Claire Le Paven

Ratiba Benzerga

Anthony Ferri

Didier Fasquelle

Vincent Laur

Laurent Le Gendre

Florent Marlec

Franck Tessier

François Cheviré

Rachel Desfeux

S Saitzek

X Castel

A Sharaiha

Ferroelectric and dielectric study of strontium tantalum based perovskite oxynitride films deposited by reactive rf magnetron sputtering

Introduction

The perovskite oxynitride compounds have received much attention in recent years due to their original properties compared to their oxide parents. For example, the decrease of the band-gap consecutive of the nitrogen substitution for oxygen in the perovskite structure leads to an absorption in the visible domain, with potential applications as pigments [START_REF] Jansen | Inorganic yellow-red pigments without toxic metals[END_REF][START_REF] Aguiar | The vast colour spectrum of ternary metal oxynitride pigments[END_REF] and visible photocatalysts, for instance, in overall water splitting reactions [START_REF] Maeda | Oxy)nitrides with d0-electronic configuration as photocatalysts and photoanodes that operate under a wide range of visible light for overall water splitting[END_REF]. Moreover, high permittivities have been reported. For the SrTaO2N compound, closest reported material for comparison with our results as explained hereafter, Kim et al. first reported a permittivity  = 2800 @1MHz,RT on 55% dense ceramics [START_REF] Kim | Characterization of the structural, optical, and dielectric properties of oxynitride perovskites AMO2N (A = Ba, Sr, Ca; M = Ta, Nb)[END_REF]. Zhang et al. confirmed this trend with  = 10000 @1MHz,RT on a 90% dense ceramic post-annealed in ammonia [START_REF] Zhang | Sintering and dielectric properties of perovskite SrTaO2N ceramics[END_REF]. Recently, Sun et al. reported  = 355 @1MHz,RT for a 84% dense pellet densified with SrCO3 and post annealed in ammonia [START_REF] Sun | Additive sintering, post-annealing and dielectric properties of SrTaO2N[END_REF]; no drastic change of permittivity was observed in the temperature range 20 -160°C. On thin films, data are dramatically scarce. Oka et al. reported a permittivity close to 2000 @10 kHz,RT for a 290 nm-thick epitaxial film [START_REF] Oka | Possible ferroelectricity in perovskite oxynitride SrTaO2N epitaxial thin films[END_REF]; no significant change was observed upon cooling the films down to 50 K and varying frequency from 20 Hz to 100 kHz. The high values of permittivity observed on perovskite oxynitrides are currently assigned to a possible local oxygen/nitrogen ordering with a cis-type configuration of the nitrogen atom in the MO4N2 octaedra (M: transition metal, lanthanide) [START_REF] Yang | Anion order in perovskite oxynitrides[END_REF][START_REF] Zhang | Local anionic ordering and anisotropic displacement in dielectric perovskite SrTaO2N[END_REF]. A local ferroelectric behavior has been demonstrated on SrTaO2N, as bulk [START_REF] Kikkawa | Ferroelectric response induced in cis-type anion ordered SrTaO2N oxynitride perovskite[END_REF] and thin films [START_REF] Oka | Possible ferroelectricity in perovskite oxynitride SrTaO2N epitaxial thin films[END_REF].

The aim of our research is to provide experimental data on the oxynitride perovskite compounds, especially on their dielectric and ferroelectric properties. Moreover, we are looking for high permittivity materials for integration in miniaturized/agile devices such as antennas. Given the great difficulty of densifying oxynitrides ceramics, our approach has turned to the synthesis of these compounds in the form of thin films, as reported in other studies [START_REF] Kim | Epitaxial thin-film deposition and dielectric properties of the perovskite oxynitride BaTaO2N[END_REF][START_REF] Marozau | Pulsed laser deposition and characterisation of perovskite-type LaTiO3xNx thin films[END_REF]. We have conducted research on LaTiO2N films deposited by reactive sputtering from oxynitride LaTiO2N [START_REF] Le Paven-Thivet | Oxynitride perovskite LaTiOxNy thin films deposited by reactive sputtering[END_REF] and oxide La2Ti2O7 [START_REF] Lu | Reactive sputtering deposition of perovskite oxide and oxynitride lanthanum titanium films: structural and dielectric characterization[END_REF] targets. Recently, our interest has been focused on the deposition of films from the solid solution (1-x) Sr2Ta2O7, xLa2Ti2O7 [START_REF] Le Paven | Growth of (Sr,La)-(Ta,Ti)-O-N perovskite oxide and oxynitride films by radio frequency magnetron sputtering: Influence of the reactive atmosphere on the film structure[END_REF]. The oxide precursors are both ferroelectric materials with Curie temperatures of -107°C for Sr2Ta2O7 [START_REF] Nanamatsu | A new ferroelectric: La2Ti2O7[END_REF] and 1500°C for La2Ti2O7 [START_REF] Yan | Piezoelectric ceramics with superhigh Curie points[END_REF]. According to the results of Nanamatsu [START_REF] Nanamatsu | A new ferroelectric: La2Ti2O7[END_REF], it is expected that the (Sr0.99La0.01)2(Ta0.99Ti0.01)2O7) oxide is ferroelectric with a Curie temperature close to room temperature. We have shown that in reactive Ar + O2 + N2 plasmas, oxide films are obtained for plasmas containing dioxygen and dinitrogen whereas oxynitride (Sr0.99La0.01)(Ta0.99Ti0.01)O2N (SLTTON) films are produced only in Ar + N2 plasmas, that is without O2 [START_REF] Jacq | Deposition and dielectric characterization of strontium and tantalum-based oxide and oxynitride perovskite thin films[END_REF].

The purpose of the present contribution is to provide a complete dielectric and ferroelectric characterization of oxynitride SLTTON perovskite films. The first series of films is composed of oxynitride samples deposited on conducting Nb-doped SrTiO3 substrates for the nanoscale Piezoelectric Force Microscopy (PFM) analysis and for the low frequency dielectric characterization using Metal Insulating Metal (MIM) structures. This series also concerns films with different thickness in order to evaluate the effect of a possible crystallographic strain on the dielectric properties of oxynitride films. The second series contains samples deposited on MgO substrates for the high frequency dielectric characterization, using metallic transmission lines and a resonant cavity.

Materials and methods

Film deposition was carried out using radio frequency (rf) reactive magnetron sputtering in a Plassys MP450S reactor using a homemade (Sr0.99,La0.01)2(Ta0.99,Ti0.01)2O7 (SLTTO) target. This one, 75 mm in diameter, was obtained by uniaxial compaction of SLTTO powder under 80 MPa. The oxide powder was synthetized by solid state chemical route from stoichiometric amounts of SrCO3, Ta2O5, La2O3 and TiO2 heated under air at 1400°C during 30 h. X-ray diffraction (XRD) analysis confirms the crystallization of the powder according to a Sr2Ta2O7 analogue (see Supplementary Data 1).

For the sputtering deposition, the plasma gas mixture contained 92.3vol.% of argon and 7.7vol.% of dinitrogen, with a total pressure pT = 40 mTorr. The rf power was fixed at Prf = 90 W (2.04 W/cm²) with a target-substrate distance d = 5 cm. The substrate temperature was maintained at TS = 750°C. Before deposition, a pre-sputtering of the target was performed during 30 min under the reactive gas mixture. The films were cooled under the same gas mixture than deposition at 10°/min; no post-annealing was performed. Films of the present study were deposited using the same sputtering conditions, except the duration of depositions in order to access different thickness of samples. Two series of SLTTON samples were prepared: a first one for the Piezo-force microscopy measurements and for the dielectric characterization in the low frequency range, a second one for the dielectric characterization in the high frequency range.

The first series contains three films with thickness t = 20; 300; 1600 nm. There were deposited on conducting niobium doped (001) Nb:SrTiO3 substrates, acting as bottom electrodes in the MIM structures. Twin films deposited on (001) MgO substrates have served for the UV-visible transmittance characterization of samples giving the band gap of materials, as well for the thickness measurement by scanning electronic microscopy (SEM). SLTTON films of the first series are named relatively to their thickness.

The second series contains two films of the same thickness (1520 nm) deposited on (001) MgO substrates for the high frequency dielectric characterization. SLTTON-Hf1 was measured as function of frequency using transmission lines, whereas SLTTON-Hf2 was used for the precise determination of the dielectric losses via a measurement at a fixed frequency in a resonant cavity. SLTTON-Hf2 is a special sample, divided in two parts as can be seen in Supplementary Data 2: a 10 x 10 mm 2 area made of the MgO substrate alone for an "unloaded" measure; a 10 x 10 mm 2 area made of the SLTTON film on MgO for a "loaded" measure.

Thickness of layers was measured by SEM cross-sectional view using a JEOL 5440 SEM operating at 10 kV. Nanoscale experiments on films were conducted using a MFP-3D (Asylum Research) atomic force microscope (AFM) in contact mode under ambient conditions. Surface morphology was characterized by using Pt/Ir coated silicon tips (Arrow CONTPt, NanoWorld).

X-ray 𝜃 -2𝜃 and 𝜙 diffractograms were recorded using a Seifert XRD 3003 diffractometer with a copper anode (Cu K 1 radiation). UV-Visible transmittance spectra were collected by a Perkin-Elmer Lambda 20 spectrometer operating in the range [200-1100] nm.

Assuming direct transitions, band-gap values were obtained using the relation:

(𝛼ℎ𝜈) 2 = (ℎ𝜈 -𝐸 𝑔 )
where ℎ𝜈 is the photon energy. Hence plotting (𝛼ℎ𝜈) 2 as a function of ℎ𝜈, the extrapolation of the linear region at zero gives the value of 𝐸 𝑔 [START_REF] Kubelka | Ein beitrag zur optik der farbanstriche[END_REF]. The error is estimated as 0.05 eV.

PFM measurements were performed with Pt/Ir coated probes (PPP-EFM, NanoSensors) and stiff cantilevers (k ~ 4 N/m) to minimize electrostatic contribution in the detected PFM signal. Dual AC resonance tracking (DART) mode of the PFM was employed in order to enhance the piezoelectric signal [20], while remnant piezoresponse loops, i.e. at zero DC bias field, were recorded to promote the electromechanical response. For these spectroscopic PFM measurements, the driving voltage was set to 1.5 V at the probe tip and the conductive substrate was grounded.

Low frequency dielectric properties were measured at room temperature by a LCR meter (LCR-819 GWInstek) on MIM structures. The conducting substrate (Nb:SrTiO3) was used as bottom electrode, whereas 200 nm sputtered silver served as the top electrodes. A stabilized Keithley 2400 supply was used to provide DC electric fields in order to evaluate the variation of the permittivity of films as function of electric bias (i.e. agility of samples).

The extraction of the microwave properties of the oxynitrides films was done by using two different methods: transmission lines and a resonant cavity. The transmission line method consists in measuring the reflection S11 and the transmission S21 parameters of a charged coplanar line (silver line on the MgO substrate covered by the oxynitride dielectric film).

Effective permittivity and losses of this multilayer structure are then determined. The intrinsic characteristics of the dielectric film are further extracted using the "conformal mapping method" developped by Carlsson and Gevorgian [START_REF] Carlsson | Conformal mapping of the field ans charge distributions in mulilayered substrate CPWs[END_REF]. Measurements were made using a HP8510C network analyzer associated to a Signatone H100 probe station, from 1 to 20 GHz.

Sputtering silver metallization (2 m Ag and 5 nm Ti as adhesion layer) on the oxynitride/MgO bilayer followed by a standard photolithographic wet etching process (HNO3/H3PO4/CH3COOH/H2O solution for silver and HF/H2O solution for Ti) were used to define the metallic patterns of the transmission lines.

The cavity method proved to be appropriate to characterize thin dielectric films [START_REF] Queffelec | Intercomparison of Permittivity Measurement Techniques for Ferroelectric Thin Layers[END_REF],

giving very accurate values of the microwave dielectric properties of materials [START_REF] Laur | Cavity-perturbation method improvement by a numerical calibration process[END_REF]. Here, it was employed to extract precise values of dielectric losses. However, on the contrary to transmission line methods, it is limited to a single frequency which depends on the size of the cavity. In this study, we used a cavity constituted by a 5-cm long WR-90 waveguide coupled by circular apertures. A non-radiating slot, placed in the middle of the waveguide section, allows inserting the material under test. A low perturbation approach is used to extract the dielectric properties of the thin film under study. At first, an MgO substrate is inserted in the cavity; this "unloaded" measurement will constitute the reference. Then, a bilayer oxynitride film/MgO is placed in the cavity for a "loaded" measurement. The changes of resonance frequency and quality factor thus induced allow extracting the complex permittivity of the dielectric film, further the dielectric losses. Main error source comes from the difference of dimensions and/or dielectric properties between the MgO substrate for the "unloaded" measurement and the MgO substrate on which the dielectric film is actually deposited. To get around this problem, we deposited the oxynitride dielectric film on half of a 20-mm long substrate (as shown in Supplementary data 2). This ensures that the two parts of the MgO substrate have undergone the same deposition process, particularly the heating at TS = 750° C.

Results and Discussion

The characteristics of the studied films are detailed in Table 1.

Structure of SLTTON films

The UV-visible transmittance spectrum of the twin film deposited on MgO substrate in the same run than the SLTTON-1600nm sample is shown in Figure 1, with, in insert, the extrapolation giving the band gap value (Eg = 2.35 eV). The fringes above the absorption threshold are related to interferences created at the air/film and film/substrate interfaces and depend on the thickness, roughness and density of film. Here, the fringes are tightened because of the high thickness (1600 nm) of the sample. Band gap values of other films are given in Table 1 and are around the average value Eg ~2.30 eV. This is very similar to the reported values on the closest oxynitride material, SrTaO2N, with, for example, Eg = 2.27 eV for films [START_REF] Oka | Possible ferroelectricity in perovskite oxynitride SrTaO2N epitaxial thin films[END_REF] and Eg = 2.30 eV for bulk [START_REF] Kikkawa | Ferroelectric response induced in cis-type anion ordered SrTaO2N oxynitride perovskite[END_REF].

The films of the first series are phase pure according to XRD, with peaks indexed as an oxynitride isotype to SrTaO2N (Figure 2, with the indexation of peaks made considering a pseudo-cubic cell). The Nb-(001) SrTiO3 single-crystalline substrate has been chosen because of its low crystallographic mismatch with SrTaO2N (+ 3.44 %) suitable for a textured or epitaxial growth of deposited films. XRD analysis show that the films of the first series are epitaxially grown with the occurrence of intense (00l) peaks in the  diffractogramms (Figure 2) and a four-fold symmetry of peaks in -scans (for example, for SLTTON-1600nm in Supplementary Data 3) confirming the cube-on-cube growth of the oxynitride compound on the substrate.

Cell parameters were extracted from the  patterns recorded at  = 0° (out-of-plane c parameter) and  = 45° (in-plane a parameter) and the ratio c/a was calculated. Ratios c/a of the thick films (1600 and 300 nm) are similar (respectively, 1.000 and 1.002) and close to the reported value for the SrTaO2N bulk material (c/abulk ~ 1.002 [START_REF] Kikkawa | Ferroelectric response induced in cis-type anion ordered SrTaO2N oxynitride perovskite[END_REF]). Ratio is clearly expanded for the thin SLTTON-20nm sample with a value c/a = 1.008. It means that the latter is strained, with an elongation of the c parameter in the direction of growth (c = 4.088 Å / cbulk = 4.039 Å) and a (slight) tensile strain of the film in its plane (a = 4.054 Å / abulk = 4.0306 Å ).

AFM images of films of the first series are presented in Figure 3. We observe a nanostructured surface for each film, with an average grain size ranging from 30 to 100 nm for the two thinnest films and from 10 to 70 nm for the thicker one. The root mean square roughness for a 10 × 10 μm² scan area was measured to be around 1.5 nm for the 20 nm-thin film and 3.6 nm for the two thicker films. The surface morphology of SLTTON-1600nm consists in dense circular grains while the thinnest films present irregular shapes. The white grains of SLTTON-20nm are actually indicative of a more pronounced peak to valley roughness of this sample; porosities are also visible.

The films of the second series, deposited on MgO, are phase pure and identified as an isotype of SrTaO2N (Supplementary Data 4) as previously; they are just (00l) textured, certainly due to the higher mismatch between SLTTON and this substrate (-4.07 %) compared to the Nb-SrTiO3 substrate (+3.44 %). As their thick homologs on the latter substrate, their ratios c/a (1.000) are close to the reported value for bulk, demonstrating fully relaxed films in accordance with their high thickness (1520 nm).

Piezo-force microscopy characterization

Figure 4 presents local remnant piezoresponse loops measured over the surface of the films deposited onto Nb-doped SrTiO3 substrates. Regardless the films thickness, both inphase and in-amplitude PFM loops are obtained. Indeed, phase loops display a square hysteretic behavior where two stable polarization states separated of about 180° are acquired, which reveal the local switching of the probed domains. From amplitude signals, typical butterfly-shape is observed traducing piezoelectric activity under the AFM tip. Besides, an asymmetry in the tip vibration-amplitude along the vertical axis is seen. This can be explained by the different work functions of the probing tip (Ti/Ir) and the conductive substrate (Nb-SrTiO3), leading to an internal bias field [START_REF] Hong | Fabrication and investigation of ultrathin, and smooth Pb (Zr, Ti)O3 films for miniaturization of microelectronic devices[END_REF]. In addition, we also note a shift toward negative voltage values along the DC bias axis, which can be related to an imprint behavior of the local switching [START_REF] Gruverman | Asymmetric nanoscale switching in ferroelectric thin films by scanning force microscopy[END_REF][START_REF] Ferri | Thickness dependence of the nanoscale piezoelectric properties measured by piezoresponse force microscopy on (111)-oriented PLZT 10/40/60 thin film[END_REF] consistent with the presence of an internal built-in electric field [START_REF] Lichtensteiger | Tuning of the depolarization field and nanodomain structure in ferroelectric thin films[END_REF].

Consequently, from these local experiments, both switching behavior and electromechanical response evidence the local piezo-/ferroelectric nature of the oxynitride SLTTON thin films, as recently demonstrated by D. Oka et al [START_REF] Oka | Possible ferroelectricity in perovskite oxynitride SrTaO2N epitaxial thin films[END_REF]. exhibit an extremely low decay of  and tan as frequency increases, with permittivities equal to 30 and 55 at 10 kHz, respectively. The low permittivity of the SLTTON-20nm sample may be related to defaults located at the interface substrate/film, emphasized in the dielectric response of the sample due to its very low thickness. SLTTON-1600nm shows an abrupt decrease of  in very low frequencies, from 100 to 1500 Hz (the logarithm scale allows to see this decrease but enhances it disproportionately regarding the whole frequency range). This is due to the existence of space-charge regions in which free carriers are diffusing; these entities may be related to nitrogen species. The permittivity of SLTTON-1600nm becomes nearly constant above 1500 Hz; at 10 kHz,  = 175. Dielectric losses tan reach a stable value around 0.0075 above 20 kHz. This very thick film thus exhibit better dielectric characteristics (higher  and lower tan) than the thinnest of the series. Indeed, this sample exhibits unique features: a band gap (2.35 eV) characteristic of the formation of a perovskite oxynitride, a textured growth with a c-axis orientation and a very smooth surface in view of its very high thickness. The evolution of  and tan with frequency however restricts its use in the upper range of low frequencies.

At 100 kHz, the permittivity ( = 175) of the SLTTON-1600nm film is higher than that obtained on an oxide perovskite Sr2Ta2O7 film ( = 95), polycrystalline and deposited by sol-gel route [START_REF] Kato | Surface morphology and dielectric properties of alkoxy-derived Sr2Ta2O7 and Sr2(Ta,Nb)2O7 thin films[END_REF]. This feature has always been evidenced for perovskite oxynitride materials compounds compared to their oxide parents, in the form of bulk [START_REF] Kim | Characterization of the structural, optical, and dielectric properties of oxynitride perovskites AMO2N (A = Ba, Sr, Ca; M = Ta, Nb)[END_REF] as well as thin films [START_REF] Kim | Epitaxial thin-film deposition and dielectric properties of the perovskite oxynitride BaTaO2N[END_REF].

It is besides what has developed the strong enthusiasm for the perovskite oxynitride materials. However, it is for us to recognize that the values achieved in the present study do not reach the values observed on SrTaO2N compound:  = 750 for bulk [START_REF] Kikkawa | Ferroelectric response induced in cis-type anion ordered SrTaO2N oxynitride perovskite[END_REF] and  = 2000 for film [START_REF] Oka | Possible ferroelectricity in perovskite oxynitride SrTaO2N epitaxial thin films[END_REF] (@10 kHz, RT). In the latter study, the high permittivity was explained by the high compressive strain attained in the film sample due to its epitaxial (001) growth on the Nb-(001)SrTiO3 substrate. It was proposed an ordering of the nitrogen atoms along the c-axis of the perovskite cell, hereafter identified as a trans-type ordering. Classical type ferroelectricity was assigned to this structuration and held responsible for the observed high permittivity. This compressive strain was associated to very high ratios c/a, for example c/a = 1.026 for a 20nm-thin film. In our case, we have determined a ratio c/a = 1.008 for the SLTTON-20nm sample, which denotes a moderate strain. It is certainly too low to impact significantly the anion ordering and so, the dielectric properties. Nevertheless, the permittivity obtained here for the thickest film ( = 175) may be related to the value obtained on bulk SrTaO2N material (= 750 [START_REF] Kikkawa | Ferroelectric response induced in cis-type anion ordered SrTaO2N oxynitride perovskite[END_REF]) considering that permittivities of films are generally lowered compared to bulk due to smaller grain sizes [START_REF] Petzelt | Ferroelectric soft modes in ceramics and films[END_REF]. It is besides very close to the value  = 200 (@100 kHz, RT) obtained on an epitaxial BaTaO2N film deposited by laser ablation [START_REF] Kim | Epitaxial thin-film deposition and dielectric properties of the perovskite oxynitride BaTaO2N[END_REF].

Figure 6 presents the evolution of the permittivity and dielectric losses of the above samples as function of an applied dc electric field. No variation of the permittivity is seen, which significates no agility of these oxynitride samples at a macroscopic scale. The maximum applied fields are 600 kV/cm (not seen on the graph for clarity reason), 31 kV/cm and 62 kV/cm, respectively for the SLTTON-20nm, -300nm and -1600nm samples. The first value is very high and emphasizes a more insulating character of the very thin film compared to the others. This sample has also a lower permittivity ( = 30). So, despite a band gap value of 2.05 eV, we suggest that this sample actually contains an insulating oxide contribution which may come from the diffusion of oxygen atoms from the SrTiO3 substrate at the high temperature used during deposition (TS = 750°C) [START_REF] Schneider | The origin of oxygen in oxide thin films: role of the substrate[END_REF]. Concerning its losses, they experience an increase in positive applied electric fields, due to the difference of nature of the top (silver) and bottom (Nb-SrTiO3 substrate) electrodes of the MIM structure. The maximum applied fields of 31 kV and 62 kV/cm for thicker oxynitride films are lower than those currently obtained on dielectric and/or ferroelectric perovskite oxide films (for example, [START_REF] Corredores | Study of ferroelectric/dielectric multilayers for tunable stub resonator applications at microwaves[END_REF]). As already reported for bulk [START_REF] Kim | Characterization of the structural, optical, and dielectric properties of oxynitride perovskites AMO2N (A = Ba, Sr, Ca; M = Ta, Nb)[END_REF][START_REF] Ebbinghaus | Perovskite-related oxynitrides -Recent developments in synthesis, characterization, and investigations of physical properties[END_REF] and thin films [START_REF] Kim | Epitaxial thin-film deposition and dielectric properties of the perovskite oxynitride BaTaO2N[END_REF], oxynitride materials are intrinsically more conductive than their oxide parents. This is in connection with the band-gap of oxynitrides, lower than the oxides because of the substitution of nitrogen for oxygen, thus allowing more easily the electrons to move to the conduction band. This will in turn increase the conductivity of the material, hereafter the maximum applied electric fields and the dielectric losses.

Finally, since the samples do no exhibit a variation of their permittivity as function of an electric bias, no macroscopic ferroelectric polarization measurements (P-E) have been initiated.

High frequency dielectric characterization

Figure 7 presents the evolution as function of frequency up to 20 GHz of the permittivity and loss tangent of the SLTTON-Hf1 sample deposited on MgO. Permittivity is stable above 5 GHz, with a value   65 at 6.72 GHz; dielectric losses are less than 0.2 on the entire frequency range. An increase of the permittivity is seen for the lowest frequencies; this is in accordance with the value  = 175 measured via MIM structures on the SLTTON-1600nm film, with thickness similar to that of SLTTON-Hf1.

The uncertainty on the tan value led us to complete our measurements by using a resonant cavity on a similar 1520 nm thick film (SLTTON-Hf2). Supplementary Data 2 presents the amplitude of the transmission parameters of the cavity loaded with a single MgO substrate and with the SLTTON-Hf2/MgO bilayer. The cavity loaded with the MgO substrate showed a resonance frequency of 6.7207 GHz and a quality factor of 4019. When the bilayer is inserted, both resonance frequency (fr = 6.7167 GHz) and quality factor (Q = 3786) are lowered. The application of the low perturbation analysis led to dielectric losses tan1.2.10 - 2 . These are low, which definitively validates the use of the resonant cavity method especially adapted for low loss materials and thin films. We should add that, in transmission lines and resonant cavity methods, the tangential dielectric response of samples is only considered, so that these two methods can act as complementary characterization techniques in high frequencies.

Conclusion

(Sr0.99La0.01)(Ta0.99Ti0.01)O2N films have been successfully deposited by radio frequency magnetron sputtering using an oxide target. The use of a 93.3 %vol. Ar and 7.7 %vol.

N2 reactive plasma allowed synthesizing oxynitride films with band gap values around 2.30 eV. Films were epitaxially grown on Nb-(001)SrTiO3 substrates and textured with a preferential c-axis orientation on (001)MgO substrates. The difference in structural arrangement is due to a lower crystallographic mismatch in the case of the SrTiO3 substrate than in the case of MgO. A series of films with thickness from 20 to 1600 nm reveals very smooth surfaces with root mean square roughness less than 3.6 nm. Piezo-force microscopy demonstrated the local piezoelectric and ferroelectric behavior of the oxynitride perovskite films with the occurrence of butterfly shapes in the piezoelectric response and hysteresis curves in the ferroelectric response. The butterfly shape was not evidenced at a macroscopic scale in capacitanceelectric field measurements (@10 kHz,RT). The thicker sample (1600 nm) exhibits a permittivity around 175 with losses tan = 0.015. The evolution of its permittivity is stable above 1500 Hz, with, below, an increase of the permittivity related to the existence of space charge regions. The thin sample (20 nm) experiences a moderate crystallographic strain but does not exhibits a particular high permittivity. In high frequencies, at 6.72 GHz, the permittivity of a 1520 nm-thick oxynitride films is around 65, with dielectric losses of 0.012.

List of

Figure 5

 5 Figure 5 presents the evolution as function of the frequency from 100 Hz to 100 kHz of the permittivity  and the dielectric losses tan of the films of the first series with different thickness deposited on Nb-SrTiO3 substrates. One can see a different behavior of the thicker film (SLTTON-1600nm) compared to the other two. SLTTON-20nm and SLTTON-300nm

Figure 1 .

 1 Figure 1. UV-visible transmittance spectrum of the twin film (orange in color, deposited on MgO substrate) of the SLTTON-1600nm sample. The extrapolation, giving the band gap, is shown in insert.

Figure 2 .

 2 Figure 2.  diffractogramms of SLTTON films deposited on Nb-(001)SrTiO3 substrates by reactive sputtering with different thickness. Indexation is made according to a pseudocubic cell of SLTTON, with (Ag) related to the metallization for MIM measurements, () to the substrate and (*) to the sample holder.

Figure 3 .

 3 Figure 3. AFM surface morphology of the (a) 20-, (b) 300-and 1600-nm-thick SLTTON films.

Figure 4 .

 4 Figure 4. Nanoscale piezo-/ferroelectric response of the SLTTON films. Local phase and amplitude piezoresponse loops measured over the surface of the films with thickness of (a) 20, (b) 300 and (c) 1600 nm.

Figure 5 .

 5 Figure 5. Evolution as function of the frequency of: (a) permittivity  and (b) dielectric losses tan of SLTTON films with different thickness deposited on Nb-SrTiO3 substrates by reactive sputtering.

Figure 6 .

 6 Figure 6. Evolution as function of an applied dc electric field of: (a) permittivity () and (b) loss tangent (tan) of SLTTON films with different thickness deposited on Nb-SrTiO3 substrates by reactive sputtering.

Figure 7 .

 7 Figure 7. Evolution as function of frequency of: (a) permittivity and (b) loss tangent of the SLTTON-hf1 film deposited on MgO substrate by reactive sputtering.

Figure 1 .

 1 Figure 1.

Figure 2 .

 2 Figure 2.

  Figure 3.

Figure 5

 5 Figure 5.

Figure 7

 7 Figure 7.

Acknowledgments

Authors gratefully acknowledge S. Jacq for scientific and technical assistance. This work was supported by the Syndicat de Gestion du Pôle Universitaire de Saint Brieuc and Saint Brieuc Agglomération (France). The "Région Nord-Pas de Calais" and the "Fonds Européen de Développement Régional (FEDER)" under the "Contrat de Plan État-Région (CPER)" project "Chemistry and Materials for a Sustainable Growth" are also gratefully acknowledged for funding of MFP-3D microscope.


Christian Barillot
email: christian.barillot@irisa.fr

Elise Bannier

Olivier Commowick

Isabelle Corouge

Anthony Baire

Ines Fachfach

Justine Guillaumont

Yao Yao

Michael Kain

Shanoir: Applying the Software as a Service Distribution Model to Manage Brain Imaging Research Repositories

Keywords: Neuroimaging, database, data sharing, neuroinformatics, software as a service (SaaS), cloud computing, web application, Java, web services, shared repositories, centralized resources Digital Imaging and Communications PACS Picture archiving and communication system Sharing Neuroimaging Resources SOAP Simple Object Access Protocol WSDL Web Service

Two of the major concerns of researchers and clinicians performing neuroimaging experiments are managing the huge quantity and diversity of data and the ability to compare their experiments and the programs they develop with those of their peers. In this context, we introduce Shanoir, which uses a type of cloud computing known as software as a service (SaaS) to manage neuroimaging data used in the clinical neurosciences. Thanks to a formal model of medical imaging data (an ontology), Shanoir provides an open source neuroinformatics environment designed to structure, manage, archive, visualize and share neuroimaging data with an emphasis on managing multi-institutional, collaborative research projects. This article covers how images are accessed through the Shanoir Data Management System and describes the data repositories that are hosted and managed by the Shanoir environment in different contexts.

Introduction 1.Context

Two of the major concerns for researchers and clinicians performing neuroimaging experiments are managing the huge quantity and diversity of data and the ability to compare their experiments and the programs they develop with those of their peers. In practice, researchers and clinicians in the neuroimaging field are encouraged to set up large-scale experiments, but the inability to recruit sufficient local subjects who meet specific criteria results in the need for cooperation to gather the relevant imaging data. Pooling experimental results via the Internet and cooperative efforts by centers provide larger and more specific subject populations that expand the scope and value of scientific research.

Searches on distributed neuroimaging databases for similar results and images containing singularities (quirk, peculiarities, …) or the use of data mining techniques may highlight possible similarities. Such efforts also broaden the possible panel of people involved in neuroimaging studies while maintaining the quality of the work. Indeed, the explosion of data generated by the neurosciences community in the early 90s has resulted in the need for innovative techniques for data and knowledge sharing and reuse [START_REF] Mazziotta | A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM)[END_REF][START_REF] Roland | Brain atlases -a new research tool[END_REF][START_REF] Shepherd | The Human Brain Project: neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data[END_REF]. This has led to the emergence of large-scale projects on the human brain. A recent objective added to these initial issues is the application of data analysis and data processing software to various data repository systems for knowledge discovery and data mining, including its more recent extension to merging imaging and genetic data [START_REF] Hibar | Common genetic variants influence human subcortical brain structures[END_REF]. In parallel the development of web applications has stimulated the interest of researchers and clinicians in distributed databases and information sharing.

Background

It is now commonly accepted in the neuroimaging community that sharing data and image processing services will play a crucial role in translational research [START_REF] Barillot | Neurobase: Management of distributed knowledge and data bases in neuroimaging[END_REF][START_REF] Keator | Towards structured sharing of raw and derived neuroimaging data across existing resources[END_REF][START_REF] Poldrack | Making big data open: data sharing in neuroimaging[END_REF][START_REF] Poline | Data sharing in neuroimaging research[END_REF][START_REF] Van Horn | Why share data? Lessons learned from the fMRIDC[END_REF][START_REF] Walport | Sharing research data to improve public health[END_REF]. Research funding agencies now clearly identify the sharing of scientific resources (data processing) as a top priority. International organizations such as the International Neuroinformatics Coordinating Facility (INCF † ) are now dedicated to promoting the field of neuroinformatics [START_REF] Book | Neuroinformatics Database (NiDB)--a modular, portable database for the storage, analysis, and sharing of neuroimaging data[END_REF][START_REF] Kennedy | The Three NITRCs: A Guide to Neuroimaging Neuroinformatics Resources[END_REF]. Sharing data and image processing services for translational research is needed for:

1) the integration of large data sets for population-wide studies and construction of imaging cohorts [START_REF] Barillot | Federating Distributed and Heterogeneous Information Sources in Neuroimaging: The NeuroBase Project[END_REF][START_REF] Evans | The NIH MRI study of normal brain development[END_REF][START_REF] Hall | Sharing heterogeneous data: the national database for autism research[END_REF][START_REF] Jack | The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods[END_REF][START_REF] Marcus | Human Connectome Project informatics: quality control, database services, and data visualization[END_REF][START_REF] Shepherd | The Human Brain Project: neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data[END_REF][START_REF] Van Essen | The WU-Minn Human Connectome Project: an overview[END_REF][START_REF] Van Horn | The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies[END_REF][START_REF] Weiner | The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception[END_REF], 2) the validation of image processing tools on reference datasets for validation and quality control of image processing procedures [START_REF] Menze | The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)[END_REF][START_REF] Styner | 3D Segmentation in the Clinic: A Grand Challenge II: MS lesion segmentation[END_REF], 3) the reuse of image processing pipeline on different sets of data and different peers for sharing processing tools [START_REF] Bellec | The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows[END_REF][START_REF] Dinov | Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline[END_REF][START_REF] Glatard | Interoperability between the CBRAIN and VIP web platforms for neuroimage analysis[END_REF][START_REF] Gorgolewski | Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python[END_REF][START_REF] Keator | Towards structured sharing of raw and derived neuroimaging data across existing resources[END_REF][START_REF] Keator | Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid[END_REF][START_REF] Ooi | CamBAfx: Workflow design, implementation and application for neuroimaging[END_REF], and 4) the validation of research results based on proofed control statistical analysis of images for validation and quality control of experimental research [START_REF] Button | Confidence and precision increase with high statistical power[END_REF][START_REF] Carp | The secret lives of experiments: methods reporting in the fMRI literature[END_REF][START_REF] Ioannidis | How to make more published research true[END_REF][START_REF] Ioannidis | Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention[END_REF]. This is particularly significant in the field of neuroimaging as several large recent multicenter initiatives have shown. These include Evans et al. [START_REF] Evans | The NIH MRI study of normal brain development[END_REF], which performed a study using magnetic resonance imaging (MRI) of normal brain maturation from birth to adulthood in approximately 500 children with behavior disorders, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), which has assembled a very large variety of images for its work [START_REF] Weiner | The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception[END_REF]. The Human Connectome Project (HCP), which worked with 1,200 healthy volunteers to investigate brain connectivity in the normal brain [START_REF] Marcus | Human Connectome Project informatics: quality control, database services, and data visualization[END_REF][START_REF] Van Essen | The WU-Minn Human Connectome Project: an overview[END_REF], is another well-known example illustrating the importance of aggregate imaging data and relating data warehouses to image processing resources.

To provide archiving solutions for large or various multicenter projects, several architectures have already been proposed. The Biomedical Informatics Research Network (BIRN) has been a pioneer in launching brain imaging solutions [START_REF] Ashish | Neuroscience data integration through mediation: An (F)BIRN case study[END_REF][START_REF] Gupta | BIRN-M: A Semantic Mediator for Solving Real-World Neuroscience Problems[END_REF][START_REF] Keator | A national human neuroimaging collaboratory enabled by the Biomedical Informatics Research Network (BIRN)[END_REF][START_REF] Keator | Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid[END_REF]. Another early initiative, the FMRIDC project sought to share task-based fMRI imaging data [START_REF] Van Horn | Why share data? Lessons learned from the fMRIDC[END_REF]. @NeurIST set up a dedicated solution (funded by an Integrated European Project) to support research and treatment of cerebral aneurysms using heterogeneous data, computing and complex processing services [START_REF] Benkner | @neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services[END_REF]. The LORIS/CBRAIN project is an initiative to develop a pan-Canadian platform for distributed processing, analysis, exchange and visualization of brain imaging data [START_REF] Das | LORIS: a web-based data management system for multi-center studies[END_REF][START_REF] Sherif | CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research[END_REF]. Finally, other generic data management systems have been proposed to offer shared solutions for managing multicenter studies. These include the Extensible Neuroimaging Archive Toolkit (XNAT) [START_REF] Marcus | The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data[END_REF], which has been successful due to its integration in the management of large projects [START_REF] Marcus | Human Connectome Project informatics: quality control, database services, and data visualization[END_REF] and ability to communicate with data management servers via dedicated REST web services, and the Collaborative Informatics and Neuroimaging Suite (COINS), which provides a web-based neuroimaging and neuropsychology software suite [START_REF] Scott | COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets[END_REF]. Though, the extensibility of these platforms is part of the motivations, none of them are built on top of a formal semantic model or ontology that can guarantee the sustainability of any evolution of the original da-ta scheme.

Significance

In this context, the Sharing Neuroimaging Resources (Shanoir) environment enables sharing between distributed sources of neuroimaging information over the Internet, whether the sources are located in various centers of experimentation, clinical departments of neurology, or research centers in cognitive neurosciences or image processing. A large variety of users can thus share, exchange and have controlled access to neuroimaging information using the software as a service (SaaS) type of cloud computing [START_REF] Rimal | A taxonomy and survey of cloud computing systems. INC, IMS and IDC[END_REF] almost as easily as if the data were stored locally.

In this paper, we introduce the Shanoir software environment for managing neuroimaging data production in the context of clinical neurosciences and show how the images are accessible through the Shanoir Data Management System. Shanoir is an open source neuroinformatics environment designed to structure, manage, archive, visualize and share neuroimaging data with an emphasis on multi-institutional, collaborative research projects. The software offers features commonly found in neuroimaging data management systems along with research-oriented data organization capabilities and enhanced accessibility. It also provides user-friendly secure web access and an intuitive workflow that facilitates the collection and retrieval of neuroimaging data from multiple sources.

In Section 2, we provide a brief overview of the software environment including its core (web portal, Study Card and quality control) and extensions for loading, querying and processing data. Section 3 describes the data repositories while Section 4 covers the use of these repositories and potential evolution.

2 Shanoir software environment

General description of the software environment

Shanoir is an open source software environment with QPL licensing designed to archive, structure, manage, visualize and share neuroimaging data with an emphasis on managing collaborative research projects. It includes the common features of neuroimaging data management systems along with research-oriented data organization and enhanced accessibility. Shanoir is based on a secure J2EE application running on a JBoss server which is accessed via graphical interfaces in a browser or by third-party programs via web services using Simple Object Access Protocol (SOAP). It behaves like a repository of neuroimaging files coupled with a relational database containing metadata (Figure 1).

Shanoir uses semantics for structuring the concepts as defined by the OntoNeuroLOG ‡ ontology [START_REF] Michel | Grid-wide neuroimaging data federation in the context of the NeuroLOG project[END_REF] [START_REF] Temal | Towards an ontology for sharing medical images and regions of interest in neuroimaging[END_REF]. OntoNeuroLOG reuses and extends the OntoNeuroBase ontology defined earlier [START_REF] Barillot | Federating Distributed and Heterogeneous Information Sources in Neuroimaging: The NeuroBase Project[END_REF] (see Figure 2). Both were designed using the methodological framework [START_REF] Temal | Towards an ontology for sharing medical images and regions of interest in neuroimaging[END_REF] of the foundational Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) [START_REF] Masolo | WonderWeb Deliverable D18, Ontology Library[END_REF] and a number of core ontologies that provide generic, basic and minimal concepts and relationships in specific fields such as artefacts, participant roles, information and discourse acts. In Shanoir, the OWL-Lite implementation was manually derived from the OntoNeuroLOG initial expressive representation to Java classes. The data model based on this ontology is dedicated to the neuroimaging field and is structured around research studies in which patients are examined to produce image acquisitions or clinical scores. Each image acquisition is composed of datasets represented by acquisition parameters and image files. For security and legal reasons, all data on the system is anonymous by default, this can be customized with specific algorithm (e.g. defacing is not currently implemented but can easily be embedded in a specific anonymizer that Shanoir will call).

Raw as well as derived (i.e., post-processed) image files can also be imported into the system using medical imaging technology (e.g., media based on the Digital Imaging and Communications in Medicine (DICOM) standard, picture archiving and communication system (PACS) or image files in the Neuroimaging Informatics Technology Initiative (NIfTI)/Analyze-style data format) using online wizards, which complete related metadata, command line tools or SOAP web services. Once identity information has been removed from raw data during the importation process, the DICOM header content is automatically extracted, enriched and inserted into the database with the customizable "Study Card" feature. Shanoir can also record any execution process for retrieval of workflows applied to a particular dataset along with the derived data.

Clinical scores from instrument assessments (e.g., neuropsychological tests) can be recorded and easily retrieved and exported in different formats (Excel, CSV, XML). The instrument database is scalable and new measures can be added in order to meet specific project needs (Figure 3). Scores, image acquisitions and post-processed images are bound together so that relationships can be analyzed. Using cross-data navigation and advanced search criteria, the user can quickly indicate a subset of data for download. Client-side applications have also been developed to locally access and exploit data though web services. The security features of the system require authentication with user rights set for each study. A study manager can define the users allowed to see, download or import data into his/her study or simply make it public.

In practice, Shanoir serves neuroimaging researchers by efficiently organizing their studies while cooperating with other laboratories. By managing patient privacy, Shanoir offers the possibility of using clinical data in a research context. Lastly, it is a handy solution for publishing and sharing data with a broader community.

Study Card and quality control concepts

Images can be imported in Shanoir from various sources: DICOM media, PACS (with DICOM Query & Retrieve), and 3D/4D image files (in NIfTI/Analyze format). Users are guided step-by-step through online forms to perform imports. In addition to archiving DICOM files, NIfTI copies are automatically generated and saved. This is convenient since the NIfTI format is better suited to perform image processing (such as registration, segmentation, statistical analysis, etc.) than the DICOM format. ). An instrument can be any record were an alphanumerical value can be attached.

The Study Card

During archiving, the DICOM files are processed in two phases. The first phase de-identifies the images. The second phase populates the database with the new metadata items generated from the DICOM header and enriched with the Study Card, which enables online metadata wrapping between the local data to be imported (center, acquisition equipment, etc.) and the semantic concepts of the research study to which the data will be assigned. The actual DICOM metadata can thus be aligned with the ontology, and also provides additional allocation of concepts to the stored images that are more closely related to the research study protocol (e.g., functional MRI, perfusion imaging, contrast agent, diffusion imaging…). The mechanism behind this feature is based on a set of rules that the user predefines to associate specific acquisition equipment and a specific data production site to the desired research study. Each rule determines the specific value of a metadata item according to the value(s) of one or more specific DICOM tag(s) (e.g., Series Description, see Figure 4). This greatly facilitates the consistent recording and alignment to the ontology of metadata for all data in a research study without the need for tedious workflow during the online import of images. Due to the simplicity of the process, no specific skills are required to perform data import and it only takes a few minutes over the Internet.

The "Study Card" concept makes possible an automatic quality control of the imported data using their metadata. For instance, a conformal statement can be attached to the imported data according to a match score to the Study Card rules.

Quality Assessment

Shanoir's next major functionality concerns the quality check of the images for conformity of the imported data with the predefined study protocol and ensure the integrity of the archived data. We have identified three levels of control:  Study Protocol: controls the time interval between examinations (expected visits) as defined by the principal investigator (PI) of the study;  Acquisition Protocol: controls the presence of all the sequences of the imaging protocol as defined by the PI of the study;  Raw data:

o The software automatically checks the range of parameters for a given protocol, experimental center or acquisition scanner as defined in the Study Card by the PI's technical representative; o Visual inspection of the image quality and integrity can be reported and assigned to the imported data; however, the mechanism to detect the visual quality is not yet integrated in the Shanoir environment.

In the next release of Shanoir, quality assessment will be present as flags (flawless, acceptable or inadmissible) in the database.

This QA capability do not address the control of image formation as for instance to check image artefacts (bias, motions, ghosting, etc…). This category of QA can be implemented in a dedicated image visualization and processing tools that interoperate with Shanoir through the dedicated web services.

Web portal

Shanoir provides user-friendly secure web access and offers an intuitive workflow to facilitate the collection and retrieval of neuroimaging data from multiple sources (Figure 5). On the home page, the user has direct access to the most frequent functionalities: Find and Download Datasets, Explore the Research Studies, Find Clinical Scores, and Import Data (Figure 6). On the top of all pages, the user always has a very complete navigation menu that leads to all services.

Interoperability

Interoperability is a very important concern for the Shanoir environment. Shanoir offers web services interface that is open to a large variety of clients. We already offer several dedicated interface that are already in used by different external applications. Hereafter, we described four of these external services that are currently available and run independently to each other: ShanoirUploader, QtShanoir, medInria and iShanoir developed either in C++, Java or Objective-C environments.

SOAP for integration of services

The Shanoir web services interface is based on the Simple Object Access Protocol (SOAP). Messages between clients and the server are exchanged using Extensible Markup Language (XML) with welldefined elements. The Hypertext Transfer Protocol (HTTP) is used with Transport Layer Security (TLS). Elements and services are described with the Web Service Description Language (WSDL).

Based on this description, client stubs can be automatically generated to simplify the connection of new clients. The web service layer is implemented with the Java API for XML web services (JAX-WS). Shanoir offers numerous dedicated web services:

 "EntityCreator": creates new entities, such as creating a new subject in the database  "CredentialTester": validates if username and password are correct  "Downloader": downloading files/datasets on base of dataset IDs  "CenterFinder": find center(s) based on different search criteria, i.e. study or investigator  "DatasetAcquisitionFinder": find acquisition(s) based on IDs or examinations  "DatasetFinder": find dataset(s) based on multiple search criteria/filters  "DatasetProcessingFinder": find dataset processing(s) based on IDs  "ExaminationFinder": find examination(s) based on multiple search criteria/filters  "ExperimentalGroupOfSubjectsFinder": find group of subjects based on multiple criteria  "InvestigatorFinder": find investigator(s) based on IDs or centers  "MrDatasetFinder": find MR dataset(s) based on multiple search criteria/filters  "StudyFinder": find study/-ies based on multiple search criteria/filters  "SubjectFinder": find subject(s) based on IDs with multiple filters  "DatasetImporter": import dataset files to already existing entities in the database  "ReferenceLister": shows list of reference strings stored in the database  "FileUploader": upload files in local archive for later import, used by ShanoirUploader

ShanoirUploader for seamless integration of data

"ShanoirUploader" is a Java desktop application that transfers data securely between a PACS and a Shanoir server instance (e.g., within an hospital). It offers both a direct DICOM query/retrieve connection to search and download images from a local PACS and a DICOM CD upload facility. After retrieval, the DICOM files are locally anonymized and then uploaded to the Shanoir server (the anonymization algorithm can be customized according to specific operational/regulation constraints). The primary goals of the application are to enable mass data transfers between different remote server instances and reduce user waiting time when importing data into Shanoir. Most of the import time thus involves data transfer. "ShanoirUploader" requires a local Java installation. For a simpler distribution and installation of the software, Java Web Start (JWS) can be used. The application can be installed with a simple web link that is opened in a web browser. Java takes care of the installation and, later, of automatic updates. Internal components are based on Java Swing for the graphical user interface (Figure 7), dcm4chee § libraries to connect with a PACS and Java, and WebServices (JAX-WS) to transfer data to a Shanoir server.

Apache Solr for metadata querying

Shanoir integrates the open source enterprise search platform Apache Solr (http://lucene.apache.org/solr/), which provides users with a vast array of advanced features such as near real-time and queries, full-text searching, faceted navigation, autosuggestion and autocomplete.

One of the most important features of the Solr search is the faceted navigation. Facets correspond to properties of the Solr information elements and are derived by analyzing pre-existing metadata that are related to the ontology model used by Shanoir.

Shanoir users can access all metadata with a simple Solr search bar. After entering at least one character, a user will be automatically guided to complete his search. Data are sorted by categories and dynamically displayed once a facet is chosen. By clicking on Solr data results, users access all the additional information available in Shanoir corresponding to their search, and then use these queries for local downloading (Figure 8). § http://www.dcm4che.org All metadata are indexed in a JBoss server that hosts the Solr servlets. A custom security post-filter has also been developed and implemented in Shanoir to control user access. This filter retrieves user identification and access rights in Shanoir and interacts with the Solr server to show relevant results that the user is allowed to access.

iShanoir for mobile data access

An iOS application, iShanoir, has been developed for iPhones and iPads. It opens a secure connection with a Shanoir server and enables the user to access data stored on a Shanoir server. With iShanoir, the user can navigate within the Shanoir data tree structure on the server. After data is selected from the mobile app, the images can be downloaded to the local device, displayed and analyzed with any local DICOM viewer or through cloud services (i.e. Dropbox, iCloud, Google or OneDrive).

The iShanoir application has been developed with Xcode and implemented in Objective-C. For the graphical user interface, two storyboards have been developed to fit the different display sizes between iPhones and iPads (Figure 9). It uses the following iOS frameworks: Foundation, CoreFoundation, UIKit and CFNetwork. For implementation of the SOAP web services client, the WSDL2ObjC utility has been used as it offers a client stub code generation based on the server WSDL document.

Qt Shanoir for image processing

Shanoir web services may also be queried from standalone C++/Qt applications through the QtShanoir library ** , which uses SOAP web services provided by a Shanoir server to access and display studies, patients, and data with their associated metadata. In QtShanoir, a set of Qt widgets are defined that can be embedded in any Qt application. The library was used to implement a Shanoir query plugin inside the medInria visualization and processing software † † for interrogation and downloading of image data from Shanoir for processing within medInria for example using the available processing tools and then upload the processing results back to the Shanoir server with the correct metadata values (Figure 10).

Distribution of Shanoir

The Shanoir server can be freely downloaded on request. It is currently deployed using Docker containers running on a Linux kernel. Linux containers are implemented using namespaces for locating each type of resource. Dockers are tools for managing lightweight method of virtualization (named containers) on Linux that are lighter than traditional virtual machines. The host and guest systems share the same kernel. The kernel is responsible for host↔guest and guest↔guest isolation (the result of ** QtShanoir library: http://qtshanoir.gforge.inria.fr † † medInria: http://med.inria.fr system calls depends on the container in which the calling process is running). As described in Figure 11, a minimal Shanoir deployment consists of four servers running in at least four separate containers:

 "shanoir_container": The actual Shanoir server. It relies on a mysql container (for the database) and on the PACS container (for archiving DICOM data),  "pacs_container": The DICOM PACS server, currently managed by dcm4chee ‡ ‡ ,  "mysql_container": The database server that is hosting two databases: shanoirdb and pacsdb,  "nginx _container": The web frontend server based on a nginx § § HTTP server configured as a reverse-proxy for reaching the Shanoir server. It is the only server that is publicly reachable. It provides TLS encryption and security filtering,  "smtpsink _container": An optional SMTP server for outgoing e-mails.

Data repositories

Each Shanoir repository has an administrator that manages the access rights of the repository. Each user requests an account through a web-based form and specifies which study he/she wants to access, contact, role in the study, required level of expertise/access (guest, user, expert, admin), etc. According to the information provided, the Shanoir administrator of the repository determines whether the user can access the system. Access to a specific study is granted by the person responsible for the study (i.e., the PI of the research study or the official representative). Depending on these settings, the new user will be able to see, download, and import datasets or even modify the study parameters. The corresponding rights are set for a limited time and must be renewed regularly. If requested, the user can receive a report by e-mail each time data are imported into the study. ‡ ‡ http://www.dcm4che.org/ § § http://wiki.nginx.org/ All data produced at Neurinfo for academic or clinical research purposes are managed through a dedicated Shanoir@Neurinfo repository (Figure 12) administered by the facility's staff. The Shanoir@Neurinfo server also hosts data from imaging studies at multiple sites. In total, around 2To of data from 42 centers and 50 MR scanners are archived at this repository. The amount of data increases by 30Gb per month (see table in Figure 12).

In daily practice, DICOM data are imported by a technician from either a local PACS, a CD/DVD or a disk drive containing the DICOMDIR in its root directory and the DICOM files. *** http://www.neurinfo.org/ The clinical studies stored on the Shanoir@Neurinfo server concern the whole body (brain, spine, heart, lung, pelvis, vasculature, etc.) with a major focus on brain anatomy and function in normal control and pathological populations. Out of the 70 or so ongoing research studies on the Neurinfo platform, 75% relates to brain imaging, 15% concern abdominal imaging and 10% concern heart imaging. Among the neuro-imaging clinical studies, multiple sclerosis, dementia, tumors, stroke and mood disorders are the most investigated pathologies.

Depending on the specific nature of the research study, typical neuro-imaging protocols include structural imaging, functional BOLD MRI, Arterial Spin Labeling perfusion imaging, diffusion imaging, relaxometry sequences, pre-and post-gadolinium T1w sequences or vascular sequences. The following studies are examples of research carried out with the Shanoir@Neurinfo service as well as the types of data that are managed by the Shanoir@Neurinfo repository.

Along with the MRI raw data, post-processed images can be stored for each dataset. For example, in a study on functional motor activation, the motor areas were delineated by a trained radiologist and associated with each 3D T1w image. MS lesion segmentation masks can also be attached to the examination. In addition to image data, clinical scores can also be stored for each subject in the repository. Several MS clinical studies collect measurements such as the number of T2 new lesions, number of T1w Gd enhancing lesions or clinical scores such as EDSS. These measurements are also included in the search engine and consequently easily accessible through requests. For more advanced clinical follow-ups, Shanoir can easily be interfaced with existing databases.

The general policy for the Shanoir@Neurinfo repository for dissemination of data related to a particular study is decided upon beforehand with the PI in compliance with the informed consent form approved by the ethics committee and signed by the participant. Any opening of the data to third parties is submitted to the approval of the PI prior to allowing (complete or partial) access to a third party user. Nonetheless, to ensure dissemination and the best use of data acquired from public funding, the Neurinfo team strongly encourages investigators to share their data, which is usually done after an embargo period.

Shanoir@OFSEP Repository

The French Multiple Sclerosis Observatory (OFSEP) A dedicated imaging working group is in charge of acquiring, processing and integrating imaging and derived imaging data into a shared imaging resource center (IRC), and ensuring that the IRC is integrated with clinical databases. The consistent assessment of MRI-based measurements on a large scale requires robust and efficient image processing pipelines. A further goal of this project is to establish an information technology infrastructure enabling audited access to imaging data, as well as a virtual laboratory environment supporting the distributed, synergistic development, validation, and deployment of specialized image analysis procedures developed by different national and international research centers. To ensure easy access to the imaging data and allow modifications, queries, annotations and access control, the Shanoir environment has been selected. It will also ensure interoperability and data management related to the imaging aspect of the cohort (the clinical part is managed by the EDMUS ‡ ‡ ‡ system). For this purpose, we have set up a specific Shanoir@OFSEP image repository that is currently in its pilot phase.

Begun in 2012, the Shanoir@OFSEP server was installed to store the imaging data of the OFSEP cohort, which will study the neuroimaging data of 40,000 MS patients over the next 10 years. A consensus has emerged concerning the acquisition protocol, which requires (at least) one brain MRI every three years, one spinal MRI every six years, i.e., 200,000 MRIs over 10 years. The Shanoir@OFSEP database will grow during this period and beyond [START_REF] Cotton | OFSEP, a Nationwide Cohort of People with Multiple Sclerosis: Consensus minimal MRI protocol[END_REF].

Since OFSEP is a nationwide project covering many patients, many IRCs and many different kinds of MRI acquisition equipment, a national repository with nationwide access and uniform measures was therefore needed. The OFSEP imaging working group is continuously gathering new acquisition centers volunteering to take part to the cohort. In Shanoir@OFSEP, there are currently about 30 IRCs which include 31 pieces of MRI acquisition equipment representing 14 different MR scanner models from three MR manufacturers (Siemens, Philips and GE). All the centers are importing data in one main study called the "Mother Cohort". Each center follows the OFSEP protocol, which will be checked through the quality control module as described in section 2.2.2. If necessary, derived imaging data can then be imported back to the server in order to refer to potential post-processing information and MS specific imaging biomarkers to make them available for authorized users.

Currently the Shanoir@OFSEP repository is hosting five studies: the "Mother Cohort" (200,000 MRIs planned over the next 10 years) as well as four MS imaging clinical research projects. More of these "OFSEP-labeled" clinical research projects or nested cohorts will be integrated in coming years. Everyone can join the "Mother Cohort" study as long as they use the OFSEP protocol. One can also ask the OFSEP to contribute to the project through his study as soon as the PI presents his research study subject to the OFSEP scientific committee that can grant (or not) the hosting. Data hosted on Shanoir@OFSEP, will remain confidential (private) throughout the duration of the study, but can be made available to all researchers through a specific OFSEP application.

Conclusion and perspectives

The Shanoir software as a service manages the sharing of distributed information sources in neuroimaging over the Internet, whether these resources are located in centers of experimentation, clinical departments in neurology, or research centers in cognitive neurosciences or image processing. Through the description of two repositories that administer a Shanoir environment (Neurinfo and OFSEP), we have illustrated how a large variety of users can diffuse, share or access neuroimaging information between peers almost as easily as if the data were stored at their local hospital, research lab or company. Through the description of the Shanoir software environment, we have illustrated how neuroimaging data can be structured, managed, archived, visualized and shared.

In the medium term we plan to integrate Shanoir's resources and services with the open community through the French National Infrastructure's "France Life Imaging" (FLI) § § § , and more specifically the "Information Analysis and Management" (IAM) node that is dedicated to providing large scale IT infrastructure for in vivo imaging. For this purpose, the FLI-IAM node will build and operate an infrastructure to store, manage and process in vivo imaging data from human or pre-clinical procedures. The main achievements of the IAM node will consist of a versatile software platform composed of several sub-components that will connect hardware and software facilities to build:

 an archiving and management infrastructure of in vivo images as well as provide solutions to process and manage the acquired data through dedicated software and hardware solutions;  versatile image analysis and data management solutions for in vivo imaging to facilitate interoperability between production sites and users and provide heterogeneous and distributed storage solutions for raw and metadata indexing (e.g. through the use of semantic models).

As such, we are under integrating Shanoir as one of the data management solutions of the FLI-IAM facilities along with a collection of companion data management software platforms such as CATI-DB (http://www.cati-neuroimaging.com) and ArchiMed, or a collection of processing clients or highperformance computing workflow facilities such as medInria, BrainVisa (brainvisa.info) and the VIP platform (http://www.creatis.insa-lyon.fr/vip). For this purpose, within FLI-IAM, we are setting up the "glue" between these platforms that will make it possible to connect and interoperate between them. In addition, through FLI-IAM, we will provide the necessary information for additional resources to join the FLI-IAM infrastructure by defining the basic conformal statement that will make the technology and scalability of FLI-IAM possible.

Nonetheless, as described in the introduction, there are a lot of similar initiatives going on recently in the medical imaging research field, like Human Brain Project, ADNI, XNAT-based solutions, etc. For these initiatives, as well as for Shanoir, the goal is to share the data at a large extent. This cannot be done without a significant additional effort on standardization in the field and on interoperability between software platforms addressing similar services. This is what motivates the integration of Shanoir in the French FLI-IAM e-infrastrucre initiative. This challenge of tomorrow is to continue this effort at the international level.

Figure 1 :Figure 2 :

 12 Figure 1: Shanoir Software Architecture

Figure 3 :

 3 Figure 3: Shanoir "instrument" database can be used for attaching clinical scores to images (e.g., EDSS score in MS). An instrument can be any record were an alphanumerical value can be attached.

Figure 4 :

 4 Figure 4: Example of a "StudyCardRule" for a 3D T1-Gd sequence

Figure 5 :Figure 6 :

 56 Figure 5: Shanoir web portal summary of the main functionalities

Figure 7 :

 7 Figure 7: Shanoir Uploader architecture for secure transfer of local PACS data to a Shanoir Server (left) and user interface (right)

Figure 8 :

 8 Figure 8: Example of Apache Solr search of the Shanoir server

Figure 9 :

 9 Figure 9: Example of storyboard interfaces under the iShanoir iOS mobile application connected to a Shanoir server

Figure 10 :

 10 Figure 10: Example of a Shanoir query service within the medInria environment by using QtShanoir web services

Figure 11 :

 11 Figure 11: Schematic diagram of a Shanoir server. There is one set of containers for production: prod-Shanoir, prod-pacs, prod-mysql and prod-nginx, and one set for qualification: qualif-shanoir, qualif-pacs, qualif-mysql and qualifnginx (not represented here). The arrows represent relationships between containers

Figure 12 :

 12 Figure 12: Evolution of the Shanoir@Neurinfo repository Global Statistics (left) and Service Infrastructure (right)

  † † † , a major epidemiological tool on multiple sclerosis (MS) for the scientific community, was selected after a call for projects for Cohorts 2010, funded by France's Investment in the Future Program. It is a collaborative project involving over 40 MS research centers in France. The aim of the project is to build and maintain a nationwide cohort of patients with MS, and enrich the clinical data with biological samples, socio-economic data and neuroimages. † † † The OFSEP MS Cohort observatory: http://www.ofsep.org/en/

† http://www.incf.org/

‡ OntoNeuroLOG: http://neurolog.i3s.unice.fr/public_namespace/ontology

‡ ‡ ‡ EDMUS: http://www.edmus.org § § § France Life Imaging https://www.francelifeimaging.fr) with the IAM node (https://project.inria.fr/fli/) is a national infrastructure for in vivo imaging.

Acknowledgements

This work has been supported by Inria under a "technological development program" grant for the Neurinfo platform from the Brittany regional council and the EU-Feder program and two grants provided by the French government through the Agence Nationale de la Recherche as part of the Investment in the Future Program under references ANR-10-COHO-002 (for OFSEP) and ANR-11-INBS-006 (for FLI).

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