-museum institutions such as the City of Paris (@Paris) participated in the Night of Museums. Analysis of the flows during the MuseumWeek 2014 and 2015, European Night of Museums 2016, Europeans Days Heritage 2016 events, led us to identify six attributes used to qualify accounts according to their modes of participation and one computed score was associated at each attribute (cf. Table 1). We used the terms "participant", "producer", "relayed", "relaying", "mentioned" and "passing". The attribute "participant" was assigned to an account if it produced at least one original tweet or retweet during of of the the two temporal periods of the event (before and during the event). The attribute "producer" was assigned to an account if D R A F T it produced at least one original tweet. The attribute "relayed" was assigned to an account if at least one of its tweets was retweeted or quoted . The "relaying" attribute was assigned to an account if the account retweeted or quoted at least one tweet. The "mentioned" attribute was assigned to an account if its Twitter account name was mentioned at least once in a tweet. The attribute "passing" was assigned to an account if it was both "relayed" and "relaying". Our hypothesis is that an account with a high passing score is a key influential user who actively participates at the circulation ofinformation. Consequently, we computed for each account a passing score that is the product between the number of accounts that this account retweeted and the number of accounts that retweeted it. The value of this index is not significant in itself; it simply provides a means of comparing accounts. Note that these attributes were calculated irrespective of the number of followers. From these six attributes, it is possible , to compare the behavior of several accounts (see Section 4.2). Several quantitative analyses can be carried out. Quantitative analyses focus on the observation of flows and aim to identify accounts that contribute to the circulation of information during the cultural event. For each attribute (see above) a ranking of the accounts is computed, keeping only the first 10 or 15 accounts in this ranking. This make it possible to order the accounts that produce the largest number of original tweets, which are the most retweeted, and so on. In order to build a clasifier, a classification analysis of the contents of the messages was carried out in three stages. First, a team composed of two linguists and 10 community managers of cultural institutions designed a model, that is to say, determined the classes in which to categorize the tweets, and the features used to assign a tweet to a class. Four classes were identified (Encouraging participation, Interacting with the community, Promoting the event and informing about it, Sharing experience). The features selected were semio-linguistic (mostly lexical, but also including punctuation marks, emoticons), tweet-specific features (for example, the presence / absence of hashtags in tweets) as well as metadata such as the identity of the account. In the second stage, a classifier was built based on a corpus of 1000 tweets annotated by hand by cultural experts according to the categories defined in the previous step [START_REF] Courtin | A tool-based methodology to analyze social network interactions in cultural fields: The use case museumweek[END_REF]. The classifier is based on the Naive Bayes and SVM models, with unanimous vote. In a third stage, the classifier was applied to the corpus of tweets to categorize all the tweets. Results of the thor-D R A F T ough evaluation of the quality of the classifier carried out on the MuseumWeek 2015 campaign are detailed in [START_REF] Foucault | Automatic classification of tweets for analyzing communication behavior museums[END_REF]; the F-measure F 0.5 coefficient is 0.696.

Implementing the Methodology

We implemented our methodology by building a workflow (cf. Figure 1) based on the one hand, on open access tools like R3 (statistical), TXM4 (text mining), Gephi5 (graphs visualization), Neo4j6 (graphs mining), Scikit-learn7 (machine learning), and Sphynx (faceted search engine) 8 , and on the other hand, we developed some scripts python. All data are stored in a NoSql database 9 , and the scripts are used to query this database and compute specific attributes. The data acquisition stage consisted in harvesting tweets with only one hashtag, the official event hashtag #NDM16. We developed a script Python, based on Twarc (http://github.com/docnow/twarc) module proposed by Ed Summers using the streaming option of Twitter Application Programming Interface (API). In this paper, we limit the analysis to tweets in French sent during the week preceding the event and the day of the event, that is to say from 14 May to 21 May 2016 midnight. The main figures are the following: 11 264 tweets of which 3 301 original tweets (29%), 7 963 retweets (71%) sent by 4 012 participants. The specific figures are the following : more than half (56%) of organizational tion in this event was largely limited to the action of retweeting (75% of tweets) the messages sent by the institutional partners. The findings of the supervised classification of original tweets sent during the event are shown in table 2. It should be noted that some tweets have been categorized twice (that is why the sum of the total percentages is greater than 100%). This table shows that the organizational accounts tweets mainly (93%) serve to promote and inform about local events planned for the occasion. This result is consistent with the organizational framework of the cultural event, the success of which is partly linked to its attendance. There is also some interaction with the public but messages of engagement are very sparse. We applied the t-SNE algorithm [START_REF] Van Der Maaten | Visualizing high-dimensional data using t-sne[END_REF] to get a global cartography of the networks (fig 1 .). Several communities are well distinguished. One community (in green) composed mainly of organizational accounts, which is more productive and interactive, and others, composed entirely of private accounts (in pink) which produced few tweets (see Section 4.3).

Key Influential Users

Passing Accounts . We analysed the top ten passing accounts (see Section 3). A remarkable point is the non-correlation between their passing score and their number of followers. The Pearson-correlation computed on the these passing accounts (NuitdesMusées excluded) is equal to 0.15875, that is very low. Consequently, while the growth of followers of museums Twitter accounts is considered as a marketing target, the circulation of information during a cultural event is depending of key influential accounts which are not necessarily what is called 'big players' [START_REF] Espinos | Museums on social media : Analyzing growth through case studies[END_REF]. Some characteristics of these passing accounts must be pointed out. They are all organizational accounts with the exception of the account '@Mariette Escalier'. The owner of this private account is a professional in the field of cultural mediation. Ranked second after the organizational account of the event although she has less than 2 000 followers. In 5th place, the presence of the account of Alain Juppé, a French politician, mayor of the city of Bordeaux and former prime minister, shows that culture has become a political issue and plays an important role in marketing Cities and territories. Great diversity was observed in the communicative practices of museum accounts . Some museums relay exclusively organizational accounts, while others D R A F T relay only private accounts. We also noted that the passing accounts during the event are not the same as the passing accounts of the period preceding the event. Only the 'NuitdesMusées' and 'MinistèreCultureCom" accounts are common to both these periods (see [START_REF] Juanals | Information flow on digital social networks during a cultural event: Methodology and analysis of the european night of museums 2016 on twitter[END_REF] for more details). In order to compare the 15 most active accounts, we performed a principal component analysis with 4 variables for each account: number of original tweets, mentioned score, relayed score and passing score (see Section 2). We did not take into account the official account of the event (@NuitdesMusees) which to its official position, presents specific characteristics. This analysis shows the positions occupied by the 15 most active stakeholders in the 2016 NDM (Figure 1). Among them, the Musée d'Orsay is in a remarkable position, far on the right; it was mentioned and relayed many times because it organized an event (a jazz concert in the museum) which was very well attended. The success of this in situ event was relayed on Twitter which worked in this respect as a soundboard whereas the Louvre Museum, which organized conferences, was seldom mentioned or relayed despite its reputation. Similarly, the position of the private account Mariette Escalier (cultural mediator) occupies another remarkable position, in the left top corner; she is a passing account and she sent a lot of tweets, unlike most private accounts.

Integration Score. The aggregated communities around the passing account are represented in figure 2. This spatial representation realized by applying the Louvain algorithm [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF] makes possible to highlight several points. The private account of the politician Alain Juppé who sought to enhance his cultural program was largely relayed by the collective who supports him (figure 3), but Alain Juppé account (bottom right) is very isolated from the rest of the network. In order to support this, we calculated a score called integration score. This score is equal to the ratio between the cardinality of the set of accounts that have retweeted the passing account, among others, and the cardinality of the set of accounts that have only retweeted the passing account. Thus, if a collective of a passing account is totally isolated from the graph of all accounts, its integration score will be equal to zero; Conversely, if a passing account is well integrated , in other words, the members of this group retweets other accounts, its integration D R A F T Fig. 4. Main communities score will be equal to 1. For example, Alain Juppé integration score is equal to 0.36 while the score of Mariette Escalier is equal to 0.8. Other communities are grouped around museum accounts (on the top left) and are partially connected to the rest of the network. Nevertheless, it must pointed out that different communities did not interact. In other words, the Musée d'Orsay and the Center Pompidou, two Parisian museums share few common accounts, at least for the Night of Museums event. On the contrary, the community of the private account @Mariette Escalier (on the top right) is much more immersed in the network.

Conclusion

We designed and implemented a methodology to analyze forms of engagement, participation and relationship between cultural institutions, organizations and audiences. Checking this methodology on the cultural event "European Night of Museums" 2016, we first applied the t-SNE algorithm to get a global cartography of the networks. We performed a correspondence analysis that shows the specificity of one private account and the role of two museums in Paris, the Muséee d'Orsay and the Centre Pompidou, which played a major role in disseminating information. We intend to dig deeper into the instrumented methodology: more specifically, we are working on the extension of our method in order to take into account an incremental analysis of graphs. This conceptual work brings together research issues on the production and circulation of information with data mining and visualization software. Data mining is seen as an heuristic iterative and incremental process.

Fig. 1 .4

 1 Fig. 1. Workflow and tools

Fig. 2 .

 2 Fig. 2. t-SNE analysis

Fig. 3 .

 3 Fig. 3. Principal component analysis

Table 1 .

 1 Score calculation method

	Participant	0 (no participation in the considered period of time) or 1
	Producer	Total amount of tweets and retweets
	Relayed	Total number of accounts which relayed his/her original tweets
	Relaying	Total number of accounts she/he relayed
	Mentioned	Total number of mentions of the account in text tweets
	Passing	Product of relayed score by relaying score

Table 2 .

 2 Supervised classification

	Classes

https://www.r-project.org/

http://textometrie.ens-lyon.fr/

https://gephi.org/

https://neo4j.com/

http://scikit-learn.org/stable/

http://sphinxsearch.com/

http://www.mongodb.com/


Joachim Schöpfel
email: joachim.schopfel@univ-lille3.fr

RESEARCH AND DEVELOP MENT IN THE FIELD OF RESEARC H DATA AND DISSERTATIONS

Keywords: Research data, electronic theses and dissertations, data management, data sharing, data literacy, PhD training program, social sciences and humanities

a local workflow for the submission of research data related to PhD dissertations and the connection to the national RDM infrastructure

Introduction

Research data management (RDM) has been described as a "wicked problem" without easy answers, perhaps even insoluble, at least temporarily [START_REF] Awre | Research data management as a 'wicked problem[END_REF]. However, each research performing institution must define its own RDM strategy in order to provide optimal work conditions for its faculty and scientists. So even if (or just because) there may be no "best model" for RDM, institutions can learn from each other's experiences and successful initiatives.

Two years ago, at the 2015 Conference on Grey Literature and Repositories in Prague, we presented empirical results on research data in electronic theses and dissertations (ETDs) and on RDM behaviours and needs of scientists and PhD students on the social sciences and humanities (SSH) campus of the University of Lille [START_REF] Schöpfel | Making data in PhD dissertations reusable for research[END_REF]. The basic assumption of our research was (and still is) the observation that research results produced by PhD students can contribute to data-intensive scientific discovery [START_REF] Schöpfel | Open Access to Research Data in Electronic Theses and Dissertations: An Overview[END_REF]. They are "hidden treasures" [START_REF] Prost | Hidden Treasures. Opening Data in PhD Dissertations in Social Sciences and Humanities[END_REF]; however, a few issues must be addressed in order to make them visible, available and, moreover, reusable for scientific research.

In 2015, we published an institutional approach to RDM in the field of PhD dissertations as a White Paper [START_REF] Chaudiron | Livre blanc sur les données de la recherche dans les thèses de doctorat[END_REF], promoting five leading principles for the development of campus-based research data support services (see Schöpfel et al. 2015, figure 5). After validation by our research department, we started to implement this approach on the campus, together with the SSH graduate school, the GERiiCO research laboratory1 , the academic library and the ANRT service (National Centre for the Reproduction of PhD Dissertations). The implementation will take three years (2015)(2016)(2017)(2018); one part of the implementation was integrated in a research project called D4Humanities2 and received funding from the Regional Council (Conseil Régional Hauts-de-France) and the European Institute of Social Sciences and Humanities in Lille (MESHS).

Where are we now? What have we learned? Our paper will present the actual advancement of the implementation process, in the particular context of our institution and country, and it will make some statements and recommendations for similar initiatives.

The context

To foster uptake and increase efficiency and outcomes, the design and implementation of such a program must evaluate the specific conditions of the immediate and wider environment, including the expressed needs of the community being served. Here are the essential context features for the Lille program.

National context

 Since 2006, the French universities have a centralized system for the deposit, indexing and preservation of ETDs called STAR3 . The ETDs are reported in the French academic union catalogue SUDOC and on the platform for PhD dissertations4 . The author can opt for open access via an institutional or other academic open repository.  In 2018, the digital deposit of PhD dissertations on STAR will become mandatory for all universities, faculties and departments.  STAR allows the deposit of supplementary files including datasets but does not provide specific tools for their curation and publishing.  With funding from the CNRS (National Centre for Scientific Research) and some universities, the consortium Huma-Num is developing an infrastructure for the SSH research communities. This infrastructure includes a platform for the deposit, curation, recording, publishing and preservation of datasets (NAKALA).  The CNRS is also developing online services to improve the data literacy of scientists and professionals and to facilitate the preparation of data management plans5 .

Local context

 The University of Lille has a mandatory ETD policy; all PhD dissertations must be submitted in digital format, for deposit in the national STAR system.  A new institutional repository is under development (Dspace). It is uncertain to which extent it will accept the deposit of datasets, especially from PhD students.  The SSH campus provides a solution for the sharing of files in the cloud; however, the storage capacity is limited, and the server does not guarantee long term preservation Needs  Following our own and other survey results (Schöpfel & Prost 2016), scientists express above all a need for storage and long term preservation solutions, along with advice and assistance for RDM, for both the deposit as well as for the description and legal aspects. Data sharing, especially open access, is not a priority.  PhD students have less experience with RDM but are more motivated than other scientists in data sharing. They are also motivated by the RDM related criteria of project calls and funding agencies' programs, especially by the EU Framework Programme for Research and Innovation6 which requires the submission of a data managemen plan (DMP) along with the project proposal7 ; they know they must be compliant with these criteria in order to get funding for their research.

The development of a local workflow

Compliant with our surveys on the emerging environment of data repositories8 and, in particular, on similar projects9 we decided to create a local workflow for the deposit of research data by PhD students. Our assumptions:

 No new data repository (institutional/local or disciplinary) but a connection to existing infrastructure in SSH.  A solution linked to the national ETD system by metadata and identifiers but independent of this system.  A separation between data and dissertations from the beginning and onwards (separate deposit).  A "by default" solution, complementary to specific data repositories.  An integrative, complete solution covering all needs (recording, preservation, dissemination...).

The guiding principle was to provide an interface (with technical assistance) on our campus for the deposit of research data on the NAKALA platform of the national infrastructure for SSH communities. Figure 1 presents the main aspects of our solution.

As before, the ETDs will be submitted to the national STAR system by the academic library which in 2018 will integrate the actual ANRT staff; via the STAR system, the ETDs will be preserved by the national CINES agency, with their metadata being disseminated by the national academic union catalogue SUDOC and the national ETD portal Theses.fr. Following the PhD students' choice, the text of the dissertation can be published on the national open access TEL server, the Lille institutional repository and/or on another platform.

The same staff will deposit the associated datasets on the NAKALA platform, create the metadata and link them to the dissertation on STAR. After formal validation and acceptation of the files, NAKALA will guarantee the preservation, the dissemination (following the students' choice), the exposition of the metadata on the web and the indexing by the Huma-Num discovery tool ISIDORE10 . Like the ETD deposit, the academic library will supervise the submission of datasets together with the research laboratory GERiiCO11 , which will be in charge of the scientific follow-up of the workflow.

Figure 1: Local ETD/data workflow Thus, our main problem was not the creation of a new system but the connection between existing systems, with questions related to compliance and interoperability. The discussion with the NAKALA team identified eleven specific issues where action has to be taken:

Content/coverage
 Granularity: what exactly should be defined as a dataset for deposit? We have discussed this question in two communications (Schöpfel et al. 2016[START_REF] Schöpfel | Pour commencer, pourriezvous définir 'données de la recherche' ? » Une tentative de réponse[END_REF]. There are no clear rules or guidelines. The pragmatic solution is to accept datasets on a granularity level, which makes sense for understanding (validation) and reuse, and to allow deposit of dataset collections with a hierarchical structure.  Data format: which formats can be accepted? While the national ETD system only accepts PDF files, the NAKALA data repository supports all file formats that can be accepted by the national academic digital archive in Montpellier12 so that we can use their checklist FACILE as a filter for the validation of acceptable file format 13 .  Database: how should larger databases (surveys, inventories, text samples etc.) be dealt with? What are the limits for deposits on the NAKALA platform? This issue is part of the tests with the Huma-Num team.

Metadata

 Indexing: who should do the indexing? Our idea is that the indexing should be done and supervised by information professionals, based on the basic metadata provided by the PhD students for the national ETD system STAR.  Data structure: how should data be described and structured? Our option would be to apply the Metadata Encoding & Transmission Standard of the Library of Congress 14 but we still have to assess the compliance of METS with the NAKALA platform.  Referential: we decided to index five elements of the Dublin Core following a qualified metadata schema (file name, data type, creator, date, title). This means that we have to prepare precise descriptions and term lists and determine what is acceptable for these DC elements. These metadata together with the ETD and data identifiers will be used for the connection between dissertations on STAR and data on NAKALA.  Identifier: which unique identifier should be used for the datasets? Even if France is part of the DataCite consortium for the assignment of DOIs 15 , we opt for the moment for the handle system which is applied by the Huma-Num infrastructure, but we remain open for future adoption of the DOI.  Source code: a last issue is how to describe sources code-related to datasets. How can this information be included in metadata? So far we have no solution. Perhaps, this is out of scope, at least for the moment and/or for this project.

Other issues

 Legal aspects: we anticipate legal issues like copyright, third party rights, privacy etc. Our approach is twofold: we provide basic legal advice as part of the library's data service, and we ask the students to provide a declaration (template) that they have the permission to upload the datasets on NAKALA.  Deposit: who has access to the NAKALA platform? Who is an authorized user? Our first choice is to limit access to the project team (i.e. information professionals of the academic library, with a generic address and identification via the national academic IT network RENATER) and to prohibit self-archiving. However, this may change in the future.  Data size: actually, we don't know exactly what will be the potential data volume. In average, 60-80 PhD dissertations are submitted per year on our campus, representing roughly 2 GB. But except for very few dissertations, these deposits in the national ETD system do not contain data files. So all we can do is try to make some estimations, perhaps also with our international partner projects.

Four other issues have been raised but they are not directly linked to the development of the workflow:

 Long term preservation: Up to now, the NAKALA platform does not guarantee long-term preservation of submitted datasets. But they have an agreement with the national CINES agency which ensures long term preservation of backups of the different Huma-Num platforms' content, which means that the NAKALA datasets could be recovered if necessary.  Quality: the question was raised about the quality of datasets. Should all datasets provided by PhD students be accepted? Should we set up a kind of validation procedure? If so, which criteria should be applied? Who should evaluate? For the moment, we will not filter submitted data files otherwise than by formal criteria (size, format...), similar to other projects and data repositories. But the question remains open.  Promotion: we have already discussed how to promote the new data service -who should do this, what the best communication vectors are, and what should be the message. As mentioned before, the main message will not be "PhD students must share their data" but rather "we can provide a solution for the preservation of your research data". And the message will be communicated via the Graduate School, the Research Department and research laboratories and the academic library. Also, our intention is not to make the deposit of datasets mandatory but to promote and incite data deposit as a form of good scientific practice.  Technical documentation: after the launch of the new workflow, we will have to write the technical documentation, a procedure on two levels, one for the professional staff, the other for the students in the form of guidelines or recommendations to facilitate the process of submission and deposit.

The tests of the new workflow started at the end of September 2017. The workflow will be operational in 2018. We will perhaps customize the Huma-Num interface for the submission of datasets and the creation of metadata but this is not essential for the project.

We mentioned above that the University of Lille has started to develop a new institutional repository (Dspace). A priori, this would not modify the workflow for ETD related datasets, as it would not modify the submission of ETDs to the national STAR system. As Dspace is able to harvest metadata and to integrate different identifiers and outbound links, connecting the NAKALA datasets to the institutional repository should not be a problem.

The PhD training program

Our assumption is that RDM will become a part of basic scientific skills and good practices of research work, like sampling, statistics, surveys or systematic reviews. PhD students will have to obtain data literacy as part of their scientific education and training program. We started to work with PhD students on RDM nearly three years ago. Our experience is that most of them get some elements of data literacy through their research practice (e.g. privacy issues, ethics, backups) but they lack an overall ability of data curation, including description, preservation, and sharing 16 . In 2016 and 2017, we organised the program in a more traditional way, as a seminar with seven sessions (6 x 3 hours and 1 x 2 hours), one session per month from January to June, on different topics (figure 2). We try to cover the most important elements of RDM, with a double objective: provide theoretical and operational knowledge on research data and data management (data literacy), and develop practical skills (data behaviour). Based on the feedback of the first two years, our 2017 seminar focuses on the PhD students' own scientific experience and data behaviour; in each session, the introduction and overview is followed by discussion, practice and individual follow-up. Each session takes place in a computer room.

The operational goal of the seminar is the writing of a data management plan for each PhD project on the OPIDoR platform17 , which is the French adaptation of the JISC DMPonline service 18 . Each student creates his/her personal account on OPIDoR, writes a DMP with the European Commission's H2020 template and shares the DMP with the seminar's training staff, which provides individual follow-up, comments, suggestions etc. directly on the platform. At the end of the seminar, the staff downloads the final version of each DMP for evaluation and direct feedback.

• Introduction (3h)

• Data literacy, RDM etc.

2

• Data Managament Plan (3h)

• Why? Which content? How to write a DMP? Follow-up?

3

• Research data life cycle (3h)

• RDM as part of the research process 4

• Data description (3h)

• Metadata

5

• Data structuration (3h)

• Formats, XML etc.

6

• Data preservation and publishing (3h)

• Backups, storage, long term preservation, data papers, repositories

Concretely, each student completed the H2020 initial DMP template, i.e. a general description of the research project followed by five issues with specifications for each dataset:

 Dataset reference and name  Dataset description  Standards and metadata  Data sharing  Archiving and preservation (including storage and backup)

For instance, a student preparing an anthropological and ethnographic study on the perception of the 2016 Olympic Games by the people from Rio de Janeiro described three different data types, based on questionnaires, interviews and photos, and explained how he will index the data sets, with whom he will share them, and how he will store and preserve them.

Our experience with this approach is promising: with personal guidance, follow-up and feedback, the PhD students not only learn to write their own DMP but, in doing so, they learn to anticipate the essential issues of research data curation, like standard description, systematic back-up and secure storage and, in the end, long-term preservation and publishing. They also learn to prepare DMP in good and due form, compliant with the H2020 criteria, which will be an essential asset for future research work and project submission. Also, their DMP will contribute, as didactic material, to further training.

Nevertheless, we also observed that students who are just beginning their PhD have different questions and needs from those in second or third year of graduation who already have their methodology and often also datasets. In the future (2018), we will probably divide the seminar in two parts, the first one (modules 1-3, see figure 2) for "beginners" and the second (modules 4-7) for "advanced". Even so, the writing of a DMP will be part of both.

Lessons learned

We must keep in mind that our primary project target is not to foster or increase open access to/in research data. Our main goal is to help young scientists in SSH develop their data literacy, i.e. their RDM related skills, to raise awareness about the challenges of data curation and publishing, and to provide a solution by default for their datasets. Our project is an investment in the future, and we expect the young scientists to have efficient and modern data behaviours, just as we expect them to develop new methodologies and conceptual approaches in their research field. Data sharing is part of the game, but not the only or most important purpose.

1 -Our experience confirms that in many respects, RDM is not just a technical problem but a "people problem" [START_REF] Ward | Making sense: Talking data management with scientists[END_REF]. "Improving tools are not the only steps necessary to overcome barriers. The next steps will likely involve training for scientists (...)" [START_REF] Tenopir | Changes in data sharing and data reuse practices and perceptions among scientists worldwide[END_REF]. In fact, solutions are often available (cf. 10 say that our focus is not on data but on people. The most important decision of our project was perhaps the choice of a specific target group -young scientists and specifically PhD students in SSH. It was this choice that made our project intelligible and distinctive, on our campus as well as in the French HE landscape. 3). Data literacy (DMP, RDM) is more and more considered as good scientific practice, like usual scientific skills (sampling, statistics etc.). Our experience and conviction is that it is not enough to develop RDM tools if we do not teach scientists how (and why) to use them. Therefore, available infrastructure will shape the content of the training program (e.g. for file formats or dataset structuration); but then again, the discussion and feedback from the training program contribute to the further development of the campus-based RDM solutions. Our approach can be considered as an organizational learning process on the campus.

3 -The project is run together with the academic library research support team. However, it is NOT a library project. The library staff is part of the project, with specific tasks and skills. Their contribution is essential for the success of the project, and they are of course members of the project steering committee. Yet, from the beginning the project was designed as a research project with a doctoral training program, under the responsibility of the graduate school, with a scientific project management held by our research laboratory, and under the political and strategic leadership of our academic research department. There are at least two reasons: legitimacy (in the sense that scientists have everyday experience with RDM); and the fact that scientists do not usually consider RDM as a "library affair" but as part of their daily research work with other scientists and technical staff.

Workflow Training

Figure 4: RDM related research 4 -The project is not limited to education and workflow. As said above, its main character is research. We can distinguish two different levels of RDM related research, i.e. follow-up and insight (figure 4). Follow-up means monitoring and assessment of the training program, including feedback and continuous adjustment; and it means evaluation of uptake and usage of the new data workflow. Insight covers a larger range of data related topics, such as type, format and content of datasets, the impact of data on format and content of ETDs and the text and data mining of dissertations and data. At present, we are working on the first issue, together with partners from different universities and institutions. In the future, we will focus on the second issue and prepare, together with German colleagues and ProQuest, an international research project on new formats of PhD dissertations.

Following

  this assumption we launched a training program to develop and enhance RDM skills for PhD students in SSH, together with the research support service of the academic library but as part of the Graduate School training program, not as a library course. The integration in the official program of the Graduate School and the mixed pedagogic team (four scientists and one academic librarian) partly explains the success of the program, together with the newness and interest of the topic itself. Since the beginning, about 40 PhD students have participated.Our first training program (2015) consisted of three seminars (3 x 6 hours), organized together with scientists from the University of Lille and other institutions, on research data management, legal aspects and potential reuse and exploitation of data, including content mining.

Figure 2 :

 2 Figure 2: RDM training modules

Figure 3 :

 3 Figure 3: PhD training and ETD/data workflow

  Kindling et al. 2017). What is needed are new skills19 , information, promotion and incitation to use these solutions; paradoxically, we can 10th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library ofTechnology, 2017Technology, [cit. 2017-10-15]-10-15]. Available from:

http://nrgl.techlib.cz/conference/conference-proceedings/. ISSN 2336-5021.

Information and communication sciences, see http://geriico.recherche.univ-lille3.fr/

Deposit of Dissertation Data in Social Sciences and Humanities -A Project in Digital Humanities, see http://d4h.meshs.fr/ 10th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library of Technology, 2017 [cit. 2017-10-15]. Available from: http://nrgl.techlib.cz/conference/conference-proceedings/. ISSN 2336-5021.

http://star.theses.fr/

http://www.theses.fr/

Platform DoRANum http://doranum.fr/ and service DMP OPIDoR https://opidor-preprod.inist.fr/

Horizon 2020 or H2020, see https://ec.europa.eu/programmes/horizon2020/

See the H2020 guidelines http://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cuttingissues/open-access-data-management/open-access_en.htm

Nearly 2,000 sites indexed by the international directory re3data http://www.re3data.org/

For instance, the ETDplus project funded by Educopia https://educopia.org/research/grants/etdplus and the workflow at the University of Bielefeld, see[START_REF] Vompras | Repository workflow for interlinking research data with grey literature[END_REF]

10th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library ofTechnology, 2017Technology, [cit. 2017-10-15]-10-15]. Available from: http://nrgl.techlib.cz/conference/conference-proceedings/.ISSN 2336-5021.

ISIDORE combines a search engine and a metadata harvester for all kind of SSH data from the Huma-Num infrastructure https://www.rechercheisidore.fr/

Information and communication sciences, http://geriico.recherche.univ-lille3.fr/

CINES https://www.cines.fr/

https://facile.cines.fr/

10th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library ofTechnology, 2017Technology, [cit. 2017-10-15]-10-15]. Available from: http://nrgl.techlib.cz/conference/conference-proceedings/.ISSN 2336-5021.

Hosted by INIST https://dmp.opidor.fr/

Hosted by the JISC Digital Curation Centre https://dmponline.dcc.ac.uk/

• Feedback and evaluation (2h)10th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library ofTechnology, 2017Technology, [cit. 2017-10-15]-10-15]. Available from: http://nrgl.techlib.cz/conference/conference-proceedings/.ISSN 2336-5021.

Huma-Num (deposit, preservation and dissemination of research data via the NAKALA service), along with the RDM training program for PhD students provided by the Graduate School in Social Sciences and Humanities at the


Aline Muyle

Niklaus Zemp

Cécile Fruchard

Radim Cegan

Jan Vrana

Clothilde Deschamps

Raquel Tavares

Roman Hobza

Franck Picard

Alex Widmer

Gabriel Ab Marais

Genomic imprinting mediates dosage compensation in a young plant XY system

Sex chromosomes have repeatedly evolved from a pair of autosomes 1 . Consequently, X and Y chromosomes initially have similar gene content, but ongoing Y degeneration leads to reduced Y gene expression and eventual Y gene loss. The resulting imbalance in gene expression between Y genes and the rest of the genome is expected to reduce male fitness, especially when protein networks have components from both autosomes and sex chromosomes. A diverse set of dosage compensating mechanisms that alleviates these negative effects has been described in animals 2-4 . However, the early steps in the evolution of dosage compensation remain unknown and dosage compensation is poorly understood in plants 5 . Here we show a novel dosage compensation mechanism in the evolutionarily young XY sex determination system of the plant Silene latifolia. Genomic imprinting results in higher expression from the maternal X chromosome in both males and females. This compensates for reduced Y expression in males but results in X overexpression in females and may be detrimental. It could represent a transient early stage in the evolution of dosage compensation. Our finding has striking resemblance to the first stage proposed by Ohno for the evolution of X inactivation in mammals.

In Drosophila, the X chromosome is upregulated specifically in males, resulting in complete dosage compensation through both ancestral expression recovery in males and equal expression between the sexes (hereafter sex equality) 6 . In Caenorhabditis elegans, both X chromosomes are downregulated in XX hermaphrodites resulting in sex equality, but only a few genes have their X expression doubled for ancestral expression recovery 7 . In placental mammals, including humans, one X chromosome is randomly inactivated in XX females, resulting in sex equality but without recovering the ancestral expression of sex chromosomes, except for a few

dosage-sensitive genes whose X expression was doubled in both sexes [8][9][10][11][12] . In the marsupials, the paternal X chromosome is consistently inactivated in XX females 13 . Differential expression that depends on the parent of origin is known as genomic imprinting 14 , and this mechanism also operates in the mouse placenta 15 .

Despite the plethora of studies on gene expression on sex chromosomes, it is not yet clear if genomic imprinting is commonly involved in the early steps of dosage compensation evolution. In a seminal work, Ohno hypothesized a two-step process for the evolution of dosage compensation 16 . In the first step, expression from the X is doubled, thereby mediating the recovery of ancestral expression in XY males. Second, the resulting overexpression in XX females selects for X inactivation. This scenario is consistent with the fact that sexual selection is often stronger on males than on females. Under this scenario, selection on XY males to upregulate their single X chromosome should be stronger than selection on females, leading to overexpression in females until a second correcting mechanism evolves 3 . However, in order to understand these early steps of dosage compensation evolution, species with young sex chromosomes must be studied.

The plant Silene latifolia is an ideal model to study early steps of sex chromosome evolution thanks to its pair of X/Y chromosomes that evolved ~4 Mya 17 . Dosage compensation is poorly understood in plants 5 . Thus far only sex equality has been studied. Equal expression levels were observed for males and females for some genes despite Y expression degeneration 18- 23 . However, the mechanisms through which sex equality is achieved -and whether ancestral expression is recovered in S. latifolia males -remain unknown. To address these questions, we have developed an approach relying on (i) the use of an outgroup without sex chromosomes as an ancestral autosomal reference 5 in order to determine whether X chromosome expression increased or decreased in S. latifolia, (ii) the application of methods to study allele-specific expression while correcting for reference mapping bias 5 , and (iii) a statistical framework to quantify dosage compensation 5 .

Because only ~25% of the large and highly repetitive S. latifolia genome has been assembled so far [START_REF] Chikhi | Informed and automated k-mer size selection for genome assembly[END_REF] , we used an RNA-seq approach based on the sequencing of a cross (parents and a few offspring of each sex), to infer sex-linked contigs (i.e. contigs located on the nonrecombining region of the sex chromosome pair) 24 . X/Y contigs show both X and Y expression, while X-hemizygous contigs are X-linked contigs without Y allele expression. We made inferences separately for three tissues: flower buds, seedlings and leaves (Supplementary Table S2). Results are consistent across tissues and flower buds and leaves are shown in Supplementary Materials. In seedlings, ~1100 sex-linked contigs were inferred. Among these, 15% of contigs with significant expression differences between males and females were removed for further analyses (Supplementary Table S2 and Materials and Methods). These are likely involved in sex-specific functions and are not expected to be dosage compensated 25 . This was done as a usual procedure for studying dosage compensation, however the resulting trends and significance levels are not affected. About half of the non sex-biased sex-linked contigs could be validated by independent data using three sources: literature, a genetic map and sequence data from Y flow-sorted chromosomes (see Supplementary Table S2 and Materials and Methods). Xhemizygous contigs are more difficult to identify than X/Y contigs using an RNA-seq approach (see Supplementary Text S1). This explains conflicting earlier results on dosage compensation in S. latifolia 5 . A study using genomic data (i.e. not affected by the aforementioned ascertainment bias) found sex-equality in approximately half of the studied X-hemizygous genes [START_REF] Chikhi | Informed and automated k-mer size selection for genome assembly[END_REF] . In our set of X-hemizygous contigs, no evidence for dosage compensation was found (Supplementary Text S1), in agreement with previous work relying on an RNA-seq approach 18,22 . This could be due to an over-representation of dosage insensitive genes in our set of X-hemizygous contigs (Supplementary Text S1).

We estimated paternal and maternal allele expression levels in males and females for sexlinked and autosomal contigs in S. latifolia after correcting for reference mapping bias (Materials and Methods). We then compared these allelic expression levels to one or two closely related outgroups without sex chromosomes in order to polarise expression changes in S. latifolia. For autosomal contigs, expression levels did not differ between S. latifolia and the outgroups (Figure 1). This is due to the close relatedness of the outgroups (~5My, Supplementary Figure S1), and validates their use as a reference for ancestral expression levels. We used the ratio of Y over X expression levels in S. latifolia males as a proxy for Y degeneration and then grouped contigs on this basis. As expression of the Y allele decreased (paternal allele in blue in Figure 1), expression of the corresponding X allele in males increased (maternal allele in red in Figure 1). This is the first direct evidence for ancestral expression recovery in S. latifolia, i.e. ancestral expression levels are reestablished in males despite Y expression degeneration. In females, expression of the maternal X allele also increased with Y degeneration (gray bars in Figure 1), similarly to the maternal X allele in males. The paternal X alleles in females, however, maintained ancestral expression levels, regardless of Y degeneration (black bars in Figure 1). Consequently, sex equality is not achieved in S. latifolia due to upregulation of sex-linked genes in females compared to ancestral expression levels. These results were confirmed in two other tissues and when analysing independently validated contigs only (although statistical power is sometimes lacking due to the limited number of validated contigs, Supplementary Figures S2-S7).

Upregulation of the maternal X allele both in males and females of S. latifolia (Figure 1 and Supplementary Figures S2-S7) establishes a role for genomic imprinting in dosage compensation. In order to statistically test this inference at the SNP level, we used a linear regression model with mixed effects (Materials and Methods). Outgroup species were used as a reference and expression levels in S. latifolia were then analyzed while accounting for the variability due to contigs and individuals. The joint effect of the parental origin and the degeneration level was estimated, which allowed computing expression differences between maternal and paternal alleles in females for different Y/X degeneration categories (Figure 2).

Maternal and paternal alleles of autosomal SNPs were similarly expressed in females, indicating a global absence of genomic imprinting for these SNPs. However, for X/Y SNPs, the difference between the maternal and paternal X in females increased with Y degeneration. These results were confirmed in two other tissues and when analysing independently validated contigs only (although statistical power is sometimes lacking due to the limited number of validated contigs, Supplementary Figures S8-S13).

Previous studies that showed sex equality in S. latifolia could have been explained by simple buffering mechanisms, where one copy of a gene is expressed at a higher level when haploid than when diploid, due to higher availability of the cell machinery or adjustments in gene expression networks [START_REF] Chikhi | Informed and automated k-mer size selection for genome assembly[END_REF]26,27 . However, the upregulation of the X chromosome we reveal here in S. latifolia males cannot be explained by buffering mechanisms alone, as the maternal X in females would otherwise not be upregulated. Instead, our findings indicate that a specific dosage compensation mechanism relying on genomic imprinting has evolved in S. latifolia. This apparent convergent evolution with marsupials is mediated by different mechanisms (in marsupials the paternal X is inactivated 13 , while in S. latifolia the maternal X is upregulated). that DNA methylation is involved. Indeed, one arm of one of the two X chromosomes in females was shypomethylated, as well as the same arm of the single X in males 28 (Figure 3 and Supplementary Figure S14). Based on our results, we hypothesize that the hypomethylated X chromosome corresponds to the maternal, upregulated X. Unfortunately, parental origin of the X chromosomes was not established in this study 28 . It would be of interest in the future to study DNA methylation patterns in S. latifolia paternal and maternal X chromosomes, along with the homologous pair of autosomes in a closely related species without sex chromosomes. The methylation pattern observed by chromosome staining suggests that dosage compensation in S. latifolia could be a chromosome arm-wide phenomenon. To test this hypothesis with expression data, positions of genes along the X chromosome remain to be elucidated.

Our study is the first to establish female upregulation of the X chromosome compared to autosomes, as predicted by Ohno. An earlier report in Tribolium castaneum was later shown to be due to biases from inclusion of gonads in whole body extracts 4 . X overexpression in females may be deleterious. Its occurence suggests that reduced expression of sex-linked genes in males is more deleterious than overexpression in females. This potentially suboptimal situation may be transitory and a consequence of the young age of S. latifolia sex chromosomes. Sex equality may evolve at a later stage, following the evolutionary path trajectory originally proposed by Ohno for placental mammals 16 .

Methods

Sequence data and inference of sex-linkage. RNA-seq data was generated in S. latifolia for a cross (parents and progeny) for three tissues (seedlings, leaves and flower buds) and analysed using the SEX-DETector pipeline 24 . RNA-seq data was also generated for two outgroup species (S. viscosa and S. vulgaris). Reference mapping bias was corrected using the program GSNAP 29 .

Inferences of sex-linked contigs were validated using three sources of information (literature, a genetic map and flow-sorted Y chromosome sequences). See Supplementary Text S2 for details.

Allelic expression levels.

Contigwise autosomal, X, Y, X+X and X+Y normalised allelic expression levels were computed by summing read numbers for each X-linked or Y-linked alleles for filtered SNPs of the contigs (Supplementary Text S2) for each individual separately and then normalised using the library size and the number of studied sex-linked SNPs in the contig:

E = r/ (n * l) (1)
With E = normalised expression level for a given individual, r = sum of total read counts, n = number of studied SNPs, l = library size of the individual (number of mapped reads). Allelic expression levels were then averaged among individuals for each contig. In order to make S. latifolia expression levels comparable to S. viscosa and S. vulgaris, S. vicosa and S. vulgaris expression levels were estimated using only the filtered SNP positions used in S. latifolia.

Normalised expression levels computed as explained in equation (1) in the two outgroups were then averaged together for leaves and flower buds as expression levels are highly correlated (R 2 0.7 and 0.5 for flower buds and leaves respectively and p-value < 2.10 -6 in both cases). Averaging expression levels between the two outgroups allows to get closer to the ancestral autosomal expression level.

Sex-biased expression. Sex-biased contigs were inferred as in Zemp et al 30 . See Supplementary Text S2 for more detail.

Expression divergence between S. latifolia and the two outgroups at the contig level. The normalised difference in allelic expression between S. latifolia and the two outgroups (hereafter Δ) was computed in order to study how sex chromosome expression levels evolved in S. latifolia compared to autosomal expression levels in the two outroups: Δ is equal to zero if S. latifolia and the outgroups have equal expression levels, Δ is positive if S. latifolia has higher expression levels compared to the outgroups and Δ is negative otherwise: Δ = (S. latifolia expression level -outgroup expression level) / (outgroup expression level) (2) Sex-linked contigs were grouped by categories of degeneration level using the average Y over X expression ratio in males. 200 autosomal contigs were randomly selected in order to have similar statistical power among gene categories. Δ values for each allele (maternal and paternal in males and females) and each gene category were compared to zero using a Wilcoxon test. Pvalues were corrected for multiple testing using a Benjamini and Hochberg correction. The estimated median Δ, confidence intervals and adjusted p-values were then used to plot Figure 1 and Supplementary Figures S2 to S7.

Expression differences between maternal and paternal alleles at the SNP level. Maternal and paternal alleles expression were compared in S. latifolia for autosomal and sex-linked SNPs. In order to deal with the difference in numbers of autosomal versus sex-linked contigs (Supplementary Table S2), 200 autosomal contigs were randomly selected in order to keep comparable powers of detection. Allelic expression levels in S. latifolia for each individual at every SNP position were analysed using a linear regression model with mixed effects with the R package lme4. We assumed a normal distribution of the read count data after log transformation.

In order to account for inter-individual and inter-contig variability, a random "individual" and a random "contig" effect were included in the model. The aim of this modeling framework was to estimate the joint effect of the chromosomal origin of alleles (paternal or maternal in males or females) and the status of the gene (autosomal or sex-linked with various levels of Y degeneration defined by the average Y over X expression ratio in males). Two fixed effects with interaction were therefore considered in the model, see equation (3). In order to estimate the changes in sex-linked gene expression levels since the evolution of sex chromosomes, we used the average of the two outgroup expression levels as a reference (offset) for every SNP position, divided by two in order to be comparable to S. latifolia allelic expression levels. GSE35563, European Nucleotide Archive PRJEB14171), it included flower buds and leaf tissues from individuals of a cross in S. latifolia as well as individuals in S. vulgaris. In addition to these preexisting data, RNA-seq reads were generated in a comparable way for seedlings of a controlled cross using the same parents in S. latifolia, four males and four females were sampled (Seed_lati_female_1, Seed_lati_female_2, Seed_lati_female_3, Seed_lati_female_4, Seed_lati_male_1, Seed_lati_male_2, Seed_lati_male_3 and Seed_lati_male_4). Seedlings were also sequenced for S. vulgaris (Seed_vulg_herm_1, Seed_vulg_herm_2, Seed_vulg_herm_3 and Seed_vulg_herm_4). Seedlings were grown in a temperature controlled climate chamber in Eschikon (Switzerland) using the same conditions as in 8 . The S. latifolia and S. vulgaris seedlings were collected without roots at the four-leaf stage. The sexing of the S. latifolia seedlings was done using Y specific markers 9 that were amplified with the direct PCAR KAPA3G Plant PCR Kit (however male number 3 was later shown to be a female). High quality RNA (RIN > 8.5) was extracted using the total RNA mini kit from Geneaid. Twelve RNA-seq libraries were produced using the Truseq kit v2 from Illimina. Libraries were tagged individually and sequenced in two Illumina Hiseq 2000 channels at the D-BSSE (ETH Zürich, Switzerland) using 100 bp paired-end read protocol.

S. viscosa seeds we received from botanical gardens or collected in the wild by Bohuslav

Janousek and grown under controlled conditions in a greenhouse in Eschikon (Switzerland) and Lyon (France). Similarly to 8 , flower buds after removing the calyx and leaves were collected.

Total RNA were extracted through the Spectrum Plant Total RNA kit (Sigma, Inc., USA)

following the manufacturer's protocol and treated with a DNAse. Libraries were prepared with the TruSeq RNA sample Preparation v2 kit (Illumina Inc., USA). Each 2 nM cDNA library was sequenced using a paired-end protocol on a HiSeq2000 sequencer. Demultiplexing was performed using CASAVA 1.8.1 (Illumina) to produce paired sequence files containing reads for each sample in Illumina FASTQ format. RNA extraction and sequencing were done by the sequencing platform in the AGAP laboratory, Montpellier, France (http://umr-agap.cirad.fr/).

A female individual from an interspecific S. latifolia cross (C1_37) was back crossed with a male from an 11 generation inbred line (U10_49). The offspring (hereafter called BC1 individuals) were grown under controlled conditions in a greenhouse in Eschikon (Switzerland).

High quality RNA from flower buds as described in 10 was extracted from 48 BC1 individuals (35 females and 13 males). 48 RNA-seq libraries were produced using the Truseq kit v2 from Illimina with a median insert size of about 200 bp. Individuals were tagged separately and sequenced in four Illumna Hiseq 2000 channels at the D-BSSE (ETH Zürich, Switzerland) using 100bp paired-end read protocol. The parents used for this back cross had previously been sequenced in a similar way 1,8 .

1.2) DNA-seq data from filtered Y chromosome

Y chromosome DNA was isolated using flow cytometry. The samples for flow cytometric experiments were prepared from root tips according to 11 with modifications. Seeds of S. latifolia were germinated in a petri dish immersed in water at 25°C for 2 days until optimal length of roots was achieved (1 cm). The root cells were synchronized by treatment with 2mM hydroxyurea at 25°C for 18h. Accumulation of metaphases was achieved using 2.5μM oryzalin.

Approximately 200 root tips were necessary to prepare 1ml of sample. The chromosomes were released from the root tips by mechanical homogenization using a Polytron PT1200 homogenizer (Kinematica AG, Littau, Switzerland) at 18,000rpm for 13 s. The crude suspension was filtered and stained with DAPI (2μg/ml). All flow cytometric experiments were performed on FACSAria II SORP flow cytometer (BD Biosciences, San José, Calif., USA). Isolated Y chromosomes were sequenced with 2x100bp PE Illumina HiSeq.

1.3) RNA-seq PacBio data

Plants from an 11 generation inbred line were grown under controlled conditions in a greenhouse in Eschikon (Switzerland). One male (U11_02) was randomly selected. High quality RNA (RIN > 7.5) were extracted using the total RNA mini kit of Geneaid from very small flower buds, small and large flower buds, flowers before anthesis without calyces, rosette leaves, seedlings (4 leaves stage) and pollen. RNA of the different tissues was equally pooled and cDNA was produced using the Clontech SMARTer Kit. The cDNA pool was then normalized using a duplex specific endonuclease of the Evrogen TRIMMER kit. Two ranges were selected (1-1.3 kb and 1.2 -2 kb) using the Pippin Prep (Sage Science). Two SMRTbell libraries were prepared using the C2 Pacific Biosciences (PacBio) chemistry and sequenced with two SMRT Cells runs on a PacBio RS II at the Functional Genomic Center Zurich (FGCZ).

1.4) RNA-seq 454 data

Previously generated 454 data was used 8,12 .

2) Reference trancriptome assembly

The same reference transcriptome as in Muyle et al. 12 and Zemp et al. 8 was used.

3) Inference of sex-linked contigs

Autosomal and sex-linked contigs were inferred as in Muyle et al. 12 and Zemp et al. 8 .

Illumina reads from the individuals of the cross were mapped onto the assembly using BWA 13 version 0.6.2 with the following parameters: bwa aln -n 5 and bwa sampe. The libraries were then merged using SAMTOOLS version 0.1.18 14 . The obtained alignments were locally realigned using GATK IndelRealigner 15 and were analysed using reads2snps 16 version 3.0 with the following parameters: -fis 0 -model M2 -output_genotype best -multi_alleles acc -min_coverage 3 -par false. This allowed to genotype individuals at each loci while allowing for biases in allele expression, and without cleaning for paralogous SNPs. Indeed, X/Y SNPs tend to be filtered out by paraclean, a program which removes paralogous positions 17 . A second run of genotyping was done with paraclean in order to later remove paralogous SNPs from autosomal contigs only. SEX-DETector 12 was then used to infer contig segregation types after estimation of parameters using an SEM algorithm. Contig posterior segregation type probabilities were filtered to be higher than 0.8. Because the parents were not sequenced for the leaf and seedling datasets, SEX-DETector was run using the flower bud data for the parents.

4) Reference mapping bias correction

In order to avoid biases towards the reference allele in expression level estimates, a second mapping was done using the program GSNAP 18 with SNP tolerant mapping option. A GSNAP SNP file was generated by home-made perl scripts using the SEX-DETector SNP detail output file. Shortly, for each polymorphic position of all contigs, the most probable posterior SNP type was used to extract the possible alleles and write them to the GSNAP SNP file. This way, reference mapping bias was corrected for both sex-linked and autosomal contigs. Only uniquely mapped and concordant paired reads were kept after this. See Supplementary Table S1 for percentage of mapped reads. SEX-DETector was run a second time on this new mapping and the new inferences were used afterwards for all analyses (see Supplementary Table S2 for inference results).

5) Validation of sex-linked contigs

5.1) Detection of false X-hemizygous contigs

Erroneous inference of X-hemizygous contig can be due to a true X/Y gene which X and Y copies were assembled into different contigs. In order to detect such cases, X-hemizygous contigs were blasted 19 with parameter -e 1E-5 against RNA-seq contigs that have male-limited expression (see section 7 below for how male-limited contigs were inferred). These cases were removed from the analyses presented here.

5.2) Validation using data from literature

A few sex-linked and autosomal genes in S. latifolia have already been described in the literature (see Supplementary Table S3).

5.3) Validation using a genetic map

A genetic map was built and contigs from the X linkage group were used to validate SEX-DETector inferences. RNA-seq reads from the flower bud S. latifolia full-sib cross (hereafter CP) and backcross (hereafter BC1) were mapped against the reference transcriptome using BWA 13 with a maximum number of mismatch equal to 5. Libraries were merged and realigned using GATK 15 and SNPs were analysed using reads2snps 16 . Using a customized perl script, SNP genotypes from the parents and the offspring as well as the associated posterior probabilities were extracted from the reads2snps output file. Only SNPs with a reads2snps posterior genotyping probability higher than 0.8 were kept for further analyses. Then, only informative SNPs were kept: both parents had to be homozygous and different between father LEUK144-3 and mother U10_37 in a first generation backcross population design (BC1) and at least one allele had to be different between mother C1_37 and father U10_49 in the crosspollinator (CP). Filtered SNPs were then converted into a JoinMap format using a customized R script. If more than one informative SNP per contig was present, the SNP was used with less segregation distortion and less missing values. This led to 8,023 BC1 and 16,243 CP markers.

of the assignment of the linkage groups was tested using LepMap [START_REF] Kazama | SlWUS1; an X-linked gene having no homologous Y-linked copy in Silene latifolia[END_REF] . Blasting the contigs against known sex-linked genes allowed the identification of the X chromosome linkage group. Contigs could not be ordered along the linkage groups due to the too limited number of individuals that prevented the convergence of contig order. However, contigs were reliably attributed to linkage groups.

5.4) Validation using isolated Y chromosome DNA-seq data

Filtered Y chromosome DNA-seq reads were filtered for quality and Illumina adapters were removed using the ea-utils FASTQ processing utilities 22 . The optimal kmer value for assembly was searched using KmerGenie [START_REF] Chikhi | Informed and automated k-mer size selection for genome assembly[END_REF] . Filtered reads were assembled using soapdenovo2 24 with kmer=49, as suggested by KmerGenie. The obtained assembly was highly fragmented, therefore RNA-seq data was used to join, order and orient the genomic fragments with L_RNA_scaffolder 25 . The following RNA-seq reads were used (see section 1): one sample of male flower buds sequenced by 454, 6 samples of male flower buds sequenced by Illumina paired-end, 4 samples of male leaves sequenced by Illumina paired-end and one sample of male pooled tissues sequenced by PacBio. The genomic assembly was successively scaffolded with L_RNA_scaffolder using RNA-seq samples one after the other, first 454 samples then Illumina and finally PacBio. The obtained contigs were filtered to be longer than 200pb.

5.5) Set of validated sex-linked and autosomal contigs

The three sources of data (litterature, genetic map and filtered Y sequence data) were compared to SEX-DETector inferred sex-linked RNA-seq contigs using BLAST 19 with parameter -e 1E-5. Blasts were filtered for having a percentage of identity over 90%, an alignment length over 100bp and were manually checked. If a sex-linked RNA-seq contig blasted against a sequence from one of the three data sources (literature, X genetic map or filtered Y DNA-seq) it was then considered as validated. See Supplementary Table S2 for numbers of validated sexlinked contigs.

6) Expression level estimates 6.1) whole contig expression levels

Whole contig mean expression levels were obtained for each individual using GATK DepthOfCoverage 15 as the sum of every position coverage, divided by the length of the contig.

Normalised expression levels, in RPKM 26 , were then computed for each individual by dividing by the value by the library size of the individual (total number of mapped reads), accounting for different depths of coverage among individuals. Whole contig mean male and female expression levels were then computed by averaging male and female individuals for each contig.

6.2) Allelic expression levels filtering

In order to study separately X and Y allele expression levels in males and females, expression levels were studied at the SNP level. In S. latifolia, for each sex-linked contig expression levels were estimated using read counts from both X/Y and X-hemizygous informative SNPs. SNPs were attributed to an X/Y or X hemizygous segregation type if the according posterior probability was higher than 0.5. SNPs are considered informative if the father is heterozygous and has a genotype that is different from the mother (otherwise it is not possible to tell apart the X from the Y allele and therefore it is not possible to compute X and Y expression separately). X/Y SNPs for which at least one female had over two percent of her sex-biased expression were removed, as well as contigs with Y/X expression ratios above 1.5.

Sample sizes for the different contig categories are: autosomal:200; 1-1.5:148; 0.75-1:139; 0.5-0.75:160; 0.25-0.5:114; 0-0.25:79; 0:205 (note that 200 autosomal contigs were randomly selected in order to have similar statistical power among gene categories). In the absence of dosage compensation, the single X in males should be expressed at levels similar to the outgroup that does not have sex chromosomes, in other words, without dosage compensation Δ should be close to zero for the maternal allele in males (red bars). Results show that the maternal allele is hyper-expressed in S. latifolia when the Y chromosome is degenerated, both in males and females.

FiguresFigure 1 :

 1 Figures

Figure 2 :

 2 Figure 2: Normalised expression difference between maternal and paternal alleles in S. latifolia females in autosomal and sex-linked SNPs in the seedling tissue. The Y axis unit is the

Figure 3 :

 3 Figure 3: Illustration of DNA methylation staining results in S. latifolia from Siroky et al. 28 . See Supplementary Figure S14 for the original Figure. One arm of one of the two X chromosomes in females was hypomethylated, as well as the same arm of the single X in males.

Acknowledgments

This project was supported through an ANR grant ANR-14-CE19-0021-01 to G.A.B.M, SNSF grant 160123 to A.W and Czech Science Agency grant number 16-08698S to R.H. We thank Brandon Gaut, Tatiana Giraud and Judith Mank for comments on the manuscript.

Data Availability. The new sequence data presented here can be downloaded from the European Nucleotide Archive (ENA) under accession number PRJEB24933.

Supplementary Materials:

Supplementary Information includes Supplementary Texts S1-S2, Supplementary Figures S1-S14 and Supplementary Tables S1-S3.

Author contributions

Aline Muyle, Niklaus Zemp, Alex Widmer and Gabriel Marais conceived the study and experimental design. Niklaus Zemp and Alex Widmer prepared and sequenced the plant material.

Aline Muyle ran SEX-DETector on the RNA-seq datasets for the three tissues, analysed the data, prepared Tables and Figures and wrote the Supplementary Material with inputs from other authors. Niklaus Zemp generated the X chromosome genetic map (with help from Aline Muyle for the mapping and genotyping part). Radim Cegan, Jan Vrana and Roman Hobza did the Y chromosome flow cytometry sorting and sequencing. Clothilde Deschamps did the first assembly of the sorted Y chromosome and improved it with RNA-seq data with the help of Cecile Fruchard. Aline Muyle did the blasts to validate the inferences of SEX-DETector. Raquel Tavares did the GO term analysis. Aline Muyle and Frank Picard did the statistical analyses of the data.

Gabriel Marais and Aline Muyle wrote the main text of the manuscript with inputs from other authors.

Author information

The authors declare no competing interests.

Supplementary Materials

Supplementary Text S1: Dosage compensation in X-hemizygous genes

The first papers on dosage compensation in S. latifolia were contradictory because they focused on different gene sets. Muyle et al. 1 focused on X/Y gene pairs while other papers focused on X-hemizygous genes 2,3 . However, the X-hemizygous gene sets returned by the RNAseq approach used in those papers is less reliable than the X/Y gene sets 4 . A gene might be inferred as X-hemizygous simply because the -still functional -Y copy is not expressed in the tissue sampled for RNA-seq. In S. latifolia, X-hemizygous genes tend to be less expressed than X/Y genes and are less likely to be detected by segregation analysis as efficient SNP calling requires a certain read depth, see 4 . Moreover, X-hemizygous genes are inferred from X polymorphisms while X/Y genes can be detected both with X and X/Y polymorphisms, which are more numerous. Another inherent bias to X-hemizygous contig inference comes from the assembly step. If the X and the Y copy are too divergent to be assembled together, the X contig will be wrongly inferred as X-hemizygous because Y alleles will be absent from the contig (this bias was at least partly corrected in the analyses presented here, see Material and Method section 5.1). The inferences of X-hemizygous genes using the RNA-seq approach (including SEX-DETector) imply a higher rate of both false positives and false negatives than those for X/Y gene pairs. In Papadopoulos et al. 5 , 25% of the X/Y chromosomes were sequenced using a genomic approach. A much higher fraction of X-hemizygous genes was found than in previous RNA-seq papers 2,3 . Papadopoulos et al. 5 did find evidence for dosage compensation in approximately half of X-hemizygous genes (see their figure 3D). Due to limitations of the RNA-seq approach in inferring X-hemizygous genes, results on X-hemizygous contigs are analysed separately here.

Poor dosage compensation of X-hemizygous contigs compared to X/Y contigs with high Y degeneration was observed across all tissues (Supplementary Figures 2 to 7). Also, the parental origin of the X chromosome has limited to no effect on female X expression levels for Xhemizygous contigs, unlike X/Y contigs (Supplementary Figures 8 to 13). A reason that could explain such a different pattern for X-hemizygous genes compared to X/Y genes is the possible dosage insensitivity of X-hemizygous genes. X-hemizygous genes could have lost their Y copy because dosage was not important for them and selection neither slowed down the loss of the Y copy nor selected for dosage compensation when degeneration inevitably occurred 6 . A well described characteristic of dosage sensitive genes is that they tend to code proteins involved in large complexes 7 . Gene Ontology was studied using the Blast2GO PRO version 2.7.2 30 as in 8 .

Using the GO-term analysis, our set of X-hemizygous contigs were found to be significantly depleted in ribosomal protein coding genes compared to autosomal genes (p-value 1.3.10 -4 ), which is consistent with the global dosage insensitivity of X-hemizygous genes in S. latifolia. This depletion in large protein complexes was not found when comparing X/Y genes to autosomal genes.

Supplementary Text S2:

1) Plant material and sequencing 1.1) RNA-seq Illumina data reads belonging to the Y allele were removed as unlikely to be true X/Y SNPs. Informative autosomal SNPs from autosomal contigs were used in a similar way.

For contigs that only have X/X SNPs (SNPs for which the father's X is different to both Xs from the mother), Y expression level is only computed from the father as all males are homozygous in the progeny. Such contigs were therefore removed when having under 3 X/X SNPs to avoid approximations on the contig mean Y/X expression level (39 contigs removed in the flower buds dataset, 44 in the leaves dataset and 40 in the seedlings dataset).

In order to make S. latifolia expression levels comparable to S. viscosa and S. vulgaris for sex-linked contigs, S. vicosa and S. vulgaris expression levels were estimated using only the positions used in S. latifolia (informative X/Y or X-hemizygous SNPs). The read count of every position in every contig and for every S. viscosa and S. vulgaris individual was given by GATK DepthOfCoverage 15 . Only positions corresponding to informative autosomal, X/Y or Xhemizygous SNPs in S. latifolia were used to compute the expression level for each contig and each individual as explained in equation (1).

Contigwise S. latifolia autosomal, X, Y, X+X, X+Y allelic expression levels were then averaged among individuals. Autosomal normalised expression levels in the two outgroups (S. vulgaris and S. viscosa) were averaged together.

7) Identification of contigs with sex-biased expression

The analysis was done separately for the three tissues (flower buds, seedling and rosette leaves) as in Zemp et al. 8 using the R package edgeR 27 . See Supplementary Table S2 for number of sex-biased contigs removed in order to study dosage compensation. Male-limited expressed

Supplementary Figures

Supplementary Figure S1: Relatedness among the three studied species, extracted from [START_REF] Rautenberg | Geographic and phylogenetic patterns in Silene section Melandrium (Caryophyllaceae) as inferred from chloroplast and nuclear DNA sequences[END_REF] ages at the nodes are shown in million years (My). The exact relationship among species is poorly resolved [START_REF] Rautenberg | Geographic and phylogenetic patterns in Silene section Melandrium (Caryophyllaceae) as inferred from chloroplast and nuclear DNA sequences[END_REF][START_REF] Jenkins | A phylogenetic comparative study of preadaptation for invasiveness in the genus Silene (Caryophyllaceae)[END_REF][START_REF] Rautenberg | Phylogenetic Relationships of Silene multinervia and Silene Section Conoimorpha (Caryophyllaceae)[END_REF] . In some phylogenies S. viscosa is closest to S. latifolia, whereas in others S. vulgaris is closest as shown here, and in others both species are equally diverged to S. latifolia. Supplementary Figure S2: Normalised difference in allelic expression levels between S. latifolia and the two outgroups without sex chromosomes S. vulgaris and S. viscosa (hereafter Δ), in autosomal and sex-linked contigs for the seedling tissue. Maternal and paternal allelic read numbers were summed at SNP positions and normalised for each individual separately, then averaged among individuals for each contig. Δ was computed as follows: Δ=(allelic expression in S. latifolia -allelic expression in the outgroup) / allelic expression in the outgroup). If Δ is lower, higher or equal to zero, then expression in S. latifolia is respectively lower, higher or equal to the outgroup. For all contig categories, Δ was compared to zero using a Wilcoxon test.

Supplementary Tables

Supplementary Table S1: library sizes (number of reads) of each individual and mapping statistics. S2: Number of contigs after SEX-DETector inferences, removal of sexbias and selection of validated contigs in the three tissues. Supplementary Table S3: list of known sex-linked genes in S. latifolia and associated literature references.

Supplementary Table