Gilles-Alex Pakora
email: pakoragillesalex@yahoo.fr

Didier Buisson
email: didier.buisson@mnhn.fr

Keywords: Biotransformation, Viridin, Detoxification, Reduction, Pichia, Rhodococcus, Mucor

Biotransformation of viridin, an antifungal produced by biocontrol agent, with non-viridin producing microorganisms is studied. The results show that some environmental non-targeted microorganisms are able to reduce it in the known phytotoxin viridiol, and its 3-epimer. Consequently, this reduction, which happens in some cases by detoxification mechanism, could be disastrous for the plant in a biocontrol of plant disease. However, a process fermentation/biotransformation could be an efficient approach for the preparation of this phytotoxin.

Bioconversion of antifungal viridin to phytotoxin viridiol by environmental non-viridin producing microorganisms

Gilles-Alex Pakora

Introduction

Biological control is a strategy of pests and disease management using another organism. It's particularly developed in the control of plant diseases. It provides an alternative to the use of chemicals and it receives a good public acceptance as a natural method. Microorganisms are biological control agent acting with different mechanisms, such as antibiosis, mycoparasitism, production of cell wall-degrading enzymes, competition for nutrients and/or space and induced resistance in plant [START_REF] Pal | Biological control of plant pathogens[END_REF][START_REF] O'brien | Biological control of plant diseases[END_REF]. Several mechanisms could be combined in the control disease, which is achieved to antagonize pathogens. Trichoderma species are used as biological control agent of several destructive plant pathogens and are commercialized as biological fungicide [START_REF] Verma | Antagonistic fungi, Trichoderma spp.: Panoply of biological control[END_REF].

Microbial secondary metabolites play a crucial role in the fight against plant diseases [START_REF] Liu | Fungal secondary metabolites in biological control of crop pests[END_REF].

Viridin [START_REF] Pal | Biological control of plant pathogens[END_REF], gliovirin and gliotoxin are the most known metabolites involved in the antibiosis.

Viridin [START_REF] Pal | Biological control of plant pathogens[END_REF], which first described in 1945 [START_REF] Brian | A highly fungistatic substance produced by Trichoderma viride[END_REF] is a steroidal antibiotic produced by Gliocladium and T. virens. Strains are used against damping-off of bean (Rhizoctonia solani) [START_REF] Dubey | Integration of soil application and seed treatment formulations of Trichoderma species for management of wet root rot of mungbean caused by Rhizoctonia solani[END_REF], of cucumber (R solani, Pythium ultimum, Meloidogyne incognita) [START_REF] Roberts | Biocontrol agents applied individually and in combination for suppression of soilborne diseases of cucumber[END_REF], cotton seedling disease [START_REF] Howell | Biological-control of Pythium damping-off of cotton with seed-coating preparations of Gliocladium virens[END_REF] and gray mold of chickpea (Botritis cinerea) [START_REF] Khan | Management of gray mold of chickpea, Botrytis cinerea with bacterial and fungal biopesticides using different modes of inoculation and application[END_REF]. We demonstrated that T. virens T7, a biocontrol agent of cocoa black pod diseases, produced gliovirin and viridin 1 [START_REF] Pakora | Inhibition of Phytophthora species, agents of cocoa black pod disease, by secondary metabolites of Trichoderma species[END_REF]. Gammaray induced mutants of Trichoderma with better antagonistic effect against phytopathogen than the wild-type have been generated. They showed a greatly increasing production of secondary metabolites as viridin [START_REF] Haggag | Enhancement of antifungal metabolite production from Gamma-ray induced mutants of some Trichoderma species for control onion white rot disease[END_REF].

Viridin (1) could be reduced to viridiol (2) (Fig. 1) by viridin-producing strains [START_REF] Jones | Conversion of viridin to viridiol by viridin-producing fungi[END_REF]. Viridiol

(2) is less active against phytopathogens but it is a phytotoxin [START_REF] Jones | Plant-growth response to the phytotoxin viridiol produced by the fungus Gliocladium virens[END_REF][START_REF] Andersson | Isolation, identification and necrotic activity of viridiol from Chalara fraxinea, the fungus responsible for dieback of ash[END_REF]. The production of this herbicidal molecule by the microorganisms restricts their use as biological control agent.

Studies have been performed to avoid the production of viridiol [START_REF] O'brien | Biological control of plant diseases[END_REF]. It was found that production of viridiol (2) by G. virens could be inhibited when culture medium contains cytochrome P-450 inhibitors [START_REF] Howell | Suppression of Gliocladium virens phytotoxin production with steroid synthesis inhibitors and use of G. virens cultures as biocontrol agents[END_REF]. Another efficient approach was obtained by producing mutants of T. virens which were deficient for the production of viridiol (2) [START_REF] Howell | Viridiol deficient mutant strains of the biocontrol agent Trichoderma virens, process of making and using as biocontrol agent[END_REF].

Any molecule that ends up in the environment can undergo biotic and abiotic transformations to give new molecules with new biological activities. It is in this area of chemical ecology that we are interested in microbial biotransformation of chemicals and natural molecules. For example, concerning the former, we have shown that environmental biotransformation of drugs that can lead to the formation of more ecotoxic metabolites [START_REF] Laurence | Anticipating the fate and impact of organic environmental contaminants: A new approach applied to the pharmaceutical furosemide[END_REF][START_REF] Olvera-Vargas | Microbial biotransformation of furosemide for environmental risk assessment: identification of metabolites and toxicological evaluation[END_REF]. For the second, we have shown that juglone, a phytotoxic naphtoquinone produced by the walnut, is reduced by an endophytic fungus to give isosclerone [START_REF] Prado | One-step enantioselective synthesis of (4S)-isosclerone through biotranformation of juglone by an endophytic fungus[END_REF]. We present here a study concerning the production of viridin (1) and its microbial metabolism by environmental non-producing microorganisms.

The aim of this study is to evaluate their ability to transform viridin [START_REF] Pal | Biological control of plant pathogens[END_REF] and to study the relationship between metabolism and detoxifying power.

Materials and Methods

General

TLC was performed on pre-coated silica gel plates (Silice 60 F254 Merck, Germany). 1D and 2D NMR spectra were recorded in CDCl3 and CD3OD with Bruker 400 and 600 spectrometers using TMS as internal standard. The chemical shifts were given in δ (ppm). HR-ESI-MS and fragmentation MS spectra were obtained with an Applied Biosystem QSTAR Pulsar I mass spectrometer. Analytical HPLC analyses were performed on GILSON system with pump 305, pump 306, gradient dynamic mixer 811B and autoinjector 234. The system was controlled and the results were analyzed by Unipoint Gilson software. The column (Hypersil, C18 5 µm (250x4.6)) was eluted (1 mL/min) with a gradient solvent system: solvent A (H2O-TFA 1%) 60% and solvent B (CH3CN/H2O-TFA 1% (50/50)) 40% isocratic for 3 min followed by gradient to 60% solvent B in 10 min and held at 60% solvent B for 5 min.

Preparative HPLC were performed on a Agilent system equipped with XDB column (150 x 21.2 mm) using gradient solvent system: solvent A (H2O-TFA 0.05% /CH3CN (95/5)) 85% and solvent B (CH3CN/H2O-TFA 0.05% (95/5)) 15% isocratic for 2 min. followed to gradient to 25% solvent B in 30 min., flow 10 mL/min. For analytical and preparative HPLC, the detection wavelengths were 230 and 250 nm. Solvent were HPLC grade and use as such.

Microorganisms.

Trichoderma virens was provided to us by the National Center for Agricultural Research in Côte d'Ivoire [START_REF] Mpika | Antagonist potential of Trichoderma indigenous isolates for biological control of Phytophthora palmivora the causative agent of black pod disease on cocoa (Theobroma cacao L.) in Cote d'Ivoire[END_REF] and maintained on PDA at 4°C. Other microorganisms were stored at 4°C in medium containing Bacto peptone 5 g/L, yeast extract 5 g/L, malt extract 5 g/L, glucose 20 g/L and agar 20g/L.

Preparation of viridin (1) and viridiol (2).

Conidia of T. virens grown on PDA for 5 days were collected by sterile deionized water (1.5 10 8 conidia/mL). Fifteen Roux flasks containing 250 mL PDA were inoculated with 2 mL of conidial suspension and the cultures were incubated for 7 days at 25°C in dark. Solid media fermentation and mycelia were recovered and extracted with ethyl acetate 3 times with magnetic stirring. The organic layer was evaporated to obtain 4.79 g of crude extract.

Viridin (1)

This extract is triturated and washed three times with MeOH (3 mL) to give viridin (

Stability of viridin (1)

The effect of pH on viridin stability was studied in citrate buffer media (pH 3 to 8) at 0.5 unit intervals during 48h.

Biotransformations

Screening of actives strains

Screening experiments were carried out in resting cells conditions. Bacteria and fungi (Table S1) were tested using conventional method and yeasts using our method based on combinatory library of microorganisms (CLM). Cultures were conducted in Erlenmeyer flasks (50 mL) filled with YMS culture medium (20 mL) containing (in g/L): yeast extract 4, malt extract 10, soybean peptone 5 and glucose 16. Microorganisms were grown at 26°C on rotary shaker (160 rpm).

After 48 hours, biomasses were recovered by filtration for filamentous fungi and centrifugation for bacteria and yeasts. Biomasses of bacteria and fungi were suspended in 10 mL of citrate buffer (0.1M, pH5) and biomasses of yeasts were suspended in 5 mL and four mixtures were prepared by placing 2 mL of each suspensions as described in Table S2. Plates were incubated at 26°C and 37°C for bacteria, and inhibition zone were daily measured as the formula S=π×R 2 .

Results

T. virens T7, isolated in Cote d'Ivoire, has been used and an optimization of viridin [START_REF] Pal | Biological control of plant pathogens[END_REF] production was performed by culturing the microorganism in different conditions. Viridin [START_REF] Pal | Biological control of plant pathogens[END_REF] and viridiol (2) (Fig. 1) were isolated from an ethyl acetate extract of agar and biomass prepared by culture on solid medium (PDA) in the dark at 25°C. Viridin (1) was obtained by precipitation in methanol and viridiol (2) by chromatography as described in Materials and Methods.

Structures of products were established by comparison with literature. Natural viridin (1) which is a α-methoxy ketone can undergo an epimerization at C-2 position to give 2-epi-viridin. [START_REF] Grove | Viridin Part I. Isolation and characterisation[END_REF] In order to study its stability, we incubated 1 in buffer solutions (pH 3.0-8.0). Its isomerization was observed in pH > 5.5. In buffer pH ≤ 5.5, viridin (1) is stable and no formation of 2-epiviridin was observed. 1) by various microorganisms because its fate in the environment, and in particular its biotic transformation products have never been investigated. Since viridin (1) is known to be fungistatic and showed low antibacterial activity [START_REF] Hanson | The viridin family of steroidal antibiotics[END_REF], it was possible to conduct this study under resting cell conditions. In fact, under these conditions the microorganisms are grown in good conditions and the biomass is set to incubate in the presence of the molecule studied. Forty-five microorganisms from a broad range of environment, including bacteria (22 strains), fungi and oomycetes (11 strains) and yeasts (12 strains), have been screened for their ability to biotransform viridin [START_REF] Pal | Biological control of plant pathogens[END_REF]. While bacteria and fungi have been tested individually, the screening of yeasts was made on mixtures of strains according to our method based on combinatory library of microorganisms (CLM) [START_REF] Fromentin | Yeast-mediated xanthone synthesis through oxidative intramolecular cyclization[END_REF]. Thus, the twelve yeasts were tested in four incubations.

Incubation media [START_REF] Heraux | Composted chicken manure as a medium for the production and delivery of Trichoderma virens for weed control[END_REF] were analyzed after 48 hours incubation time. The data analysis of TLC showed that fifteen microorganisms were able to transform viridin (1) (Table 1). In some cases, the main product had the same Rf value (Rf 0.38, CHCl3/acetone 7:3) than viridiol (2) obtained by fermentation with T. virens. LC/MS and NMR data confirmed the structure of viridiol (2) (Table S3 Half of the tested bacteria were able to transform viridin (1), e.g. Bacillus cereus, Nocardia coralina and some Rhodococcus species. Among them, R. globerolus caught our attention because it gave two metabolites. The eleven tested fungi belong to the genus Aspergillus (2), Botrytis (1), Fusarium (1), Phytophthora (3) and Mucor (4). Three Mucor species were able to transform this compound, and one of them, M. racemosus, is a plant pathogen (tomatoes [START_REF] Kwon | Soft rot of yomato caused by Mucor racemosus in Korea[END_REF]). While Fusarium oxysporum and Phytophthora spp, were not able to transform viridin (1) under the study conditions, analysis of the Botrytis cinerea incubation medium showed the presence of two new metabolites whose identification requires further investigations. Since a biotransformation was observed for a single mixture of yeasts (pool 2) it was possible to conclude that only one strain is active and to identify it quickly. Indeed, it is the strain P. anomala, it was the only one that was present exclusively in this mixture (table S1).

The ability to reduce viridin (1) by M. racemosus, P. anomala and R. globerolus had been investigated using concentrations of 0.5 and 1 mg/mL. The kinetic study of reduction of viridin

(1) by the fungi and the yeast showed the fast disappearance of substrate from the incubation medium because of its absorption into cells. Then, viridiol (2) as main biotransformation product was slowly released in incubation medium. In order to carry out a kinetic study, it is necessary to extract viridin (1) from the cells by methanol treatment and sonication before HPLC analysis. M. racemosus reduced viridin (1) at 0.5 mg/ml in 40 hours, and P. anomala was less active, since the reduction in more than 48 hours. For both microorganisms, no formation of viridiol (2) was observed during incubation at 1 mg/ml. Thus, their ability to reduce viridin (1) to give viridiol (2) is concentration-dependent.

The biotransformation of viridin (1) by R. globerolus was even faster, there was only 19%

viridin (1) left after 4 hours of incubation. TLC profiles and HPLC chromatograms of experiment conducted at 0.5 mg/mL showed the presence of two metabolites whose formation kinetics are presented in Fig. 2. One of them had the same Rf value and retention time (8 min.) than the viridiol (2). The second metabolite 3, with greater Rf (0.48) and retention time (10 min.) was a isomer of 2 with molecular ion at m/z 355.119 for [M + H] + and formula C20H18O6.

Other peaks observed in its electrospray ionization (ESI) mass spectrum were 336 (M + -18), 308 (M + -46) and 280 (M + -74). This fragmentation pattern was similar to that described for viridiol 2 [START_REF] Jones | Conversion of viridin to viridiol by viridin-producing fungi[END_REF][START_REF] Wipf | Structure reassignment of the fungal metabolite TAEMC161 as the phytotoxin viridiol[END_REF]. The 1 H-and 13 C-NMR spectra of 3 were also very similar to those of 2,

indicating that a reduction of the 3-oxo group in viridin (1) had occurred, and not at the C-7 and C-17 carbonyls which appear in the carbon spectrum of 3 at 173.3 ppm and 208.8 ppm, respectively (Table S3, Fig S2 andS3). In the NOESY spectrum of 3, a correlation between H-3 and H-18 allowed to determine the α-stereochemistry of the hydroxy group at C-3. Another correlation of H-19 with H-18 confirmed that viridin (1) did not undergo epimerization under the pH incubation conditions.

Therefore, this second metabolite 3 possesses a 3S-configuration, and was named 3-epi-viridiol.

On the other hand, enantiomeric synthesis of both epimers, 2 and 3, have been described [START_REF] Del Bel | Enantioselective chemical syntheses of the furanosteroids (-)-viridin and (-)-viridiol[END_REF]. As viridin (1) is not active against bacteria, biotransformation cannot be therefore considered as a detoxification mechanism in these organisms. On the contrary, since viridin [START_REF] Pal | Biological control of plant pathogens[END_REF] has antifungal activity, we can suggest such a role in other microorganisms. Thus, we investigated the relationship between the reducing capacity of two selected microorganisms and biological activity of viridin (1) and viridiol [START_REF] O'brien | Biological control of plant diseases[END_REF]. However, we also tested their activity against R. globerolus as we have shown that this bacterium is capable of reducing 1. Both molecules were inactive.

Viridiol (2) 3-epi-viridiol (3)
The antifungal activity of viridin (1) and viridiol (2) was evaluated using paper disk assays and results are given as inhibition areas in Table 2 (M. racemosus) and Table 3 (P.

anomala). In both cases, we observed an inhibition of the growth by viridin (1) but viridiol [START_REF] O'brien | Biological control of plant diseases[END_REF] was totally inactive. However, we pointed out that the inhibition areas decreased during the incubation in particular with fungus. For example, 25 µg of viridin (1) produced an inhibition area of 132 mm 2 after 24 hours incubation, which decreased to 78 mm 2 after 48 hours and finally, no inhibition was detected after 72 hours.

Discussion

Many specialized metabolites produced by plants or microorganisms are defense molecules.

Their biosynthesis may be constitutive or stress-induced [START_REF] Hans | Two classes of plant antibiotics: phytoalexins versus Phytoanticipins[END_REF]. In response and to develop, the target organisms must detoxify them by biotransformation. The two main mechanisms for detoxifying xenobiotics, whether natural or not, involve a variety of reactions that aim either to increase their hydrophilia, generally by conjugation [START_REF] Fill | Conjugation of antifungal benzoic acid derivatives as a path for detoxification in Penicillium brasilianum, an endophyte from Melia azedarach[END_REF], or to deactivate them. This, are well described for phytoalexins [START_REF] Pedras | Metabolism and detoxification of phytoalexins and analogs by phytopathogenic fungi[END_REF] and mycotoxins [START_REF] Mccormick | Microbial detoxification of mycotoxins[END_REF]. These biotransformations may occur as part of the use of microorganisms for the bio-control of phytopathogens by antibiosis.

Detoxification reactions can be carried out by the target microorganisms but also by other endophyte or epiphytic microorganisms. In this case the implementation of biocontrol is more delicate because it is dependent on the absence of these microorganisms. We tested a range of environmental microorganisms and among them, bacteria of the genus Rhodococcus which are known to be active in bioconversions [START_REF] Warhurst | Biotransformations catalyzed by the genus Rhodococcus[END_REF] and in bioremediation [START_REF] Kuyukina | Application of Rodococcus in bioremediation of contaminated environments[END_REF]. These endemic bacteria have been found to thrive in soil, water, and eukaryotic organisms.

Viridiol (2) which was obtained as main product during all biotranformation was the result of a highly regio-selectivity since viridin (1) is a tri-ketone. Two carbonyl groups are adjacent to a benzene nucleus, one forming an indanone framework and the other a naphthalene framework. These two ketones are not reduced during viridin biotransformation. On the contrary, the third carbonyl group of 1, which is adjacent to methoxy group was reduced to form viridiol (2). Our results were consistant with the reduction of viridin (1) as detoxification mechanism and showed that non-viridin producing microorganisms are able to transform 1 to 2. These microorganisms can be non-sensitive (bacteria) or sensitive (yeast and fungi) to viridin [START_REF] Pal | Biological control of plant pathogens[END_REF]. The reduction of viridin (1) by microorganisms present in environment could reduce the effectiveness of biocontrol in two ways.

The first is the transformation of the active substance in a derivative without antifungal activity, which is released in environment. Then, phytopathogen fungi could benefit to this inactivation. The reduction of viridin (1) by P. anomla, a non-Saccharomyces wine yeast [START_REF] Renouf | Inventory and monitoring of wine microbial consortia[END_REF],

is interesting regarding the biocontrol of Botrytis cinerea, responsible of botrytis bunch rot in grape, by Trichoderma sp. Indeed, we determined activity of viridin (1) against B. cinerea and found that spore germination is strongly prevented by viridin (1) with MIC 20 ± 2 µg/mL.

However, despite this good activity, T. virens is not used as BCA against grey mould. [START_REF] Elmer | Biosuppression of Botrytis cinerea in grapes[END_REF].

Results from biotransformation experiments leading to inactivation provide insights into the structural elements essential to the activity and may assist in the design of new control agents. [START_REF] Daoubi | Screening study of lead compounds for natural product-based fungicides: antifungal activity and biotransformation of 6a,7a-dihydroxy-b-himachalene by Botrytis cinerea[END_REF] In the case of viridin (1), as the reduction of ketone leads to a total loss of antifungal activity, it is necessary to design molecules for which this reduction is disadvantaged.

The second is that viridiol (2) is a phytotoxin with necrotic activity [START_REF] Andersson | Isolation, identification and necrotic activity of viridiol from Chalara fraxinea, the fungus responsible for dieback of ash[END_REF] which could have disastrous effects against the treated plant. On the contrary, it is through its herbicidal activity that T. virens has begun to be used in the biocontrol against Setaria viridis and Amaranthus retroflexus [START_REF] Hutchinson | Trichoderma virens-inoculated composted chicken manure for biological weed control[END_REF][START_REF] Heraux | Composted chicken manure as a medium for the production and delivery of Trichoderma virens for weed control[END_REF]. However, there is a need to increase the concentration of viridiol (2) to ensure reliable performance [START_REF] Verma | Antagonistic fungi, Trichoderma spp.: Panoply of biological control[END_REF] and the reduction of viridin (1) by non-viridin producing microorganisms could be an interesting approach. Indeed, an effective viridiol [START_REF] O'brien | Biological control of plant diseases[END_REF] concentration could be obtained by combining the viridin production by T. virens and the reduction thereof by a non-viridin producing microorganism. In addition, it can be noted that these products are currently sought for biological studies, in particular such as candidate for PI3K inhibition-based chemotherapy, and several total syntheses are reported [START_REF] Del Bel | Enantioselective chemical syntheses of the furanosteroids (-)-viridin and (-)-viridiol[END_REF][START_REF] Ji | Total synthesis of viridin and viridiol[END_REF]. It can be highlighted that their preparation by coupling fermentation/biotransformation does not use numerous highly toxic or otherwise problematic reagents contrary to the syntheses described [START_REF] Kühlborn | Making natural products from renewable feedstocks: back to the roots?[END_REF].

Conclusion

We conducted biotransformation experiments to study the action of microorganisms (45 strains) on viridin (1), a molecule produced in large quantities by Trichoderma virens, a bio-control agent. The screenings carried out led to the selection of 15 microorganisms capable of transforming viridin (1) into viridiol (2), a phytotoxin. Among these 15 microorganisms, R.

globerolus, a bacterium was able to generate in addition to natural viridiol (2), its unknown epimer 3-epi-viridiol (3). Our results show that the conversion of viridin (1) into viridiol (2) is not exclusive of viridin-producing fungi. R. globerolus, M. racemosus, P. anomala are able to

  13 C-NMR (CDCl3, 100 MHz): d 206.7 (C-17), 173.7 (C-7), 158.7 (C-10), 158.1 (C-14), 145.8 (C-6), 145.6 (C-20), 142.4 (C-5), 137.0 (C-8), 129.9 (C-13), 127.4 (C-12), 127.3 (C-11), 122.1 (C-4), 81.7 (C-2), 71.8 (C-1), 61.7 (C-3), 60.8 (C-19), 42.4 (C-9), 36.5 (C-16), 30.5 (C-18), 28.5 (C-15).

1 2 Fig. 1 .

 121 Fig. 1. Structures of viridin (1) and viridiol (2)

Fig. 2 .

 2 Fig. 2. Conversion of viridin (1) (X) (0.5 g/l) to viridiol 2 (<) and 3-epi-viridiol 3 (p) by R. globerolus over the incubation time.

Fig. 3 .

 3 Fig. 3. Select COSY, HMBC and NOESY correlations

Fig. 4 .

 4 Fig. 4. Biotransformation of viridin (1) by R. globerolus

a,b,* , Stéphane Mann a , Daouda Kone c and Didier Buisson a,* a

  Laboratoire Molécules de Communication et Adaptation des Micro-organismes (MCAM), Muséum national d'Histoire naturelle, CNRS ; CP54, 57 rue Cuvier, 75005 Paris, France b Laboratoire de Pharmacodynamie Biochimique, UFR Biosciences, Université Félix Houphouët Boigny d'Abidjan (UFHB), 22 BP 582 Abidjan 22, Côte d'Ivoire c Laboratory Laboratoire de Physiologie Végétale, UFR Biosciences, Université Félix Houphouët-Boigny d'Abidjan (UFHB), 22 BP 582 Abidjan 22, Côte d'Ivoire

  Cells suspensions of P. anomala and M. racemosus in glycerol (15%) were obtained from agar slant and spread on the PDA plates. Cells suspension of R. globerolus were obtained from 24hhold culture in LB liquid medium and spread on LB agar medium (yeast extract 5g/L; NaCl 10g/L; agar 20 g/L) using a swab. After one hour, antibiogram paper disks (6 mm diameter) treated with viridin (1) or viridiol (2) (ten microliters of DMSO solutions of were transferred onto the agar plates (in triplicates). Ten microliters of DMSO were applied to control disks.

	(150 MHz, CD3OD) : ẟ 208.8 (C-17), 173.4 (C-7), 159.6 (C-10), 159.1(C-14), 147.1 (C-20),
	146.3 (C-5), 146.3 (C-6), 137.4 (C-8), 131.2 (C-13), 129.1 (C-11), 126.9 (C-12), 125.5 (C-4),
	89.1 (C-2), 72.3 (C-1), 66.0 (C-3), 59.8 (C-19), 43.4 (C-9), 36.7 (C-16), 29.0 (C-15), 28.6 (C-
	18).
	2.5. Evaluation of growth inhibiting activity
	MHz, CD3OD) : ẟ 208.8 (C-17), 173.4 (C-7), 161.6 (C-10), 159.1(C-14), 147.5 (C-20), 146.6
	(C-5), 146.3 (C-6), 137.7 (C-8), 130.9 (C-13), 128.0 (C-11), 127.3 (C-12), 125.4 (C-4), 86.5
	(C-2), 73.8 (C-1), 64.9 (C-3) , 61.9 (C-19), 43.6 (C-9), 37.2 (C-16), 32.3 (C-18), 29.0 (C-15).
	epi-Viridiol 3: 1 H-RMN (600 MHz, CD3OD): d 8.60 (1H, d, J = 8.2 Hz, H-11), 7.93 (1H, d, J

Viridin (1) was added (finale concentration 0.1 mg/mL) in solution of DMSO

(10 µL)

. Incubations were incubated in same conditions. Controls without compound 1 and non-inoculated flasks containing compound 1 were conducted in same conditions.

Experiments at different concentrations of viridin were conducted in the same manner. Every day, samples (500 µL) were withdrawn, for HPLC analysis and then extracted by EtOAc for TLC and MS.

2.4.2. Reduction of viridin (1) by Rhodococcus globerolus

Culture of Rhodococcus globerolus were conducted in 500 mL Erlenmeyer flaks containing 200 ml of YMS inoculated with 48h-hold pre-culture. R. globerolus cells were taken up in 40 mL of citrate buffer at pH 5 and viridin (20 mg dissolved in DMSO) was added in the flask to be incubated for 48 hours. The procedure and conditions were as in screening experiments. The residue (31 mg) obtained was purified by preparative HPLC to give 2 (1.1 mg) and 3 (1 mg). Viridiol (2): 1 H-RMN (600 MHz, CD3OD): d 8.55 (1H, d, J = 8.1 Hz, H-11), 7.91 (1H, d, J = 1.0 Hz, H-20), 7.88 (1H, d, J = 8.1 Hz, H-12), 4.99 (1H, dd, J = 4.52 1.0 Hz, H-3), 4.30 (1H, d, J = 3.78 Hz, H-1), 3.79 (1H, dd, J = 4.52Hz 3.78 Hz, H-2), 3.70-3.80 (H, m, H-15a), 3.72 (3H, s, OMe), 3.60-3.70 (H, m, H-15b), 2.70-2.76 (2H, m, H-16), 1.75 (3H, s, Me). 13 C-RMN (150 = 1.2 Hz, H-20), 7.90 (1H, d, J = 8.2 Hz, H-12), 5.07 (1H, dd, J = 3.0 Hz 1.2 Hz, H-3), 4.13 (1H, d, J = 5.1 Hz, H-1), 3.75-3.82 (1H, m, H-15a), 3.70-3.75 (1H, m, H-15b), 3.68 (1H, dd, J = 5.1 Hz 3.0 Hz, H-2), 3.66 (3H, s, OMe), 2.70-2.74 (2H, m, H-16), 1.59 (3H, s, Me). 13 C-RMN

Table 1 :

 1 , FigS1). Active microorganisms in biotransformation of viridin[START_REF] Pal | Biological control of plant pathogens[END_REF]

		Genus	Number of	Number of
			tested strains	active strains
		Bacillus	4	1
	Bacteria (22)	Nocardia Pseudomonas	1 3	1 0
		Rhodococcus	14	9
	Oomycetes (3)	Phythophtora	3	0
		Aspergillus	2	0
	Fungi	Botrytis	1	1
	(8)	Fusarium	1	0
		Mucor	4	3
		Cryptococcus	1	0
		Dipodascus	1	0
	Yeasts (12)	Kloeckera Kluyveromyces Pichia	1 2 4	0 0 1
		Rhodosporidium	2	0
		Yarrowia	1	0

Table 2 :

 2 Effect of viridin (1) and viridiol (2) at different concentrations on mycelial growth of M. racemosus given in areas of inhibition zones (mm 2 ).

				Viridin (1) (µg)		Viridiol 2 (µg)
	Time (h)	3.12	6.25	12.5	25	50	50
	24	0	50.2	78.5	132.6	314	0
	48	0	0	50	78	176	0
	72	0	0	0	0	78	0
	Although viridin (1) was more active against P. anomala, the same phenomenon was
	observed, however the inhibition areas have not totally disappeared after 96 hours. As we
	checked the stability of the viridin (1) on a PDA culture medium (pH <5.5) we can conclude
	that the two microorganisms are able to detoxify 1, if its concentration is not too high.

Table 3 :

 3 Effect of viridin[START_REF] Pal | Biological control of plant pathogens[END_REF] and viridiol (2) at different concentrations on growth of P. anomala given in areas of inhibition zones (mm 2 ).

reduce viridin (1) to viridiol (2). These results challenge us on the causes of this conversion of viridin (1), it is believed to be the action of a detoxification by M. racemosus, P. anomala.

Finally, our work shows that it is possible to produce viridiol (2) for an herbicide use.

Acknowledgments

The authors wish to thank L. Dubost for mass spectra, A. Deville for NMR spectra.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix. Supplementary material

Supplementary data of this article can be found at…


Cassia Trojahn

Luiz Bonino

Giancarlo Guizzardi

Clement Jonquet Onto4fair

Megan Katsumi

Emilio Sanfilippo

Jennifer D ' Souza

Anisa Rula

Luiz O Bonino Da

Silva Santos

418x

Guillaume Alviset

Christelle Pierkot

Marine Vernet

Workshop chairs

Keywords: Minimal metadata model, Semantic artefacts, Ontologies, Vocabularies, Metadata, FAIR, FAIRness assessment ontologies, terminologies, taxonomies, thesauri, vocabularies, metadata schemas and standards Ontology repository, Earth sciences, Ontoportal, FAIR, semantic artefacts catalogue semantic publishing, digital libraries, scholarly infrastructure, FAIR data principles, open science

Semantic interoperability is crucial for the FAIR Principles and strongly relies on Semantic Artefacts that also need to be FAIR. To achieve this, semantic artefacts require rich, structured, and interoperable metadata. The challenge lies in determining the threshold for "rich metadata" and agreeing on a common minimum set. The H2020 FAIRsFAIR project and the RDA Vocabulary Semantic Services Interest Group addressed this question by developing a "minimal metadata model" for semantic artefacts. In this paper, we present background information, methodology, discussions and workshops which contribute to the establishment of the FAIRsFAIR minimum metadata profile for semantic artefacts. We present an extension of the Metadata for Ontology Description and Publication Ontology (MOD2.0) incorporating this profile as well as its implementation (SemanticDCAT-AP) and its use to build FAIRcat, a prototype of a FAIR Data Point harvesting the content of multiple semantic artefact catalogues.

Preface

Making the resources produced by researchers fully reusable and understood requires specific efforts. The Findable, Accessible, Interoperable and Reusable (FAIR) principles were elaborated to address these issues, describing a set of requirements for resource reusability and interoperability. These principles have been gaining increasing attention in a range of different areas and applications. One the one hand, a key aspect is the ability of properly and semantically describing resources, in particular with the help of ontologies. On the other hand, ontologies themselves have to be compliant with the FAIR principles.

This series of workshops has the following goals: (a) to bring together leaders from academia, industry and user institutions to discuss the adoption of FAIR principles in real-world requirements. (b) to serve to inform industry and user representatives about existing research efforts that may meet their requirements. (c) to investigate how the FAIR principles are supported by the use of schemes, vocabulaires, and ontologies that ideally are themselves FAIR.

In 2023, we have a twin workshop at both FOIS and SEMANTICS conferences. The primary aim is to bring the gap between the scientific and the practitioner/industry sides, respectively, where we would take the greatest and latest advances in the state of the art to industry and bring back the practitioners' needs and challenges to the scientific community of figure out a solution.

This proceedings brings together works presented at both editions: 3rd edition at SEMANTICS and 2nd edition at FOIS. Both editions received 3 submissions where 2 of them have been accepted. For Onto4FAIR@FOIS, one of the papers has been accepted only for presentation following the reviewers recommendation and has not been included in the proceedings.

Each submission were reviewed by three collaborators from our program committee. Our sincere thanks to the members of the Program Committee and all the authors who submitted their work and participated in the event.

Both workshops also counted with an invited talk entitled A FAIR Catalog of Ontology-Driven Conceptual Models by Tiago Sales at FOIS and Claudenir M. Fonseca at SEMANTICS, both researchers at Semantics, Cybersecurity and Services (SCS), University of Twente, Enschede, The Netherlands, involved in the FAIR catalog projet. We warmly thank the speakers for their presentations.

Introduction

Semantic interoperability is at the very core of the FAIR Data Principles [1] and as in any interoperability effort, it requires agreement on how the resources or artefacts supporting "semantics" are described. In all domains, many vocabularies, terminologies, ontologies or more largely semantic artefacts 2 are produced to represent and annotate data to make them more interoperable. Semantic artefacts have even become a master element to achieve FAIRness and have been discussed as digital objects that themselves need to be FAIR [2][3][4].

However, in order to properly follow the FAIR principles, semantic artefacts need rich, structured and interoperable metadata, which is also a necessary condition for machine-actionability [1]. One of the main challenges for implementing the FAIR principles, whether for semantic artefacts or for any kind of data, is to determine the threshold for "rich metadata" mentioned in principle F2. It requires communities to agree on a common and minimal metadata schema that could be used as a threshold for FAIR. Reaching an agreement on such a common metadata schema and representation improve systems' interoperability by allowing the development of client applications that would need to read/parse only one representation format. On top of this, by agreeing on a minimum set of metadata for semantic artefacts, the same systems would guarantee every time an application encounters the metadata of a semantic artefact, it would know that minimally certain information would be available.

The task 2.2 of the H2020 FAIRsFAIR project was dedicated to establish prerequisites for better semantic interoperability by developing recommendations and facilitating uptake of good practices to make semantic artefacts compliant with the FAIR principles [2][3][4]. Among the recommendations produced by the project, P-Rec 3 required the creation of "A common minimum metadata schema to be used to describe semantic artefacts and their content." Such a minimum set of metadata (also called a "minimal metadata model") for semantic artefact was missing. To fill in this gap, the task established, in collaboration with a wide range of communities, a first version of this model presented here. This collaboration was supported by the RDA Vocabulary Services Interest Group's (VSSIG) Ontology Metadata task group which was discussing an extension of the pre-proposed Metadata for Ontology Description and Publication Ontology (MOD) model [5]. This extension would both: (i) offer a review of all metadata properties available for semantic artefacts (similar to a "maximal metadata model") and (ii) revise the MOD model as an extension of DCAT2.

This joint effort had the overall goal to enable the implementation of FAIRness assessment methods that would be capable of establishing some kind of base threshold for a semantic artefact to be FAIR, as for example the grid proposed in [6]. In doing this, one challenge was to identify both: (i) generic metadata properties for digital objects that would apply to semantic artefacts (e.g., creator, identifier, or license); and (ii) metadata properties that would be specific to semantic artefacts (e.g., representation language). Furthermore, a shared understanding has to be reached on the value, necessity and feasibility of the key metadata properties. Indeed, MOD v1.4, released in 2018, contained 128 properties to describe semantic artefacts. Those were taken from 15 "crosswalked" metadata vocabularies 3 and would come as this, without prioritization, so that a developer would face a concrete problem of identifying which are the key/most important of these properties. Of course, nothing would restrict the usage of more metadata properties for richer description.

Therefore, the subject of minimal metadata model for semantic artefact was discussed openly and publicly in multiple workshops and meetings organized by the FAIRsFAIR task 2.2 and RDA VSSIG task groups. Finally, a workshop organized June 4 th 2021 -in which more than 30 participants from around 20 different communities contributed (out of 76 attendees)-helped us to come to the FAIRsFAIR minimum metadata profile for semantic artefact presented in this article.

In the following, we present background information on the subject of FAIR semantic artefacts (Section 2), then we introduce our working methodology (Section 3). We briefly present the MOD2.0 proposed model for semantic artefact and their catalogues done by extending DCAT2 (Section 4). 4 Then, based on the two new main objects introduced by this model, mod:SemanticArtefact and mod:SemanticArtefactDistribution, we present the metadata properties that were gathered to describe them and eventually voted to decide the level of requirement: Mandatory or Recommended or Optional (Section 5). Then, we explain how we have integrated the requirements in MOD2 and also developed two machine-actionable representations of SemanticDCAT-AP, an experimental application profile used by FAIRcat, a tool to aggregate and align repository metadata content to DCAT for publication in a FAIR Data Point (Section 6). Finally, we conclude and present some perspectives (Section 7).

Background

Before the FAIR Principles, a recommendation for publishing RDF vocabularies was produced in 2008 by the W3C Semantic Web Deployment Working Group. 5 Then in 2014, the 5-stars LOD principles of Berners-Lee [7] were specialized for linked data vocabularies [8] as five rules to follow for creating and publishing "good" vocabularies. The degree to which the FAIR principles align and extend the 5-star open data principles was also later in studied [9,10] and [6] presented after. In 2017, the Minimum Information for Reporting an Ontology initiative published the MIRO guidelines for ontology developers when reporting an ontology in scientific reports [11]. These guidelines refer to 34 information items (such as "ontology name," "ontology license," "ontology URL") and specify the level of importance (must, should, optional) for each individual information item. This work was significant but was never put in perspective with the FAIR principles. In MOD, where the authors reviewed which properties of MOD v1.4 could "help" addressing which MIRO guidelines (cf. example in section 6.1).

In 2017, Dutta et al. [5] reviewed and harmonized existing metadata vocabularies and proposed a unified Metadata for Ontology Description and Publication Ontology (MOD) model to facilitate manual and automatic ontology descriptions, identification, and selection. MOD is not another standard nor another metadata vocabulary, but more an aggregated set of identified properties one can use to describe a semantic resource.6 MOD 1.4 was used in AgroPortal to implement a richer, unified metadata model [12].

Then, since 2020, we have seen four parallel initiatives that investigated the question of FAIR semantic artefacts:

• In March 2020, the FAIRsFAIR H2020 project delivered the first version of a list of 17 recommendations and 10 best practices recommendations for making semantic artefacts FAIR [2]. For each recommendation, the authors provided a detailed description associated with a list of related supporting technologies or technical solutions proposed by different communities.

• Later in 2020, Garijo et al. [13] produced "guidelines and best practices for creating accessible, understandable and reusable ontologies on the Web." In another position paper, Poveda et al. [14] completed their work with a qualitative analysis of how four ontology publication initiatives cover the foundational FAIR principles. They proposed some recommendations on making ontologies FAIR and listed some open issues that might be addressed by the semantic Web community in the future. In October 2021, [19]. The methodology projects the 15 foundational FAIR principles for ontologies, and proposes 61 questions, among which 80% are based on the resource metadata descriptions. The methodology has been (partially) implemented in AgroPortal [20] and is currently being transferred to other OntoPortal-based ontology repositories.

In conclusion, each of these initiatives reviewed somehow -more or less directlysome metadata properties associated to multiple criteria required to produce a so-called "FAIR semantic artefact"; however, none of these approaches explicitly list a minimal set of metadata properties to be considered FAIR and took the responsibility to qualify these properties as mandatory-recommended-optional. We believed: (i) a consensual approach, based on informal feedback and voting was actually a good way to converge and (ii) our current research projects and working groups were offering a relevant context for discussion such a consensus.

Methodology

In 2020-2021, in parallel with the RDA VSSIG Ontology Metadata Task Group which was working on defining MOD2 presented in Section 4, the H2020 FAIRsFAIR project organized three public workshops to eventually produce the minimal model presented in Section 5. These workshops involved ontologists, knowledge engineers and semantic artefact catalogue providers:

• On April 29 th , 2020 (~30 participants): the objective was to present and discuss the first set of 17 "general recommendations" and 10 "best practices recommendations" for FAIR semantic artefacts [2] and in particular about P-Rec3 on metadata for FAIR Semantic artefacts. The recommendations were also made publicly available for comments on GitHub. 7 The outcomes of this workshop as well as the discussion on GitHub and in subsequent RDA task groups meetings contributed to revise the recommendations and produce a second version [3] • On October 15 th , 2020 (~30 participants): the objective of this second workshop was to collect feedback on the first version of the recommendations and to establish the alignment of the recommendations with the RFC 2119 (MUST, SHOULD, SHALL). The outcomes of this workshop contributed to the second version of the recommendations [3]. • On June 4 th , 2021 (~80 participants): the objective was then to determine a set of key metadata properties to build a minimum metadata profile for semantic artefacts, setting up a threshold on FAIRness. In this workshop, the participants voted to decide if each property should be optional, recommended or mandatory. During the votes the participants focused on the meaning of the properties i.e., the information they encode, but not necessarily on the metadata vocabularies providing a formal property to encode this information. At the beginning of the workshop, all attendees were made aware of the idea of data modeling and a good familiarity with DCAT was suggested as these were needed to actually contribute during the voting session. They were then presented with a simple use-case to support the voting: what would be the necessary fields for retrieving semantic artefacts? Both DCAT and the MOD2 proposition were thoroughly presented, then workshop participants were asked only to share responses for which they considered themselves to have sufficient expertise and awareness to make an informed contribution. They were guided through this process by the organizers (authors).

A proposed model for semantic artefact and their catalogues (MOD2)

MOD2.0 was proposed in 2020 as a new version of the Metadata for Ontology Description and Publication Ontology, structured as an extension of DCAT. 8 The Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogues published on the Web. By using DCAT to describe datasets in catalogues, publishers increase discoverability and enable applications to consume metadata from multiple catalogues. The key idea in extending DCAT was to view semantic artefacts as "datasets of knowledge entities" that can be available in multiple "distributions" and can be "cataloged" in repositories such as BioPortal [21] or the Ontology Lookup Service [22]. 9In designing MOD2, several design issues -not necessarily discussed in this paperwere raised including: (i) how (and is it necessary) to "extend" the notion of distribution?;

(ii) which classes inside DCAT and outside, the mod:SemanticArtefact class should explicitly extend or supersede?; (iii) which metadata properties from other vocabularies are available to describe semantic artefacts?; (iv) are there properties from outdated and not maintained metadata vocabulary that MOD could adopt?. The five key-classes from DCAT were finally specialized by creating new classes in the MOD namespace, as illustrated in Figure 2: • mod:SemanticArtefact: A collection of knowledge entities (classes, properties, concepts, terms, mappings), produced and curated by a single or multiple agents, and available for access or download in one or more representations. This is typically the class of any knowledge organization systems or resources such as ontologies, vocabularies, concepts schemes, thesauri, terminologies, etc. For example, the AGROVOC thesaurus (http://aims.fao.org/aos/agrovoc) or the CODO ontology (https://w3id.org/codo). • mod:SemanticArtefactDistribution: A specific representation of a semantic artefact. Typically, the class of any possible distributions or issuances of the semantic artefacts. It could be used to distinguish either multiple versions of a semantic artefact or different format/representation available. For example, "the version 1.3 of CODO in OWL"; or the "AGROVOC Core distribution in SKOS with TTL syntax". • mod:SemanticArtefactCatalog: A curated collection of metadata about semantic artefacts. Typically, the class of repositories, libraries or services hosting and maybe also serving various semantic artefacts. For example, the NCBO BioPortal repository or the AgroPortal vocabulary and ontology repository or the NERC Vocabulary Server. • mod:SemanticArtefactCatalogRecord: A record in a catalog, describing the registration of a single semantic artefact. Typically, the class of the entries for semantic artefacts inside catalogues i.e., when a catalogue hosts a semantic artefact, it is often concretely materialized by a record describing the artefact following the catalogue metadata model. For example, the record for CODO in BioPortal (https://bioportal.bioontology.org/ontologies/CODO) or the record for AGROVOC in AgroPortal (http://agroportal.lirmm.fr/ontologies/AGROVOC). • mod:SemanticArtefactService: A collection of operations providing access to one or more semantic artefacts or SemanticArtefact-based processing functions/services. Typically, the class of the services offered for semantic artefacts. For example, the REST API of BioPortal, the SPARQL endpoint of AgroPortal, or the browsing user interface of a SKOSMOS based service, a FAIR data point. By inheriting from dcat:Resource (itself an rdfs:Resource) and dcat:Dataset, the mod:SemanticArtefact and the mod:SemanticArtefactDistribution classes could be described by the properties compatible with and suggested by DCAT. Plus, we decided to adopt and generalize in the MOD namespace the properties from OMV [23], DOOR [24] and VOAF (http://purl.org/vocommons/voaf) as those vocabularies were very specific to ontologies/vocabularies but are not maintained anymore. 10 Finally, we also had to re-incorporate the metadata properties from the previous versions of MOD (v1.4) to the relevant class (SemanticArtefact or Distribution), as MOD v1.4 did not distinguish the two. These three steps gave us a set of 92 properties (for mod:SemanticArtefact) and 46 properties (for mod:SemanticArtefactDistribution) from which we selected respectively a subset of 41 (Table 1) and 24 (Table 2) properties for the voting workshop. We have not yet status on the properties for the other new classes created (Service, Catalog, CatalogRecord) but we anticipate specialized properties justifying the creation of a new subclass in MOD2.

FAIRsFAIR Minimum metadata recommendations for semantic artefact

After the workshop, the results were collected into a spreadsheet for evaluation. The inputs were the number of votes for each endorsement level mandatory-recommendedoptional. From them, we calculated the percentage of votes for each option. The option with the highest percentage was then selected. In cases where two options voted the same, the third was taken into account, e.g., 46.43% mandatory, 46.43% recommended, 7.14% optional would imply recommended, as 7.14% also voted for optional. As an auxiliary informative metric, we computed the consensus of the voting using the following formula: consensus = (0.333 + percentage of votes for the winning optionsum of percentages of the non-winning options) / 1.333.

Table 1 and Table 2 shows the result for all the properties voted.

6.

Results and applications

Inclusion of the metadata property requirements in MOD2

We included the requirements within MOD2 as additional information about a metadata property. With this, MOD encodes now three influential works motivating the presence of a property within the vocabulary: (i) the MIRO guidelines followed with using the property; (ii) the FAIR Principle addressed with using the property; and now (iii) the requirement in the FAIRsFAIR profile. For instance, in MOD2, the property mod:acronym is encoded as follow:

SemanticDCAT-AP and FAIRcat

The minimum metadata profile described in Section 5 have been encoded into RDF/OWL (prefix semdcat) [4] (Figure 2). This enables retrieval via simple SPARQL queries and also adding meta-properties (annotations). These meta-properties are:

• rdfs:definedBy -the object is a predicate that can be used to retrieve definition of the property. This is required because individual vocabularies employ different approaches e.g., skos:definition or rdfs:comment. • semdcat:endorsement -the endorsement level being Mandatory, Recommended, Optional. While it can be argued that "mandatory" can be alternatively expressed by OWL axioms, there is no way to express "recommended" without this extension. At the same time, this approach leads to a more complicated and non-standard RDF/OWL representation, as Object Properties can map only OWL Classes. As such, every property needs an extra class wrapper to be defined. We also explored an alternative way as SHACL 12 representation of the minimum metadata profile. SHACL shapes are an established way of specifying RDF graph constraints and are extensively used such as in the FAIR Data Point specification [23]. 13This approach allows elegant and straightforward specification of the set of properties, however there are two limitations:

1. Definitions cannot be linked to properties, they must be copied into sh:description.

2. Again, there is no way to express "mandatory". The approach taken was to extend the sh:PropertyShape with possibility to include she:endorsement predicate. As SHACL is RDF, it is formally possible, however such a SHACL file will not pass the standard "SHACL of SHACL" validity, which may be a problem for some checking tools. An example of the SHACL specification is shown in Figure 3. Within FAIRcat, mappings link attributes present in the source catalogue to the equivalent ones in SemanticDCAT-AP. Those mappings are created in an editor depicted in Figure 5. The form is generated from the OWL specification, which allows populating all properties and rendering their name, definition, and level of endorsement (red "M" = mandatory). Because catalogues provide their metadata typically in JSON(-LD), the source metadata properties are identified using JSONPath. Once the mapping step is finished, the harvested metadata can be converted into the SemanticDCAT-AP representation and stored in a FAIR Data Point [23] as illustrated in Figure 6. Apart from achieving SemanticDCAT-AP representation and FAIR-compliance, this approach enables harvesting multiple repositories into one FAIR Data Point that can be then searched; complex queries using SPARQL are also possible.

Onto4FAIR 2023 Workshops

Conclusions and perspective

This paper presents the first attempt to define a common minimum metadata profile for FAIR semantic artefacts. This profile has been developed with the inputs from a large variety of communities. It aims to set a threshold, below which, an artefact can hardly be considered FAIR. Such a minimum metadata profile will be useful for FAIR assessment tools such as O'FAIRe and FOOPS! in their future evolutions. With the FAIRCat prototype, three different semantic artefact catalogues have been harvested and mapped to this minimum metadata profile (via its implementation in SemanticDCAT-AP) in order to publish their content into a unique FAIR Data Point, allowing users to search across these three catalogues without copying the content or dealing with their specific APIs. This work is now consolidated and refined in the context of FAIR-IMPACT i.e., the MOD2 proposition as well as the FAIRsFAIR profile (and its experimental implementation SemanticDCAT-AP). The aim is to reach a unified community-driven "standard to describe semantic artefacts. In the future, we also plan to investigate the W3C Profile Vocabulary (DX-PROF -https://www.w3.org/TR/dx-prof) to express the profile and eventually use it to provide a specification of a standard Application Programming Interface that semantic artefact catalogues could implement.

Finally, the SemanticDCAT-AP machine-actionable representations are an essential piece in implementing the FAIR principles as it can be used by the community to develop software tools, such as is the example of the FAIRcat prototype but also in FAIRness Onto4FAIR 2023 Workshops assessment tools and any other relevant tools for semantic artefacts. Currently, FAIRcat is limited in its possibilities, for example it cannot harvest catalogues with complex APIs (or just in a limited way), but it demonstrates the idea. Currently, the mappings are stored into an in-house data model. As future work, these mappings will themselves be FAIRified, i.e., represented in a semantic way and stored with a persistent identifier.

The EarthPortal, towards an ontology

Introduction

As in other scientific fields, the communities involved in the Earth system need access to semantic artefacts [1] for different purposes (data integration, metadata management, semantic search, etc.). However, vocabularies have been developed independently for each of the different compartments of the Earth System (atmosphere, land surfaces, solid earth and sea), and have not been defined in a transdisciplinary way. Today, these domains have to work together to conduct their scientific research, and they need to evolve their practices and find better ways of combining and matching the different semantic artefacts in an effective manner and in a FAIR way [2]. To achieve this, one approach is to use an ontology repository to store and catalogue the various semantic artefacts (from vocabularies to ontologies) for Earth systems. It provides a centralized catalogue which allows to create and store mappings between concepts from different domains. Assessing the FAIRness of the stored semantic artefacts also becomes easier. The FAIR-IMPACT project supports the implementation of FAIR-enabling practices, tools and services across scientific communities at a a European, national and institutional level. The work-package 4 "Metadata and Ontologies" deals with greater Onto4FAIR 2023 Workshops and more harmonised use of semantic artefacts, leading to semantic interoperability between disciplines. In this perspective, implementation of semantic artefacts catalogues by new communities is supported. The approach described in this paper fits into this context.

After reviewing the different practices and tools [3] used by our communities to share and expose their semantic artefacts, we will focus on the solution chosen, the reasons for choosing this solution and the improvements planned to ensure that this ontology repository meets the requirements of the Earth System and Environmental Communities.

Semantic Artefact Management in Earth and Environmental Sciences

The two semantic artefacts commonly used in the Earth and Environmental Sciences are the SWEET ontology 1 (Semantic Web for Earth and Environment Technology Ontology) defined by the Semantic Technologies Group of ESIP 2 , and the GCMD Thesaurus (Global Change Master Directory) defined by the NASA Earth Science Data Systems 3 . However, since these semantic artefacts are high-level and do not cover all the features of the Earth System, some initiatives have been done by the communities to define dedicated semantic artefacts. In France, the Data Terra Research Infrastructure which covers the four Earth System compartments, has specified the vocabularies for each of them (Theia/Ozcar 4 , Aeris 5 , Odatis 6 and Formater 7 ), and is in the process of developing an ontology, based on the SOSA ontology [4], including different type of concepts (variables, platforms, sensors, feature types).

The climate community has also defined the Climate Analysis (CA) ontology [5] based on the SOSA ontology with the addition of meteorological and geographic elements associated terms. At the European level, Actris 8 , the research infrastructure supporting research into climate and air quality has built a controlled vocabulary 9 . EPOS, the infrastructure for the solid earth compartment is in the process of specifying its own 10 . The NERC Vocabulary server gives access to standardised and hierarchically organized vocabularies, for the oceanographic and associated domains [6]. For the environmental domain, the ENVO 11 ontology which represents knowledge about environments, environmental processes, ecosystems, habitats, and related entities has been set-up.

All these semantic artefacts, from vocabularies and thesauri to ontologies, need to be managed into specialized repositories in order to be used and shared by communities:

• OLS [7] provided by the EliXir community is a repository for biomedical ontologies that aims to provide a single point of access to the latest ontology versions. OLS has been chosen by the ENVO ontology to share the concept for the Environmental domain. However, the current version of OLS (OLS3) is no longer updated but it will be replaced by a newer on the end of October 2023. Unfortunately, it is not capable of hosting something else than ontologies in OBO and OWL formats. • Skosmos [8] is an open source web-based SKOS browser and publishing tool, mainly used to access controlled vocabularies for indexing, information retrieval and vocabulary development. Skosmos has been adopted by some communities to specify their vocabularies, such as the atmospheric one (e.g. AERIS and ACTRIS research infrastructures have both used Skosmos). Nevertheless, it is strictly restricted to the usage of SKOS and do not support ontologies. • VocPrez is a tool (API and web front-end) for the read-only delivery system of SKOS vocabularies, compliant with Content Negotiation by Profile. It has been adopted by the NERC Vocabulary Server from the BODC [9], the OGC Definition server or the British Geological Survey. However, VocPrez is restricted to a custom implementation of SKOS which can require adjustments if used with other tools. Its read-only structure also not suitable for users to be able to submit their own semantic artefacts. [11]. The technology was first reused by the Agrifood communities with the release of AgroPortal [12]. More communities adopted then this technology, with the addition of EcoPortal for the ecology part [13], MatPortal for materials science, IndustryPortal for industry and related domains.

Ontoportal and Ontoportal Alliance

The overview of the panel of technologies that we have seen previously makes one of the solutions stand out of the others: Ontoportal. This open source ontology repository and semantic artefact catalogue has several advantages over the others [3]: appliance 12 .

•
The OntoPortal initiative comes back from 2005, where the BioPortal code is first re-used for agrifood communities leading to the release of AgroPortal. More communities adopted then this technology, with the addition of EcoPortal, MedPortal, SIFR Bioportal, IndustryPortal and MatPortal, publicly available. All of these are part of the consortium known as the OntoPortal Alliance. The source code is located in a common repository which is then forked by each alliance member, making all public OntoPortal instances connected to it. This makes the feature development and integration easier: a community can merge code back to the shared repository, which can be then pulled to other communities in a streamlined workflow.

The choice of OntoPortal answers most of the issues currently encountered in the Earth systems community concerning semantic artefacts. There is a strong need of unifying multidisciplinary workflows to ensure semantic artefacts are up to FAIR standards, which can be answered with the tools available in OntoPortal. Moreover, the deployment of additional instances of OntoPortal for different thematics opens some opportunities, such as an inter-portal federation. While each domain has their own set of resources, there are still some of them that can be found overlapping with others (as in Earth sciences can overlap with Ecology, so EarthPortal with EcoPortal). While this is still at the stage of an idea, portals federation could enrich existing resources with even more links to related material to push multidisciplinary workflows and avoid duplication.

The EarthPortal

Earth sciences being multidisciplinary by nature, each sub-domain follows its own conventions in the creation and usage of semantic artefacts. However, when it comes to working together, it is common to witness similarities between standards, which then require to be manually curated to provide the necessary data and metadata crosswalks for joint efforts. For example, volcanologists and atmospheric specialists have to rely on space-based observations to track the evolution of eruptive activity in near real-time. To achieve this, scientists need to find and aggregate relevant datasets from Solid Earth and Atmospheric communities. This can be done by using an Interdisciplinary Discovery and Access Service [14] which can provide users with an easy and FAIR service for discovery and access to multidisciplinary and aggregated data sets. This service has to rely on a semantic component to ensure that the discovery process is as efficient as possible. The EarthPortal, the Ontoportal instance that will be dedicated to Earth systems, will centralize and catalog semantic artefacts from all sub-domains, to meet the following needs:

• Better discovery of the semantic artefacts used in Earth sciences. They are currently scattered across a significant amount of repositories even at sub-domain level, if available on the web at all. • Providing the necessary tools for interdisciplinary work. This will be mainly done by storing and creating mappings between vocabularies and ontologies from different sub-domains. • Assess the FAIRness of semantic artefacts. While their usage is strongly recomm--ended for data and metadata to be FAIR, vocabularies and ontologies also need their own metadata to reach FAIR standards. Although depositing artefacts in the repository already gives a certain level of FAIRness, the user must provide additional informations about their submissions. The O'FAIRE [15] tool initially developed by the AgroPortal team will be used in that regard.

The EarthPortal will allow users to submit their own semantic artefacts, but will also contain links to existing vocabularies and ontologies. Since it is not possible to ask all actors from Earth science communities (or even broader communities in the case of multidisciplinary standards) to store all their resources into the same repository, the OntoPortal technology also allows external resources to be referenced and work with as if they were included. This initiative being driven at a general level, the usage of EarthPortal can see the emergence of requests from the different Earth systems communities, leading to the development of new features. One of the leads towards the evolution of this technology concerns semantic artefacts mapping. Similarly to data and semantic artefacts, mappings can also embed metadata. While most of the information is similar, mappings are also subject to automation (i.e. lexical matching) which then deliver additional metadata about the results of this automation process. SSSOM [16] might be a good candidate to issue standards concerning mapping sharing. Since it is one of the main features of the EarthPortal, integration of SSSOM is being considered.

Conclusions

The usage of the OntoPortal technology to manage Earth system semantic artefacts is motivated by the panel of tools and flexibility it provides. Its dedicated instance for Earth science called EarthPortal will be part of the OntoPortal alliance, which brings potential for collaborations with other members. Not only it offers additional technical insight but also a way to support the EarthPortal in its early stages, to manage user feedback and develop new features. The first step will be to collect and reference the semantic artefacts used in each sub-domain of Earth system, then enrich them with mappings and metadata to ensure compliance with the FAIR principles. Of course, this is not without a cost. Users might not understand the entire scope of the thesauri, vocabularies and ontologies they use nor do they have the knowledge to do so. The EarthPortal will bring all the necessary features to lower the entry bar for the creation and usage of semantic artefacts by listening to feedback from each community and developing the corresponding features. Furthermore, in the context of a collaboration between FAIR IMPACT, in charge of the EarthPortal development, and FAIR-EASE, another European Onto4FAIR 2023 Workshops project, the content of the EarthPortal repository could be used to enhance the semantic analyser planned by the FAIR-EASE project.

Introduction

Scientific research has long been conducted on datasets that measure and model aspects of the world. This phenomenon is becoming more acute given the vast amounts of datasets [1,2] released in the present age of data science [3]. Datasets are as diverse as science is [4,5]. For instance, datasets in medicine can reflect patient disease histories, datasets in earth science can reflect geological or climatic features of the world, while datasets in artificial intelligence model phenomena as machine learning objectives for a computer. The large volume of datasets released on the Web opens up new avenues for their search and discovery. Search engines now offer dedicated search platforms such as Google Dataset Search [6] for discovering datasets from various public repositories such as OpenAIRE [7] and Zenodo2 . To make datasets discoverable, dataset publishers must offer the dataset metadata information per certain prescribed formats. For example, Google advocates the Dataset3 type. However, many research datasets 4 do not come with a sufficient amount of structured metadata that fully describe their usage potentials. Therefore, discovery mechanisms used by search engines nowadays cannot detect and expose them to users. We have observed that metadata such as title, publisher, etc., contingent on their accuracy and maintenance [8], are insufficient to describe a research dataset's full context and content. Therefore, such limited metadata proves fairly uninformative to guarantee that relevant research datasets will be discovered on the Web. Studies show that, in academia, the predominant search pattern for research datasets is either a serendipitous event of finding a dataset when reading scholarly publications or actively searching for datasets in publications [4]. This emphasizes that the content of scholarly publications, which gives insights into datasets, is necessary for dataset discovery [4]. Prior work highlights three criteria based on which users select datasets: relevance, usability, and quality [9]. These factors, resulting from human data interaction studies [10,11,12,13,14,15], necessitate the supply of content and context information of a dataset (e.g. domain(s) the dataset covers, source(s) it was gathered from, and metrics to evaluate it) to support informed decisions of its use for a task. We observe that such information is found in the scholarly publications that describe research datasets [4].

Therefore, the way forward toward improved research dataset discovery is to complement its metadata with a structured representation of the contributions of its accompanying scholarly article. To adhere to current standards of semantic descriptions, the representation of these contributions should be findable, accessible, interoperable, and reusable, i.e. it should adhere to the FAIR principles [16]. To this end, semantic publishing models [17] of scholarly contributions such as the Open Research Knowledge Graph (ORKG) can be directly leveraged [18]. The ORKG publishing model presents a nextgeneration skimming device of scholarly contributions, that permits viewing their semantic representations in a similar way to comparisons of products on e-commerce websites [19]. Thus, to model research datasets, we utilize the ORKG content type, which is a typed resource with a class from a predefined set of classes.

In this paper, we present the ORKG-Dataset content type -a specialized branch of the ORKG. The design of ORKG-Dataset was driven by three main research questions (RQs). RQ1: How to present structured research dataset descriptions within the semantic web scholarly publishing model as knowledge graphs (KGs)?; RQ2: Which salient features can be extracted from scholarly article descriptions that serve the dataset selection criteria of relevance, usability, and quality?; and RQ3: How can such a representation benefit others in terms of creating customizable snapshots of specific information?

The rest of this paper is structured as follows: Section 2 presents the design principles of ORKG-Dataset and how they were met within the ORKG, addressing RQ1. Section 3 demonstrates an application example of ORKG-Dataset on the scholarly publications of 40 research datasets used for scientific information extraction (IE), addressing RQ2 and RQ3. Finally, Section 4 concludes our paper.

Design Principles of ORKG-Dataset

Although previous work has been conducted to describe the content and context of datasets, it is not fine-grained enough in terms of the properties it offers. For example, the Data Source Description Vocabulary5 and the Data Catalog Vocabulary (DCAT) 6hav no possibility to describe models trained on the dataset and their evaluation scores. Additional resources for describing datasets such as releasing datasheets [20] are not modeled using semantic web technologies that enhance the usability and discoverability of datasets.

With this outlook, we outline the principles of ORKG-Dataset design using the following scenario. Imagine a researcher looking for research datasets in a particular field. Her search can be characterized based on two activities elicited in prior work [15], (1) linking (i.e. "finding commonalities and differences between two or more datasets" [15]) and (2) time series analysis (i.e. ordering datasets on a timeline). In the present status quo of scholarly communication, a large share of the information discussed above is already available, but it is hidden within the unstructured text of scholarly articles accompanying the research datasets. However, searching for a dataset by examining unstructured text descriptions involves significant cognitive tie-ups which boils down to finding a needle in a haystack. The researcher would need to sift through millions of results from academic search engines, identify those that actually contribute a dataset, and tediously search through the articles for key information before finding a suitable dataset.

The ORKG-Dataset content type introduced in this paper is one step in a long-term research agenda of the ORKG to bring about a paradigm shift from document-based to structured knowledge-based scholarly communication [18]. Specifically, the salient aspects of research are encoded as structured property-value pairs within the ORKG. Given the salient structured format applied on scholarly communication, the two search characteristics, i. e., linking and time series analysis, are directly supported in the interaction mechanisms of the ORKG front-end interface. Several structured papers with similar properties can be combined and placed next to each other within a comparison view [19]. The ORKG platform combines semantic web technologies with front-end design components and back-end storage and query systems [21]. It utilizes Resource Descrip-tion Framework (RDF) as its default graph data representation language 7 , connecting ontologies through subject-predicate-object triples. The front-end interfaces are built with ReactJS, fetching data from back-end APIs, while the Neo4J storage software8 enables effective data querying using SPARQL 9 .

Our proposed design requires each paper contribution to be typed as both the default https://orkg.org/class/Contribution class and the https://orkg.org/ class/Dataset class. This is critical to separate other kinds of research contributions from research dataset contributions in the ORKG. We identified the following design requirements in order to generate comparable and wholesome dataset structured representations as the ORKG-Dataset content type.

• Standardized Nomenclature: We established a standard nomenclature for research datasets, starting with a controlled vocabulary that can later evolve into an ontology. This is achieved by reusing concepts from existing metadata ontologies such as https://schema.org/Dataset. We then added predicates specific to research datasets in the ORKG web namespace to enhance the vocabulary. To establish further equivalences between predicates in different ontologies, the RDF same-as relation was utilized. • Use of Templates: to maintain consistent formatting when recording new research datasets, it was essential to define a form-based template comprising a set of predetermined relevant predicates. The ORKG facilitates this requirement by implementing a template system that consists of recurring subgraph property patterns 10 . These templates enable the specification of commonly applicable properties across multiple research contributions within a KG. • FAIR Standards Compliance: the third and final requirement is that the ORKG-Dataset model should be compliant as much as possible with the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles laid out for scientific information [16]. Addressing findability, the ORKG has a system to assign digital object identifiers (DOIs) to aggregate component parts of its graph data, making them available in global scholarly infrastructures such as DataCite and Crossref [22]. ORKG resources are also findable via regular search engines due to being published on the web. In terms of accessibility, all ORKG components are accessible by HTTP protocol via REST or its user interface. Additionally, the actual graph data is separated from metadata making it possible to access the latter without the former [19]. The ORKG satisfies interoperability by using RDF, the recommended format of the W3C for representing knowledge on the web in a machine-accessible format. To ensure reusability, provenance metadata information is created automatically when publishing structured contributions in the ORKG, and the graph data is published as CC BY-SA. 11

The ORKG-Dataset Application

In this section, we demonstrate a use-case application of the ORKG-Dataset content type on research datasets in the field of natural language processing (NLP) on the research problem of scientific information extraction.

Datasets Curation

As a first step, we manually curated a collection of research datasets relevant to the problem at hand. We searched through benchmarking catalogs such as PapersWith-Code 12 , competition websites such as Kaggle 13 , academic search engines such as Google Scholar 14 , and systematic review papers [23]. From these sources, we finally arrived at a representative list of 40 research datasets spanning the years 2011 to 2022.

Datasets Semantic Representation

The next step was to create a structured representation for each of the research datasets based on their accompanying paper contributions. We, as a team of four annotators specialized in the field of scientific information extraction, followed an iterative methodology to identify the main contribution components. Our discussions resulted in the following main facets of information. • Quality: one way to reflect a dataset's annotation quality is by inter-annotator agreement (IAA) scores, e.g. by using Cohen's kappa [24]. This quality indicator can be specific to different information scopes, such as entities, relations, or sentences. To represent this information, we created the Data-centric result template (https://orkg.org/template/R220939), which records the evaluation score and linked metric using the QUDT standardized methodology for evaluations (https://qudt.org/schema/qudt/Quantity). Additionally, the has In this vein, we reused 19 relevant properties from the https://schema.org/ Dataset content type. Some of the properties are name, alternatename, asseses, description, and URL. Of particular interest is the URL property which is used to record the URL source where the dataset can be downloaded. These properties were modeled as the generic Dataset template (https://orkg.org/template/ R178304) and could be uniformly applied across the 40 papers.

Putting all the pieces together, each of the 40 structured papers was finally typed with the ORKG-Dataset content type class https://orkg.org/class/Dataset which links to the generic Dataset metadata template. The result of our annotations is publicly accessible as an ORKG Comparison view https://orkg.org/comparison/ R280270/. Figure 1 shows a partial screenshot.

Customizable Querying

A unique benefit of the semantic representation of research datasets in the ORKG is the ability to create customizable snapshots of the interconnected data graph in the context

28

Onto4FAIR 2023 Workshops of the larger graph data capturing various other kinds of scholarly contributions. This presents users with advanced search and selection options that can be implemented via SPARQL queries. We elicit this in the following three scenarios.

Bibliometric view.

With regard to bibliometrics, one could obtain detailed metadata about the authors, publication dates, and citation numbers. For example, researchers could get citation statistics and obtain the most cited dataset for a particular task during a specific period, in order to get some idea of the design and specificities of datasets that had a high impact in the community.

Dataset view.

Another example query is finding ground-truth datasets that could be used to train a model to solve a particular task. One such example query can be found in Figure 2. In addition, it is possible to filter for datasets that label particular entities, such as "methods" or "materials" (Figure 3), and which match particular inter-annotator agreement scores if available. Most of the papers come with the actual URLs under which the published ground-truth datasets could be found. Thus, one could fetch the URL for all datasets of interest.

SELECT ?task (GROUP_CONCAT(?dataset;separator=',') AS ?dataset) WHERE { res:R280270 pred:compareContribution ?contribution . ?contribution a class:Dataset ; rdfs:label ?dataset . ?contribution pred:P32/rdfs:label ?task } GROUP BY ?task SELECT DISTINCT ?concept GROUP_CONCAT(?dataset;separator=',') WHERE { res:R280270 pred:compareContribution ?contribution . ?contribution a class:Dataset ; rdfs:label ?dataset . ?contribution pred:P34062/rdfs:label ?concept . FILTER( ?concept = "Method"^^xsd:string OR ?concept = "Research problem"^^xsd:string) } GROUP BY ?concept An integrated comparison table as ours also allows to uncover implicit relations between entities, for example, to search for competing, state-of-the-art machine learning models.

Competing models are models that have been used to solve a similar NLP task, whereas a non-competing model would be a model that has not been used directly to solve the same task [START_REF] Daw | Extraction of Competing Models using Distant Supervision and Graph Ranking[END_REF]. We could also filter out models that score above a particular evaluation metric, for example, models which score about 0.7 value in F1-score. Querying for the mostly used evaluation metrics could give researchers an idea of the metrics most widely used by the community.

Conclusion

In this article, we presented the ORKG-Dataset content type as an approach for the semantic publishing of research datasets. The ORKG-Dataset content type breaks new ground in two main aspects: 1) in the studies of the transition from document-based to structured knowledge-based scholarly communication; and 2) of moving away from just metadata-based semantic descriptions of research datasets to including salient features of their accompanying scholarly publications as an enriched and more informative representation. Future developments of the ORKG-Dataset will apply it to more fields and thus make it more generalizable, as well as refine the properties to describe the quality of datasets using metrics beyond IAA.

Figure 1 .

 1 Figure 1. MOD2 proposed model for semantic artefact and their catalogues.

Figure 2 .

 2 Figure 2. SemanticDCAT-AP's OWL representation in Protégé illustrating the annotations of properties.

Figure 3 .

 3 Figure 3. SemanticDCAT-AP SHACL definition example. 3rd property illustrates the endorsement extension.

FAIRcat [ 4 ]

 4 is a proof-of-concept application, based on the federated FAIR Data Space, 14 that utilizes the described OWL-based machine-actionable SemanticDCAT-AP representation and demonstrates the potential of the common minimum metadata for FAIR semantic artefacts. At the same time, it represents a solution that can be used to increase FAIRness of semantic artefacts without any time and resource investments at the side of repository providers. The idea of FAIRcat is depicted in Figure4. Semantic artefact catalogues were harvested for their items metadata. Using mappings, they are converted into the SemanticDCAT-AP and stored into a FAIR Data Point, the FAIRsFAIR Reference FAIR Data Point in our case.15

Figure 4 .

 4 Figure 4. FAIRcat: harvest semantic artefact catalogues content and map them to a common profile.

Figure 5 .

 5 Figure 5. Illustration of mapping repository semantic artefacts to the SemanticDCAT-AP in FAIRcat.

Figure 6 .

 6 Figure 6. SemanticDCAT-AP metadata of the BioPortal semantic artefacts stored in a FAIR Data Point.

Figure 1 .

 1 Figure 1. Excerpt of a screenshot of research datasets addressing scientific IE in the ORKG comparison view with structured metadata descriptions based on a set of properties defined as the ORKG-Dataset content type. The full comparison of 40 research datasets is accessible at https://orkg.org/comparison/R280270/.evaluation item property (https://orkg.org/property/P71154), modeled by the nested template Evaluation item (https://orkg.org/template/R221194), allows specifying the granularity of annotations. This comprehensive unit of information serves as one direct indicator of dataset quality.• Performance Benchmarks: Scholarly articles of research datasets report performance benchmarks. We identified this as an indirect quality indicator for dataset consumers. We modeled this aspect with the help of the existing Leaderboard template (https://orkg.org/template/R107801) which includes properties that allow specification of model name, model code URL, and allows specifying the score and metric per QUDT standards. • Metadata: technologies for efficient and effective reuse of ontological knowledge are one of the key success factors for developing ontology-based systems[START_REF] Simperl | Reusing ontologies on the Semantic Web: A feasibility study[END_REF].In this vein, we reused 19 relevant properties from the https://schema.org/ Dataset content type. Some of the properties are name, alternatename, asseses, description, and URL. Of particular interest is the URL property which is used to record the URL source where the dataset can be downloaded. These properties were modeled as the generic Dataset template (https://orkg.org/template/ R178304) and could be uniformly applied across the 40 papers.

Figure 2 .

 2 Figure 2. Example query to obtain a list of ground truth datasets and the tasks they address. Full query: https://tinyurl.com/query-example-1.

Figure 3 .

 3 Figure 3. Example query to filter for datasets that label "Method" and "Research problem" as labeled entity types in the ground truth. Full query: https://tinyurl.com/query-example-2.

  Garijo et al. proposed FOOPS! a Web service for assessing an ontology regarding the FAIR principles [15]. • Late 2020, Cox et al. proposed guidelines ("10 simple rules") for making a vocabulary FAIR (https://fairvocabularies.github.io/makeVocabularyFAIR) and transform vocabularies that are not available following Web standards [16].

	However, the authors do not explain how the proposed rules are aligned to each
	individual FAIR principle.
	• A list of functional metrics and recommendations for Linked Open Data Knowledge
	Organization Systems (LOD KOS) was proposed in 2020 [17].
	• In the end of 2020, DBPedia Archivo [18], an ontology archive, was released to
	help developers and consumers to implement FAIR ontologies. The prototype
	automatically discovers, downloads, archives, and rates new ontologies
	(https://archivo.dbpedia.org). Unfortunately, this work had not been inspired by
	existing research methodologies/tools.
	• In 2021, Amdouni et al., introduced an "integrated quantitative FAIRness
	assessment grid for semantic resources [6]. This work was nourished and aligned
	with relevant state-of-the-art initiatives for FAIRness assessment: the RDA FAIR
	Data Maturity Model, the RDA Sharing Rewards and Credit evaluation table, the 5-
	stars for vocabulary as well as FAIRsFAIR and Poveda et al. recommendations
	cited above. The grid dispatches different credits to each FAIR principle, depending
	on its importance -according to pre-existing initiatives-when assessing FAIRness.
	• Early 2022, the same authors proposed a metadata-based automatic FAIRness
	assessment methodology for ontologies and semantic resources called Ontology
	FAIRness Evaluator (O'FAIRe), based on the grid described previously

Table 1 .

 1 List of properties, and decision for the mod:SemanticArtefact class.

	Property	# of	Consensus	Decision
		votes
	dct:title	23	86.95%	mandatory
	dct:license	28	78.57%	mandatory
	dct:identifier	28	67.85%	mandatory
	dct:accessRights	31	66.12%	mandatory
	dct:creator	31	66.12%	mandatory
	dct:created	13	53.83%	mandatory
	dct:description	28	51.77%	mandatory
	dcat:contactPoint	31	46.76%	mandatory
	owl:versionIRI	13	42.29%	mandatory
	dct:modified	28	35.70%	mandatory
	dcat:keyword	31	32.24%	mandatory
	mod:acronym	13	30.75%	mandatory
	dcat:landingPage	31	22.56%	mandatory
	dct:publisher	28	19.62%	recommended
	dct:subject	13	19.21%	mandatory
	dct:type	22	18.16%	mandatory
	dct:issued	27	16.65%	mandatory
	dcat:theme	30	9.98%	mandatory
	dct:conformsTo	30	14.98%	recommended
	dct:language	28	19.62%	recommended
	mod:URI	13	19.21%	optional
	dcat:distribution	25	15.98%	recommended
	dct:contributor	13	30.75%	recommended
	dct:rights	23	34.77%	recommended
	dct:temporal	24	6.23%	recommended
	dcat:qualifiedRelation	29	27.57%	optional
	mod:status	13	42.29%	recommended
	odrl:hasPolicy	23	21.72%	optional
	prov:qualifiedAttribution	23	41.29%	optional
	prov:wasGeneratedBy	26	13.44%	optional
	dct:relation	22	18.16%	optional
	dct:isReferencedBy	28	14.26%	optional
	schema:includedInDataCatalog	12	37.48%	optional
	mod:competencyQuestion	13	65.38%	optional
	dct:accrualPeriodicity	25	21.98%	optional
	dct:spatial	24	18.73%	optional
	mod:usedEngineeringMethodology	12	24.98%	recommended
	dcat:temporalResolution	25	39.98%	optional
	mod:hasFormalityLevel	13	19.21%	recommended
	dcat:spatialResolutionInMeters	25	57.99%	optional
	dct:accrualMethod	13	42.29%	recommended

Table 2 .

 2 List of properties, and decision for the mod:SemanticArtefactDistribution class.

	Property	# of	Consensus	Decision
		votes
	dcat:mediaType	17	47.05%	mandatory
	dct:format	17	47.05%	mandatory
	dct:title	15	39.98%	mandatory
	dcat:accessURL	17	38.22%	mandatory
	mod:hasRepresentationLanguage	10	24.98%	mandatory
	mod:hasSyntax	10	24.98%	mandatory
	dct:accessRights	17	20.57%	mandatory
	dcat:downloadURL	17	20.57%	recommended
	8		Onto4FAIR 2023 Workshops

•

  UKGovLD is a Linked Data Registry developed by the UK Government, to manage code lists or controlled vocabularies, which support content negotiation. It provides a service to create and manage controlled, authoritative lists of identifiers as URIs. It is used by the French geological survey (BRGM) to define and expose their vocabularies and by the French Data Terra RI, as a FAIR incubator for the concepts used by its data clusters. Similarly to other vocabulary browsers, it does not support ontologies.• LOV (Linked Open Vocabularies) is a catalogue of reusable vocabularies for the description of data on the Web. LOV supports Vocabulary Search, Vocabulary Assessment, Vocabulary Mapping[10]. Anyway, it does not provide any service beyond cataloguing vocabularies. • Ontoportal is an open source and generic technology to build ontology repositories or semantic artefact catalogues which has been set-up by Stanford University in 2005, for the biomedical ontologies with the BioPortal appliance

  It regroups the features of some other technologies and combine them with new ones, such as mappings creations and storage, FAIRness assessment, text Onto4FAIR 2023 Workshops annotation, version management, a REST API to read and write content. It has the advantage of being domain agnostic and support a wide range of semantic artefacts from SKOS thesauri to complex ontologies in different formats.• It is actively maintained, the Ontoportal alliance is regularly pushing fixes and new features through the centralization of the code repository • The technology is well proven today. OntoPortal comes from the NCBO BioPortal that is available since 2005 and is widely used across biomedical communities but also with AgroPortal and the agrifood community. • It can be easily deployed in a pre-configured Open Virtual Machine Format (OVF)

By "crosswalked", we mean that we have identified 346 metadata properties in those metadata vocabularies that, once mapped (crosswalks identified), bring us to 128.

This model is still being consolidated (now in the context of the Horizon Europe FAIR-IMPACT project: https://github.com/FAIR-IMPACT/MOD), but the minimal model can be presented here independently.

https://www.w3.org/TR/swbp-vocab-pub

For instance, MOD does not require the use of a specific authorship property but rather encodes that dc:creator; schema:author, foaf:maker, or pav:createdBy can be used to say so.Onto4FAIR 2023 Workshops

https://github.com/FAIRsFAIR-Project/FAIRSemantics/issues

MOD2 proposition was released in 2020 on GitHub and presented in multiple workshops and talks. However, the new model made of the 5 new classes presented here has never been published in a scientific communication yet.

We now use the expression "Semantic Artefact Catalogue".Onto4FAIR 2023 Workshops

Furthermore, we were motivated by unifying all the metadata properties specific to semantic artefacts.Onto4FAIR 2023 Workshops

https://github.com/FAIR-IMPACT/MOD/blob/master/mod-v2.0_profile.ttlOnto4FAIR 2023 Workshops

https://www.w3.org/TR/shacl

https://specs.fairdatapoint.org

https://www.eosc-pillar.eu/federated-fair-data-space-f2dsOnto4FAIR 2023 Workshops

https://github.com/FAIRDataTeam/FAIRDataPoint

Onto4FAIR 2023 Workshops

Onto4FAIR 2023 Workshops

https://ontoportal.github.io/documentation/administration/steps/getting_started

Corresponding Author: Raia Abu Ahmad, raia.abu ahmad@dfki.de.Onto4FAIR

Workshops

July 2023

https://zenodo.org

https://schema.org/Dataset

Note in this work we draw a distinction between the generic concept of a dataset on the Web and research datasets, in particular. Research datasets are those that are outcomes of a research endeavor and are thereby accompanied by a scholarly publication.

https://dqm.faw.jku.at/ontologies/dsd/4.0.0/index.html

https://www.w3.org/TR/vocab-dcat-3/ Onto4FAIR 2023 Workshops

https://www.w3.org/RDF/

https://neo4j.com

https://www.w3.org/TR/rdf-sparql-query/

For more information about templates in the ORKG: https://orkg.org/about/19/Templates

https://creativecommons.org/licenses/by-sa/2.0/

https://paperswithcode.com/datasets

https://www.kaggle.com/datasets

https://scholar.google.de Onto4FAIR 2023 Workshops

Acknowledgments

We thank all the participants of the workshops and meetings organized by both the RDA VISSG and FAIRsFAIR project for time and fruitful discussions and feedback. This work has been originally supported in part by the H2020 FAIRsFAIR project (www.fairsfair.eu -grant #831558) as well as the Data to Knowledge in Agronomy and Biodiversity project (D2KAB -www.d2kab.org -ANR-18-CE23-0017). This work is now published -and continued-with support from the Horizon Europe FAIR-IMPACT project (https://fairimpact.eu -grant #101057344).

Funding Statement

This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -project number: NFDI4DataScience (460234259).

6.2.

Example of an ontology described with some mandatory metadata http://myontologyIRI.org rdf:type owl:Ontology; mod:SemanticArtefact ; dcat:distribution http://myontologyIRI.org/distribOWL , http://myontologyIRI.org/distribPDF ; mod:acronym "MYON" ; dcterms:title "My ontology" ; owl:versionIRI <http://myontologyIRI.org/v1.0> ; dcterms:identifier "myontologyDOI" ; dcterms:license <https://creativecommons.org/licenses/by/3.0> ; dcat:landingPage "myontologyWebPageURL" ; dcterms:creator "http://orcid.org/0000-0002-2404-1582" ; dcterms:created "2023-05-01"^^xsd:dateTime ; dcterms:modified "2023-07-15"^^xsd:dateTime . http://myontologyIRI.org/distribOWL mod:hasRepresentationLanguage <http://www.w3.org/2002/07/owl>; mod:hasSyntax <http://www.w3.org/ns/formats/RDF_XML>; dcterms:description "Distribution of My Ontology in OWL"; dcat:accessURL "myontologycataloguerecordURL" . b TIB -Leibniz Information Centre for Science and Technology, Hannover, Germany c GESIS -Leibniz Institute for the Social Sciences, Köln, Germany ORCiD ID: Raia Abu Ahmad https://orcid.org/0009-0004-8720-0116, Jennifer D'Souza https://orcid.org/0000-0002-6616-9509, Matthäus Zloch https://orcid.org/0009-0007-1692-7053, Wolfgang Otto https://orcid.org/0000-0002-9530-3631, Georg Rehm https://orcid.org/0000-0002-7800-1893, Allard Oelen https://orcid.org/0000-0001-9924-9153, Stefan Dietze https://orcid.org/0009-0001-4364-9243, Sören Auer https://orcid.org/0000-0002-0698-2864