Sonia Maalej
email: sonia.maalej@gmail.com

Alexandre Kruszewski
email: alexandre.kruszewski@ec-lille.fr

Romain Delpoux
email: romain.delpoux@ensam.eu

Lotfi Belkoura

Derivative Based Control For LPV System With Unknown Parameters: An Application on a Permanent Magnet Synchronous Motors

Keywords: Linear Time Varying systems, Robust control, Uncertainties, Linear Matrix Inequalities, Polytopic systems, Permanent Magnet Synchronous Motors

This paper deals with the robust stabilization of a class of Linear Parameter Varying (LPV) systems in the continuous time case. Instead of using a state observer or searching for a dynamic output feedback, the controller is based on output derivative estimation. This allows the stabilization of the plant with very large parameter variation and uncertainties. The proof of stability is based on the polytopic representation of the closed loop, Lyapunov conditions and system transformations. The result is a control structure with only few parameters which are tuned via very simple conditions. This paper illustrates the usefulness on real application: Permanent Magnet Synchronous Motors (PMSM) position control.

I. Introduction

In the industrial process control field, the most common controllers are traditional PID which were developed in 1940s. These controller are very famous because of their low number of tuning parameters allowing in some cases empiric tuning. Unfortunately, due to their linear nature, the performance guarantee are often limited to a statespace region around a given set-point. For complex plants, it is really hard/impossible to tune them correctly without a good model or a more complex control structure. This is due to the nonlinear nature of most systems and the variation of their parameters.

Because internal states of most industrial plants cannot be directly measured and only their outputs are available for control purposes, output feedback controllers were considered. In output feedback design, static output feedback [START_REF] Huang | Static output feedback controller design for fuzzy systems: An ilmi approach[END_REF], [START_REF] Mansouri | Output feedback lmi tracking control conditions with h∞; criterion for uncertain and disturbed t-s models[END_REF], dynamic output feedback [START_REF] Sato | Gain-scheduled output-feedback controllers depending solely on scheduling parameters via parameter-dependent lyapunov functions[END_REF] and fuzzy observerbased control approaches [START_REF] Tanaka | Fuzzy regulators and fuzzy observers: relaxed stability conditions and lmi-based designs[END_REF], [START_REF] Sala | Perspectives of fuzzy systems and control[END_REF] have been employed. The design procedure of these controller need a good model and for some of them the use of complex optimization techniques. Even if the available model is good enough, using a dynamic model for computing the control law implies the identification of the model parameters. Unfortunately, the choice of the physical model structure, the identification of the model parameters, the experimental validation of model are never simple and are time consuming. Therefore, PID controllers are often tuned with a simple (linear) non physical model ( [START_REF] Åström | Advanced PID Control. ISA -The Instrumentation, Systems, and Automation Society[END_REF], [START_REF] Khodabakhshian | A new robust {PID} load frequency controller[END_REF]). However, this could lead to poor results when a process has a large operating domain.

Two important aspects have to be considered in the output feedback control problem. The first one is related to the design of the feedback control law for any system such that the closed loop is stable and guaranteed performances and tracking. This problem has attracted much research effort which resolve this problem by considering for example the L 2 gain, H ∞ norms (see [START_REF] Van Der Schaft | L2-Gain and Passivity Techniques in Nonlinear Control[END_REF], [START_REF] Zhang | Robust sliding mode h∞; control using time-varying delayed states for offshore steel jacket platforms[END_REF], [START_REF] Li | Research on robust h 2 /h∞; optimization control for unified power quality conditioner in micorgrid[END_REF]), robust LQ control [START_REF] Watanabe | Tracking control for 2dof helicopter via robust lq control with adaptive law[END_REF], [START_REF] Watanabe | Robust lq control with adaptive law for mimo descriptor system[END_REF], [START_REF] Stecha | Lq and dead beat control combination from the set of stabilizing controllers[END_REF]. The second one is related to the non availability of all variable parameters in realtime for implementation of complex control laws. As a matter of fact, the control law depends on the output of the system and on some of the varying parameters. If no information about these variable parameters is available, a constant (i.e., independent of the premise variables) output feedback control gain may be an alternative but yielding, in general, conservative results.

A solution to this problem is proposed based on some new results in the framework of model f ree control given by Cedric et al. [START_REF] Cédric | Étude préliminaire d'une commande sans modèle pour papillon de moteur ..... A modelfree control for an engine throttle: a preliminary study[END_REF]. The latter is signal based and don't require many information about the system. To implement this controller, the output derivatives has to be available in real time. In the literature, many variant of such controller have been developed (see [START_REF] Abouaissa | First steps towards a model-free control of a freeway traffic flow-Prolégomènes à une régulation sans modèle du trafic autoroutier[END_REF], [START_REF] Gédouin | Experimental comparison of classical pid and model-free control: position control of a shape memory alloy active spring[END_REF], [START_REF] Cédric | Étude préliminaire d'une commande sans modèle pour papillon de moteur ..... A modelfree control for an engine throttle: a preliminary study[END_REF], [START_REF] Novel | Commande sans modèle de la vitesse longitudinale d'un véhicule électrique[END_REF]). All these studies state that the stability as well as performances are ensured but none of them provides a standard proof on a class of system.

There exist in the literature many techniques for the signal derivation. The simplest one is to use a linear derivative filter i.e. any stable transfer function with a stable denominator and a pure differentiators for the numerator. Other possibilities consist in the use of algebraic differentiators [START_REF] Mboup | Parameter estimation for signals described by differential equations[END_REF], [START_REF] Cédric | Étude préliminaire d'une commande sans modèle pour papillon de moteur ..... A modelfree control for an engine throttle: a preliminary study[END_REF], Luenberger unknown input observer or sliding mode observers [START_REF] Slotine | Sliding controller design for non-linear systems[END_REF], [START_REF] Nounou | Robust adaptive sliding-mode control for continuous time-delay systems[END_REF], [START_REF] Yan | Robust sliding mode observer with parameters estimation for a class of nonlinear timedelay systems[END_REF], [START_REF] Feng | Terminal sliding mode observer based parameter estimation method for permanent magnet synchronous motor control system[END_REF].

In this paper, a controller introduced by the authors in previous work [START_REF] Maalej | Derivative based control for ltv system with unknown parameters[END_REF] is considered. This controller is based on a signal differentiators and dynamic nullification. It has a fixed structure and only one parameter. In order to show the efficiency of this controller, a study case of PMSM (Permanent Magnet Synchronous Motors) position control is given. The stability of the system closed loop under parametric uncertainties is studied using the Lyapunov theory and polytopic transformation. The result is presented as a set of LMI (Linear Matrix Inequalities) to solve.

The first part of this paper describes the class of systems and the controller structure. The second is devoted to the description of the Permanent Magnet Synchronous Motors as well as its polytopic representation. The third part provides numerical LMI solutions ensuring the stability of the controlled PMSM under time varying parametric uncertainties. Some real experiments illustrates the consistency of this approach. The last part gives some conclusions and perspectives.

II. Problem formulation

A. The class of system

In this paper, we consider the following class of single input single output linear system with time varying parameters:

y (n) (t) = -a 0 (t)y(t) -. . . -a n-1 (t)y (n-1) (t) + αu(t). (1)
where y(t) ∈ R is the output, u(t) ∈ R is the control input and a i are scalar time varying parameters.

For design simplification purposes, the system will be viewed as the following equation from the controller point of view:

y (v) (t) = f (t) + αu(t).
(

) 2
It means that only the differential equation order v and an approximation of the parameter α are known. These two parameter are the easiest to get for most real system. In the formulation of the system equation given in (2), f (t) is the function which contains all the dynamic information, i.e. the global dynamic of the system and the possible disturbances. The function f (t) is assumed to be control independent.

Remark 1: v is not necessarily equal to the model order n especially if the model has some fast stable dynamics.

B. The controller

Since the only information on the system are given by Equation ( 2), the idea of the controller structure is to nullify the system dynamic f (t) and then replace it with the ideal dynamic for the closed loop. Considering this idea, we get the following controller (Fig. 1):

u(t) = - 1 α ( f (t) + K Ŷ (t) -k 0 r(t)). ( 3
)
where f (t) is the estimate of

f (t), α is an approximation of α, r(t) is the reference, Ŷ (t) = [z 0 (t) . . . z v-1 (t)] T is a vector composed with z i (t) the estimation of y (i) (t) and K = [k 0 . . . k v-1
] is a vector with the coefficients of the ideal closed loop dynamic given by the specifications.

y(t)

Open Loop Dynamic Nullifier

- + Ideal Controller u(t) r(t) Feedback Closed Loop Estimator

C. Derivative estimation

In this part, the problem of estimation of f (t) and the successive derivatives of y(t) are considered. In order to get a simple solution, we consider linear filters for the output derivation:

   z0(s) y(s) = 1 τ s+1 zi(s) y(s) = E(s) i = s τ s+1 i+1 ∀i = 1 . . . v (4)
This estimator is causal and ensures a good estimation if τ is sufficiently smaller than the fastest relevant dynamic of the system. It provides the successive estimations z i (t) of y (i) . Remark 2: In the literature, many approaches were proposed: one can consider sliding mode estimation [START_REF] Slotine | Sliding controller design for non-linear systems[END_REF], [START_REF] Nounou | Robust adaptive sliding-mode control for continuous time-delay systems[END_REF], [START_REF] Yan | Robust sliding mode observer with parameters estimation for a class of nonlinear timedelay systems[END_REF], [START_REF] Feng | Terminal sliding mode observer based parameter estimation method for permanent magnet synchronous motor control system[END_REF], Luenberger observer based estimation and algebraic estimation [START_REF] Mboup | Parameter estimation for signals described by differential equations[END_REF].

The estimation of f (t) is made by inverting the dynamic Equation (2) but instead of using the value of u(t), a filtered version of it must be used. This is one of the possible solution to avoid the algebraic loop between u(t) and f (t).

f (t) = z v (t) -αû(t) (5)
with z v (t) being the estimation of y (v) (t) and û(s) u(s) = 1 τ s + 1 .

III. Main result

Let consider the following model of Permanent Magnet Synchronous Motors (PMSM) in the rotation frame (d-q) [START_REF] Delpoux | Parameter estimation of permanent magnet stepper motors without position or velocity sensors[END_REF]:

       L di d dt = v d -Ri d + n p Lwi q L diq dt = v q -n p Lwi d -Ri q -K c w J dw dt = K c i q -f v w dθ dt = w (6)
where v q and v d are the voltages applied to the two phases of the PMSM, i d and i q are the two phase currents, L is the inductance of a phase winding, R is the resistance of a phase winding, K c is the back-EMF constant (and also the torque constant), w is the angular velocity of the rotor, θ is the angular position of the rotor, n p is the number of pole pairs (or rotor teeth), J is the moment of inertia of the rotor (including the load) and f v is the coefficient of viscous friction.

A. Closed loop State space representation

The state space representation of the different dynamic equation of the last section are given here. The model in the state space form is given by choosing the state vector as x m (t) = [i d (t) i q (t) w(t) θ(t)] T , v d = 0 and v q as the system control input and the rotor angular position θ(t) as output:

ẋm (t) = A m (w(t))x m (t) + B m v q (t) y(t) = C m x m (t) (7)
where

A m (w(t)) =     -R/L n p w(t) 0 0 -n p w(t) -R/L -K/L 0 0 K/J -f v /J 0 0 0 1 0     ,
B T m = 0 1/L 0 0 and C m = 0 0 0 1 Since the currents dynamics i d and i q are fast and stable, it is sufficient to consider a second order controller design (v = 2). The controller (2) is based on the following model:

y (2) (t) = f (t) + αu(t) α = K/( Ĵ R) (8)
The reduced order controller can be represented as it follows where ẋe

(t) = [z 1 (t) z 2 (t) û(t)] T :    ẋe (t) = A e x e (t) + B ey y(t) + B eu v q (t) Ŷ (t) = C eyx x e (t) + C eyy y(t) û(t) = C eu x e (t) (9)
where:

A e =   -1 τ 0 0 -1 τ 2 -1 τ 0 0 0 -1 τ   , B T eu = 0 0 1 τ , C eyx = 1 0 0 -1 τ 0 0 , C T eyy = 0 1 τ , B T ey = 1 τ 1 τ 2
0 and C eu = 0 0 1 .

Then, the estimation of the dynamic f (t) is:

f (t) = C f x x e (t) + C f y y(t),
where:

C f x = -1 τ 2 -1 τ -α and C f y = 1 τ 2 .
By injecting the expression of (3) in ( 9), the controller can be represented as follows:

ẋe (t) = A o x e (t) + B o1 y(t) + B o2 r(t) v q (t) = C o x e (t) + D o1 y(t) + D o2 r(t) ( 10
)
where

A o = A e -Beu α (C f x + KC eyx ) B o1 = B ey -Beu α (C f y + KC eyy ) B o2 = Beu α k 0 C o = -1 α (C f x + K C eyx ) D o1 = -1 α (C f y + K C eyy ) D o2 = k0 α
Finally the closed loop of the system ( 7) and its controller ( 10) is:

ẋ(t) = A(w(t))x(t) + Br(t) y(t) = Cx(t) (11)
with the closed system state

x(t) = [x m (t) x e (t)] T , A(w(t)) = A m (w(t)) + B m D o1 C m B m C o B o1 C m A o , B = B m D o2 B o2 and C = 0 (1×3) 1 0 (1×3) .

Consider that w(t) ranges between known extremal value w(t) ∈ [w, w].

In this frame, the PMSM can be described using a LPV state space representation which depends on a time varying parameter w(t).

B. Polytopic representation and stability analysis

There exist several ways to get stability conditions. Here the closed loop system given by ( 11) is considered as a polytopic system [START_REF] Boyd | Linear matrix inequalities in system and control theory[END_REF] and the stability analysis is performed using a quadratic Lyapunov function V (x) = x T P x. The stability conditions are given in the form of a set of LMI to satisfy.

The model may be represented as follows:

   ẋ(t) = A(µ(t))x(t) + Br(t), A(µ(t)) = N i=1 µ i (t)A i , ∀i, µ i (t) ≥ 0, N i=1 µ i (t) = 1. ( 12
)
where N = 2, with A 1 = A(w) and A 2 = A(w).

The stability theorem of this class of system is given by: Theorem 1: If it exist a matrix P = P T > 0 such that

A T i P + P A i < 0, ∀i = 1, . . . , N (13)
then, the closed loop ( 12) is asymptotically stable. The proof is obvious considering the results of [START_REF] Boyd | Linear matrix inequalities in system and control theory[END_REF].

IV. Application In this section, the stability and the robustness of the closed loop is analyzed by considering the stepper motor test bench developed in LAGIS at École Centrale de Lille (see Fig. 3). The motor parameters have been identified in [START_REF] Delpoux | Parameter estimation of permanent magnet stepper motors without position or velocity sensors[END_REF]: R = 2.86Ω, L = 10.2mH, K c = 0.26N.m.A -1 , F v = 2.37.10 -4 N.m.S.rad -1 , J = 3.18.10 -4 kg.m 2 and w(t) ∈ [-15, 15]rad.s -1 .

A. Stability Analysis

Consider the closed loop [START_REF] Watanabe | Robust lq control with adaptive law for mimo descriptor system[END_REF] with the controller parameters τ = 0.001, n = 2 and the desired closed loop dynamic ÿ(t) = -100y(t) -20 ẏ(t) + 100r(t) i.e. K = [100 20]. By using MatLab/SeDuMi [START_REF] Sturm | Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones[END_REF] as solver and YALMIP [START_REF] Lofberg | Yalmip : a toolbox for modeling and optimization in matlab[END_REF] as parser in MATLAB , a feasible solution for Theorem 1 LMI conditions is obtained. This ensures the closed loop stability.

B. Experimental result

The results obtained on the LAGIS test bench (see Fig. 3) are presented in Figure 2. This figure shows the evolution of the PMSM position θ(t) and the ideal closed loop output

θ ref (solutions of ÿ(t) = -k 0 y(t) -k 1 ẏ(t) + k 0 r(t))
for different values of τ and k i . It appears clearly that for smaller values of τ the tracking error E t is closer to zero. Considering small enough τ allows to follow any specified dynamic (k 0 , k 1 ) but the control signal v q is more affected by noises and may be more susceptible to saturate. Despite that the controller is derivative based, the noises affecting the control signal are low and acceptable.

V. Robustness Analysis

In order to show the robustness of this controller, we consider two cases: The first case considers time varying uncertainties on the PMSM parameters. The second case considers a bad choice on the controller parameter α.

A. System Parameters Time Varying Uncertainties

For this example, consider that the controller parameters are τ = 0.01, K = [100 20] and α = K/(JR) (K, J and R being the parameters nominal values). The PMSM parameter [START_REF] Khodabakhshian | A new robust {PID} load frequency controller[END_REF] uncertainties are now considered with the following system matrix A m (t):

A m (t) =     -p 3 (t) n p w(t) 0 0 -n p w(t) -p 3 (t) -p 4 (t) 0 0 p 1 (t) -p 2 (t) 0 0 0 1 0     ,
where the time varying parameters are:

• p 1 (t) ∈ [0.9 * K/J 1.2 * K/J] represents the time varying uncertainty on K/J,

• p 2 (t) ∈ [0.9 * F v /J 1.2 * F v /J] represents the time varying uncertainty on F v /J, • p 3 (t) ∈ [R/L 1.2 * R/L] represents the time varying uncertainty on R/L,
• p 4 (t) ∈ [K/L 1.02 * K/L] represents the time varying uncertainty on K/L. Since the matrix A m (t) is bounded, it is easy to obtain a convex hull vertices [START_REF] Boyd | Linear matrix inequalities in system and control theory[END_REF] A i , i = 1, . . . , N such that A(t) ∈ conv(A 1 , . . . , A N ). Here N = 2 5 because the matrix A m (t) has 5 independent time varying parameters (w(t) and p i (t)). Because the LMI problem of Theorem 1 with these matrices has a feasible solution, then PMSM in closed loop is stable despite the time varying uncertainties.

Note that the results provided here are not optimized. By considering different values of τ and α or by considering more general Lyapunov functions [START_REF] Asemani | A multiple lyapunov function approach for robust h∞; observer-based tracking control of uncertain t-s fuzzy systems[END_REF], [START_REF] Horng | H2 guaranteed cost of uncertain continuous t-s fuzzy systems by multiple lyapunov function approach[END_REF], these uncertainties can be considered larger.

B. Bad Tuning Of α

For this case, consider that that the controller parameters are τ = 0.01 and K = [100 20], and that the parameter α has an uncertainty of ±20. By following the same approach as the previous section, the LMI problem of Theorem 1 with the appropriate matrices has a feasible solution so the PMSM in closed loop is stable despite the bad tuning of the parameter α.

Another important point concerns the system robustness and the estimation of the parameter α. E t α1 E t α2 E t α3 v q α1 v q α2 v q α3 Fig. 4. Simulation results on the PMSM showing the evolution of the output θ(t), the specified dynamic θ ref (t), the tracking error Et and the control signal vq for different approximation of α such that α1 = 0.5α, α2 = α and α3 = 1.5α

shows that even if there is some mismatch between this parameter and its estimation α, the stability and the specified dynamic tracking can be guaranteed.

VI. Conclusion

This paper proposed a method to design dynamic output controller. In contrast of observer based controllers, the proposed controller allows the stabilization of LPV systems even if the time varying parameters are not measured. An application to the Permanent Magnet Synchronous Motors stabilization with uncertainties is proposed. This application shows the good tracking properties of this controller and the simplicity of its tuning. Our future work will deal with extended the class of system (multi-input multi-output, zero dynamics), guaranteed performances and noise rejection.

Fig. 1 .

 1 Fig. 1. Controller plant

Fig. 3 .

 3 Fig. 3. Stepper-motor test bench.

Fig. 2 .

 2 Fig. 2. Experimental results on the PMSM showing the evolution of the output θ(t), the specified dynamic θ ref (t), the tracking error Et and the control signal vq for different value of the controller parameter

Figure

4

 4


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

Hélène Prost
email: helene.prost@inist.fr

Cécile Malleret
email: cecile.malleret@univ-lille3.fr

MAKING DATA IN PHD DISSERTATIONS REUSAB LE FOR RESEARCH

Keywords: PhD Dissertations, Research Data, Data Management, Open Access, Digital Humanities

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Dissertations and data

Open access to PhD dissertations1 is on the agenda of academic libraries. Today, the rapid development of data-driven research (e-Science) and the debate on open data and re-use of research results has led us to discover another challenge in the field of PhD dissertations, beyond the debate on open access and embargo, i.e. the existence of large amounts of data produced by the PhD candidate and partly submitted together with the text of the dissertation. How can these data be made available in the context of open access and open data policies, what are the potential barriers for dissemination and reuse, and how can academic libraries contribute to this challenge?

Research results produced by PhD students could contribute to data-intensive scientific discovery [START_REF] Schöpfel | Open Access to Research Data in Electronic Theses and Dissertations: An Overview[END_REF]. The PhD dissertations could become "windows for the scientist" not only to understand but also to reproduce and extend scientific results [START_REF] Lynch | Jim Gray's Fourth Paradigm and the Construction of the Scientific Record[END_REF], in so far as they could integrate research data that could be enriched, updated, extracted, shared, aggregated and manipulated [START_REF] Mcmahon | Interactive Publications and the Record of Science. Information Services and Use [online[END_REF]. They could become live documents. However, there are two barriers.

First, dissertations must be freely available in open access, deposited in institutional or other repositories and disseminated with sufficient user rights to allow reuse. So far, the reality is mitigated. On the one hand, more than half of all open repositories contain theses and dissertations and the technical and political environment globally supports open access to academic works. On the other hand, a significant portion of the digital dissertations are not online, not open, not freely available but embargoed or under restricted access [START_REF] Schöpfel | A French-German survey of electronic theses and dissertations: Access and restrictions[END_REF].

The second barrier is the fact that research data related to PhD dissertations are largely "dark data", i.e. "data that is not easily found by potential users (…), unpublished data (and) research findings and raw data that lie behind published works which are also difficult or impossible to access as time progresses" (Heidorn 2008, pp.281 and 285). They are, in other terms, "hidden treasures". These data, defined as reusable research results, collected, observed, or created for purposes of analysis to produce original research results, are produced in a large variety of formats, 3 sources and types. Research results may be presented as tables, graphs, etc. in the paper or as additional material (appendix). In the past, print theses and dissertations have regularly been submitted together with supplementary material and data, in various formats and on different supports (print appendices, punched cards, floppy disks, audiotapes, slides, CD-ROMs). In the new ETD infrastructures, such material can be processed together with the text files or as supplementary files in different formats, depending on disciplines, research fields and methods. But as the ETDplus project at the Educopia Institute at Atlanta, Georgia, states, these "complex digital objects (e.g., software, multimedia files, digital art, and other material that sometimes is integral to the thesis or dissertation itself…)" are often not collected or preserved2 . Sometimes the data are available on a distant server. And too often the data are simply not available; or data, methodology, tools, primary sources are mingled, not or badly indexed, or not linked with the text.

Description and preservation of digital objects are part of the work of traditional academic libraries. For this reason, they generally consider research data curation and management as a new challenge, a kind of new frontier for the development of their campus services, either on a local level or as part of a scientific network (CLIR 2013). For the same reason, we started to work on the topic from 2013 on. Empirical results and recommendations are based on our research at the University of Lille 3, a large social sciences and humanities campus in the Northern part of France, with 19,000 students and nearly 500 PhD candidates in three graduate schools and 55 doctoral degrees. The project is going on.

Sources and types of data

In order to find out more about the data deposited by PhD students, we conducted a survey on 283 dissertations from 1987 to 2013 from the University of Lille 3, covering nearly the whole range of all disciplines on the campus3 and representing about 30% of all dissertations of that period. 88 were digital (31%) and 195 print dissertations (69%). 188 dissertations contain one or more appendices, i.e. documents attached to the end of a dissertation, with some kind of research data (66%)4 . The length of these appendices varies widely, from 5 to 829 pages, with a median of 81 pages, and totalling more than 25,000 pages. All disciplines have appendices with data but some disciplines such as History of Art, Education, Archaeology and Egyptology, "produce" rather large appendices, while others, like Psychology or Philosophy, often contain shorter appendices (Figure 1). For our survey, we tried to distinguish between data sources and research results. This is not always simple, as the concept of research data depends on scientific fields and methods and not clearly discernible from data sources. For instance, are photographs of archaeological inventories primary sources students used for their analysis, or results of their research, or both? Our approach was to identify and describe types of research data that are potentially reusable which means that they may become, together with the dissertation, sources of further research and future research data.

The PhD students used a wide variety of sources for their scientific work. We identified three major data sources, i.e. archives, surveys and interviews and text samples (Figure 2). As for the research data present in the appendices, our survey reveals several different and heterogeneous data types (Figure 3). Again, text samples are the most important data type, followed by spreadsheets, images and drawings, maps, photographs, statistics, graphs, databases and timelines (chronologies). We only found one dissertation with audio-visual media (recorded interviews) and we have not found any dissertation with geolocation data. In some disciplines, one or two data types are predominant. This is the case in Philosophy, Linguistics and Foreign Languages and Literature where text samples represent more than half of the data. Other disciplines are characterized by a wide number of different types of research data. Some examples: History: ten different data types, including text (21%), tables (15%) and images (14%). Information and Communication Sciences: ten different data types, including text (33%), tables (17%) and graphs (13%). Some data types are present in all disciplines, like text samples, images, tables or graphs and figures. Others, in particular inventories or audio-visual material, are at least in our sample specific for one or two disciplines.

Medium and format of data

While 30% of the print dissertations clearly separate text and appendices in different volumes, digital dissertations do not often separate text and appendices but glue them together into the same file (52%). Also, some dissertations have poor or no table of contents for their appendices. All files of digital PhD dissertations must be deposited with the text, and the French national computer centre for Higher Education5 maintains a list of accepted file formats for long term preservation6 . However, nearly all files in our survey are in PDF (image or text), and other formats are very rare. For instance, in our sample only one dissertation has been submitted with video and audio files on CD-ROM. Other data are available on a distant web site or deposited on compact disc, DVD or USB flash drive. Dissertations in History, especially for studies on historical social groups, sometimes contain detailed and well-structured biographical information, presented like a database. One example for this "prosopographical" approach: a dissertation on the Renaissance elite of the old Flemish town of Douai7 with biographical records of 423 aldermen, with structured information about, among others, place and date of birth, date of death, mandate period, noble titles and occupation.

Data management

Many, if not all of these data could be of real value for further research. These data could be used to create image databases, digital maps collections or digital libraries with manuscripts, archival material and other text samples open for text mining tools. Results from experiments and surveys could be published in a way that allows for reuse, data mining and automatic meta-analysis on different datasets. Research results could thus become new data sources and generate further research. However, this potential reuse requires data management and curation to remain accessible and interpretable over time, including metadata and long-term preservation (Neuroth et al. 2013). For young scientists and PhD students, learning how to design and implement a data management plan (DMP) is even more important in so far as more and more funding bodies evaluate the existence and quality of DMPs in research project proposals. Our empirical data do not tell us if the PhD students conducted a data management plan. But only few dissertations demonstrate a real effort of data management and curation. In particular, our study reveals three barriers to open data: Incomplete, inadequate or missing description of the whole datasets and/or individual data.

7

Missing organisation. Research data are presented without any structuration or organisation, often together with other, not reusable material in a kind of information mash-up not suitable for further research. Inadequate format. Data and text are glued together in a PDF file instead of being separated and published in adequate file formats.

In a second survey on research data management and sharing at the University of Lille 3 (Prost & Schöpfel 2015), PhD students represented 33% of the whole sample of 270 scientists. Compared to professors, senior lecturers etc., they have less experience with data management. They all store their data on the hard disks of their personal computers, sometimes also on a computer at the research laboratory or department, with back-ups on an external device like hard drive, USB flash drive or DVD, and sometimes even in the cloud (Dropbox). This is more or less personal knowledge management, good enough for personal research work and small projects but not compatible with larger research projects, such as the European H2020 programme. Also, they do not delegate this management. The Lille PhD students are not really different compared to other universities, as other survey results show8 -many PhD students are interested in data management and to some extent in support of sharing at least some data but have little or no experience at all.

Data sharing

Our survey on research data at the University of Lille 3 confirms that PhD students have less experience with data sharing, which is not surprising as they are at the very beginning of their scientific career. More than other scientists, they often simply do not know options and opportunities for the deposit and sharing of their research results. Yet, 30% of them declare that other persons of their research team have access to their own data. This is a basic way of data sharing, not on the Internet but on their computers or via flash drives, Dropbox, the University Intranet etc. Also, they are more interested in reuse of data from other researchers than other categories.

Nearly one third (28%) of the students do not want to make their data available in the future or at least hesitate, which is the same part as for other scholars and researchers. Yet, they show a significantly higher motivation to deposit their research results in a data repository (63% compared to 43%), even in a local repository (laboratory, department) while the other scientists clearly prefer international and domain-specific sites. When asked which kind of service they would need, they ask for technical advice and help for data management plans for the publishing of their results.

More than the elder staff, they also ask for assistance in ethical and legal issues. As a matter of fact, privacy issues and third party copyright are two serious legal problems that need awareness. Our survey on PhD theses reveals two issues: Some appendices contain personal data, about living or dead people, historical persons or unknown (anonymous) people. These may be survey data, experiments, interviews, biographies etc. In so far as the information allows identifying individual persons, they need special processing and careful handling.

8th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library of Technology, 2015Technology, [cit. 2015-12-15]-12-15]. Available from: http://nrgl.techlib.cz/index.php/Proceedings. ISSN 2336-5021. 8 Some dissertations contain material that is protected by copyright and cannot be reproduced or disseminated without authorization, even by fair use or copyright exceptions (short citation, research…). These may be text samples, maps, photographs, copies from books etc.material not created by the PhD student him/herself. These problems should be addressed as a part of PhD education on data management, well ahead of decisions on preservation and dissemination.

Recommendations

Advice and assistance will be necessary for PhD students to prepare their data in an adequate way. Adequate means at least: Clear separation of text and data. Digital research data must be submitted in different and separate files.

Structuration of the research data, with a detailed and organized tagging (markup) of the datasets.

Metadata of good quality. The data must be described in a standard language and format, with sufficient detail for retrieval and data mining.

Deposit in original format. Data should be submitted in their original and if possible, open format (and not in PDF), to facilitate long-term preservation and reuse.

Clearing of privacy and copyright issues.

The empirical evidence of this study suggests that assistance and advice for PhD students to help them manage their research data must go beyond general rules and recommendations. Not all doctoral projects produce research data. Not all data are submitted with the dissertation to back up the research in the dissertation or to further explain and clarify the matter. Not all data can be reused especially, but not only, for legal reasons. And finally, even if our sample is not representative, it seems obvious that many characteristics of data sources and types have strong relationships with disciplinary methods, topics and approaches.

Following the work of [START_REF] Reznik-Zellen | Tiers of research data support services[END_REF] at the University of Massachusetts Amherst, we develop three tiers of research data support services for PhD students on our campus, including education, consultation and infrastructure (Figure 4).

Figure4 Research data support services

Education: We already organized three events on research data especially designed for PhD students in social sciences and humanities, and we will launch a first doctoral seminar in October 2015 on data management and sharing. In the future, we will edit or adopt guidelines and make them available for the PhD students, together with frequently asked questions and updates on data management, open data etc.

Advice and assistance: Probably as a part of the future Learning Centre of the University of Lille 3, we will develop personalized help and assistance for PhD students, able to provide answers and advice to their specific questions and problems.

Infrastructures: Our approach is based on intermediation, not on research and development. Even if we will probably develop some basic tools for temporary storage and metadata on our campus, our main idea is to partnership with existing data networks and repositories, including agreements if necessary and delegation of the deposit.

These three tiers of research data support services will be launched progressively between 2015 and 2018. Their development will follow five guiding principles (Figure 5). 10 One size does not fit all. Research data support services must be flexible and adjusted to the scientific disciplines and domains of the PhD research. This means a very good knowledge of research methodologies, data types, formats etc. but also a good cooperation with the research teams, large projects and laboratories.

Data management and sharing must become part of the mandatory doctoral education syllabus, such as project management, scientific writing or data analysis.

The University of Lille 3 will develop its own templates for data management plans, in line with social sciences and humanities and be compatible with the criteria of the European research projects.

Deposit of research data along with PhD dissertations should become near to mandatory. At least, there should be strong incentives to submit those data for temporary storage and long term preservation.

Finally, as mentioned above, our University will contribute to the preservation and dissemination of these research datanot necessarily with campus-based infrastructures (they are not excluded, though) but rather through partnerships and networking with local or national providers. We are already doing so in the field of open access, with good success, as our institutional repository is hosted by the Lyon-based CCSD 9 and part of the national open repository HAL 10 .

The academic library, already present and engaged in ETD management and open access, will be a leading partner for these new research data support services, in cooperation with the graduate school and the research laboratories. Nevertheless, this leading position must become legitimate and accepted by the scientific community and the PhD students. So far, following our campus survey on data management and sharing, scientists and students have not identified the academic library as a potentially useful structure for their data. In other words, the implementation of the new services must be accompanied by communication about the role and usefulness of each partner, and by the acquisition of new skills and knowledge by the information professionals.

  4

Figure 1

 1 Figure 1 Size of appendices (circle size = number of dissertations, N=188)

Figure 2

 2 Figure 2 Data sources per dissertations (N=188)

Figure 3

 3 Figure 3 Data types, per dissertations (N=188)

Figure 5

 5 Figure 5 Five leading principles for the implementation of research data support services

http://nrgl.techlib.cz/index.php/Proceedings. ISSN

  8th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library ofTechnology, 2015Technology, [cit. 2015-12-15]-12-15]. Available from:

	2336-5021.
	Psychology: nine different data types, including tables (29%) and statistics (28%).
	Archaeology and Egyptology: nine different data types, including photographs (21%),
	maps (17%) and images (17%).
	6

http://nrgl.techlib.cz/index.php/Proceedings.

  Grey Literature and Repositories: proceedings [online]. Prague: National Library ofTechnology, 2015Technology, [cit. 2015-12-15]-12-15]. Available from: ISSN 2336-5021.

	8th Conference on
	• Seminars, conferences, training	• Legal and technical help (data management plan) • Technical assistance for	• Mediation for deposit • Partnership with
	• Online resources (guidelines, FAQ...)	deposit • Liaison with laboratories	networks and repositories
	• Alert service		• Development of tools
			on the campus
	Education	Advice, assistance	Infrastructures

In the following we shall use the term "PhD dissertation" to designate the document submitted in support of candidature for the academic degree of doctorate, as synonym for "PhD or doctoral thesis".8th Conference on Grey Literature and Repositories: proceedings [online]. Prague: National Library of Technology,

 [cit. 2015-12-15]. Available from: http://nrgl.techlib.cz/index.php/Proceedings.ISSN 2336-5021.

http://educopia.org/research/grants/etdplus

In our sample, History, Psychology, Philosophy, Foreign Languages and Literature (English and American, Spanish, Slavonic, Hebrew…), Information and Communication Sciences (including Library Sciences), History of Art, Linguistics, Archaeology and Egyptology were the most represented disciplines.

NB: some pages contain empty questionnaires or survey forms, experimental procedures, bibliographies etc. which cannot be considered as data.

CINES https://www.cines.fr

https://www.cines.fr/archivage/des-expertises/expertise-formats/liste-des-formats-archivables/

Duquenne, F. (2011). Un tout petit monde : les notables de la ville de Douai du règne de Philippe II à la conquête française (milieu du XVIe siecle-1667) : pouvoir, réseaux et reproduction sociale. Université de Lille 3.

See for instance[START_REF] Simukovic | Unveiling Research Data Stocks: A Case of Humboldt-Universität zu Berlin[END_REF] and a recent, unpublished survey from the University of Strasbourg.

8th Conference on GreyLiterature and Repositories: proceedings [online]. Prague: National Library ofTechnology, 2015Technology, [cit. 2015-12-15]-12-15]. Available from: http://nrgl.techlib.cz/index.php/Proceedings.ISSN 2336-5021.