Jérôme Mathieu
email: jerome.mathieu@upmc.fr

EGrowth: a global database on intraspecific body growth variability in earthworm

Keywords: Body size, Life history traits, Interspecific variability, Intraspecific trait variability (ITV), Database, Allometry 1

Earthworms play a key role in soil and ecosystem functioning. Predicting their abundance and spatial distribution is required to understand their ecological role. There is growing evidence that mechanistic models of earthworm population dynamics are promising tools to tackle this issue.

However, this approach requires a fair amount of data because it explicitly integrates the three fundamental biological processes: growth, reproduction and mortality. Hitherto, the lack of comprehensive databases on life history parameters related to these three processes hampered the widespread development of mechanistic earthworm population dynamics models. As a consequence, predicting earthworm abundance in a variety of conditions across species is still difficult.

earthworms.

The clear bottleneck for making progress is the lack of databases on the intraspecific variability of earthworm life history traits in response to environmental conditions. Data related to body growth and body size are critical because body size largely determines reproduction and mortality rates.

Body growth is therefore the backbone of mechanistic models of earthworm population dynamics.

Here I present EGrowth, the first comprehensive database on intraspecific variability of earthworm body growth in relation to environmental conditions. The EGrowth database contains 1073 growth curves of 51 species of earthworms, representing 16002 measures of body mass. It covers publications on earthworm body size from 1900 to 2016. The environmental conditions in which the growth curves were produced are also reported. The database is open access and can be browsed from a graphical user interface. EGrowth will be updated regularly in the future as new studies are published. I propose a standardized framework for reporting future data on body growth of Introduction Earthworms play an important role in soil functioning [START_REF] Lavelle | Earthworm activities and the soil system[END_REF]. For instance, they modify soil structure, bulk density and aggregate stability, with direct consequences for water infiltration rates and hydrological conductivity [START_REF] Blanchart | Restoration by earthworms (megascolecidae) of the macroaggregate structure of a destructured savanna soil under field conditions[END_REF][START_REF] Bossuyt | Protection of soil carbon by microaggregates within earthworm casts[END_REF][START_REF] Van Schaik | Linking spatial earthworm distribution to macropore numbers and hydrological effectiveness[END_REF]. They also affect nutrient and carbon fluxes through their effect on decomposition and microbial activity [START_REF] Pashanasi | Effects of the endogeic earthworm Pontoscolex corethrurus on soil chemical characteristics and plant growth in a low-input tropical agroecosystem[END_REF][START_REF] Richardson | Nutrient and pollutant metals within earthworm residues are immobilized in soil during decomposition[END_REF]. These modifications impact vegetation [START_REF] De Deyn | Soil invertebrate fauna enhances grassland succession and diversity[END_REF][START_REF] Hattenschwiler | Soil animals alter plant litter diversity effects on decomposition[END_REF][START_REF] Laossi | Effects of endogeic and anecic earthworms on the competition between four annual plants and their relative fecundity[END_REF] and climate dynamics [START_REF] Lubbers | Greenhouse-gas emissions from soils increased by earthworms[END_REF][START_REF] Zhang | Earthworms facilitate carbon sequestration through unequal amplification of carbon stabilization compared with mineralization[END_REF]. A number of studies have shown that all these effects quantitatively depend on earthworm abundance and traits, which are themselves constrained by environmental conditions (e.g. [START_REF] Jouquet | Above-ground earthworm casts affect water runoff and soil erosion in Northern Vietnam[END_REF][START_REF] Van Schaik | Linking spatial earthworm distribution to macropore numbers and hydrological effectiveness[END_REF]. Understanding how the environment impacts earthworm abundance and traits in a quantitative way is therefore a key requisite for a better grasp of their role in soil functioning. However, predicting earthworm abundance and traits in a specific context is very challenging because soils are extremely heterogeneous even at fine scales.

In addition, ecological preferences vary among species. A major challenge is thus to develop a quantitative understanding of earthworm ecology for predicting earthworm abundance and traits in relation to ecological conditions.

A promising approach for tackling this issue is the development of process based models (Jager et al., 2006;[START_REF] Kooijman | Dynamic Energy Budget theory for metabolic organisation[END_REF]. They have been used for a variety of purposes such as understanding the spatial structure of earthworm populations, the impact of earthworms on infiltration rates, or the effect of pesticides on earthworm population dynamics [START_REF] Barot | Self-organization in a simple consumer-resource system, the example of earthworms[END_REF]Baveco and De Roos, 1996;Schneider and Schroder, 2012;Vorpahl et al., 2009). These models mimic growth, reproduction and death of individuals or cohorts, in order to predict population dynamics. They require a fair amount of data to parametrize the modelled processes, which has been identified as the primary bottleneck for the development of such approaches (Schneider and Schroder, 2012).

Data on body growth variability, and thus on intraspecific body size variability (IBSV), are critical because body size determines the rate of reproduction and life span [START_REF] Brown | Toward a metabolic theory of ecology[END_REF]. Indeed cocoon production, cocoon hatchability, food consumption, and longevity all depend on the body size of earthworms (Daniel, 1990;Michon, 1954). In turn, the population dynamics and ecological effects of earthworm are also related to their body size [START_REF] Brown | Toward a metabolic theory of ecology[END_REF].

In order to build mechanistic models of body growth, we need to estimate the effects of the environment on growth patterns. For this we need databases that document the respective effect of a variety of environmental factors. This kind of data is typically available from experiments in controlled conditions, where only the factor(s) studied vary. A large number of studies have reported this type of experiments on earthworms. However, as far as I am aware, there is no comprehensive database compiling data on the effect of the environment on intraspecific body size variability in earthworms.

Building a database on IBSV that covers a wide spectrum of earthworm species is challenging in several aspects. The first inherent difficulty is that the shape of body growth curve varies among studies [START_REF] Grimm | Life-history trait database of European reptile species[END_REF]. In particular, growth curves can be non-monotonic because body size can regress, present oscillations or can follow a staircase shape (Lakhani and Satchell, 1970;Michon, 1954;Tondoh and Lavelle, 1997). Hence, data on body size without reference to the shape of growth pattern have limited utility for modelling the effect of the environment on body size. The ideal structure of a global database should include this diversity of growth patterns, and should enable generalization to species for which growth form has not been measured so far. A simple way to achieve this is to build IBSV databases on body growth curves -i.e. ontogenetic growth. Body growth curves are measures of body mass at different times on the same individual or on the same population. This approach allows the modelling of body growth and the calculation of a variety of body growth parameters that can be compared among ecological conditions and across species [START_REF] West | A general model for ontogenetic growth[END_REF].

A second difficulty for building databases on IBSV is the retrieval of the conditions during growth.

Having this information is critical for identifying the drivers of IBSV, and to quantify their effect on body size. This information also allows parametrizing reaction norm functions, which are mathematical models that predict body growth in relation to environmental conditions such as temperature [START_REF] Angilletta | Temperature, Growth Rate, and Body Size in Ectotherms: Fitting Pieces of a Life-History Puzzle1[END_REF][START_REF] Gillooly | Effects of Size and Temperature on Metabolic Rate[END_REF][START_REF] Ray | The application of Bergmann's and Allen's rules to the poikilotherms[END_REF]. Retrieving environmental conditions in reports on body growth of earthworm is however challenging because there are no standardized guidelines to present this type of information. As a result, these data are often missing or dispersed within the documents.

Lastly, the data by themselves are difficult to access because they are scattered in many articles, in different journals and in different kind of reports. The title and summary of the documents often do not reveal the presence of data on body growth. Growth curve data were often published in early articles that are not recorded in search engines and not available in electronic format. Data are usually difficult to reuse because they are presented only in graphical form, without the corresponding raw data. This implies a manual digitalization of the figures to reuse the data.

Furthermore, centralizing existing data on body size and growth of soil animals is necessary, not only for making these data easily reusable, but also to ensure that these data will not be definitively lost in the future.

In this paper, I present the EGrowth database. The database compiles existing data about body growth of 51 species of earthworm. It actually contains more than 16000 body mass measures, representing more than 1000 growth curves. The database is open access and can be accessed in a variety of manner. It can be downloaded or accessed through a Graphical Interface (GUI) from R or from internet. This database will be updated in the future with new studies. In order to facilitate this process, I propose a standardized framework for reporting future data on body growth of earthworms. Metadata such as the number of measures per point, temperature and treatments were searched for manually in the text and included in the database, when available. The authors of the articles with missing data were contacted in order to complete the database. All articles were stored as pdf files in an online folder and can be accessed upon request.

Overall, 414 publications were analysed, from which only the ones with at least four monitoring dates were retained. As a consequence, many articles, in particular the ones that used Instantaneous

Growth Rate (IGR) -the difference of (log) body mass between two dates -, were not considered.

Studies that used adults at the beginning of the experiments were also discarded. At the end 162 publications were used to build the database. The list of articles is given in supplementary material.

Database structure

The database is organized in three tables (Fig. 1), which are described in detail in supplementary material.

[Figure 1] 2.2.1 File "curves.txt"

This table stores the growth curve data points (16002 entries, 4 columns). Each entry is the individual or average biomass of a batch of earthworms at a given time in a given experiment, with the standard error of the mean of the biomass, when available. Each growth curve has a unique identifier called "CURVE_ID". All points with the same CURVE_ID belong to the same growth curve.

Be aware that the column "time" is in most cases not the age of animals but the time since the beginning of the experiment.

File "curves_md.csv"

This table (1073 entries, 30 columns) describes the environmental conditions in which each growth curve was produced. This table is linked to the "curves.txt" file through the CURVE_ID field. This field allows the user to retrieve the environmental conditions in which each curve was produced. For each curve the name of the species studied, the types of factors that were tested in the experiment, the level of the factors, the intra and eventually interspecific earthworm density in the container, the room temperature, the soil moisture, the geographic origin of the individuals, the food and the substrate can be obtained. In experiments with fluctuating temperature, the average temperature was reported. There is also a "REF_ID" field that indicates the source of the data. This field is used to join this table to the file "references.csv".

File "references.csv"

This table describes the documents from which the growth curves come from. It is linked to the "curves_md.csv" file through the "REF_ID" field. It contains also the DOI of sources, when available.

This file is essential for tracking the origin of the data and for reproducibility of analyses. Only published data were included in the database. There are 162 entries and 7 columns in this table.

Database access

The database is open and can be accessed in a variety of manners that are presented in details in the user guide provided in supplementary material. A minimal example of the database usage is presented now.

This example assumes that the database (the three files "curves.txt", "curves_md.csv" and "references.csv") are stored in the R working directory.

The first step is to read the data.

[code] growth <-read.table("curves.txt", h = T, na.strings = "na",sep = "\t")

EGrowth_metadata <-read.csv2("curves_md.csv",h = T, na.strings = "na", sep = ",", dec = ".")

[end of code]

Then one way to proceed is to merge the tables based on field "CURVE_ID", which is the growth curve unique identifier:

[code]

EGrowth <-merge(growth, EGrowth_metadata, by = "CURVE_ID")

[end of code]

We can plot all curves of a species of interest.

Example with Drawida willsi:

[code]
if (!require('lattice ')) install.packages('lattice'); library(lattice) with(EGrowth[EGrowth$species =="Drawida willsi",],xyplot(bm~time|CURVE_ID, type = c("p", "smooth"),pch=20, cex =1.5,col="grey20", ylab="Biomass (mg)",xlab="Time (days)",layout=c(4, 2), par.settings=list(strip.background=list(col=c("grey80")))

))

[end of code]

[Figure 2 )

Then you can retrieve the environmental data by typing:

[code] EGrowth_metadata[EGrowth_metadata$species =="Drawida willsi",] [end of code]
More examples and the code of all the analyses presented in this paper are given in the User Guide.

The code of the GUI is provided in the GitHub repository: https://github.com/JeromeMathieuEcology/EGrowth

Coverage of EGrowth

At the present time, the EGrowth database contains 1073 growth curves, made of 16002 measurements of biomass. These data come from 162 publications.

Temporal dynamics of publication

No data were found between 1900 and 1953. The production of data on earthworm body growth started in 1954, with the seminal PhD thesis of Jean Michon (Michon, 1954). From this time onwards, the number of published growth curves increased exponentially until the 90's (Fig. 3a).

During this period, five other theses focused on earthworm body growth and life history traits. After the 90's, the publication rate of growth curves slowed down. The last past ten years showed a dramatic reduction in the production of new data. In particular the number of species covered has not increased since 2011 (Fig. 3b).

[Figure 3]

Species covered

Growth curve data on 51 species are included in EGrowth (Fig. 4). The most documented species were by far Eisenia foetida (n=244), Lumbricus terrestris (n=131), Eisenia Andrei (n=87),

Aporrectodea caliginosa (n=74) and Lumbricus rubellus (n=70) (Fig. 4). The criteria for studying a specific species was rarely reported, except for E. foetida, which is a model species in ecotoxicology (OECD, 1984). In the top five studied species, two are considered as global invasive species (L.

terrestris and L. rubellus). Overall, only one endemic species (Hormogaster elisae, Spain) was studied. There are no data for endangered species such as Megascolides australis, the giant gippsland earthworm from Australia [START_REF] Van Praagh | The biology and conservation of the giant gippsland earthworm Megascolides Australis McCoy, 1878[END_REF], or Driloleirus americanus, the giant Palouse earthworm from USA (Sánchez-de León and Johnson-Maynard, 2009).

[Figure 4]

Locations covered

Growth curves in EGrowth were issued from 86 different sites spread around the world (Fig. 5). Most of the data were produced in Europe and in India. At the moment, no dataset is available from

Russia, China and central Africa. Data from Russia and China probably exist, but were not found because they are not yet available in English. There are much more body growth curves from temperate areas (n=693) than from tropical (n=283) or from Mediterranean climates (n=97).

[Figure 5]

3.4 The general shape of body growth curves in earthworm

The shape of body growth curves from birth to death has been poorly documented so far. The most complete work regarding this topic is the PhD of Michon (1954). He compared the pattern of body growth of 14 species, from birth to death, with extremely frequent measures of body mass. He identified several stages in the life cycle of earthworm based on the shape of the logged body mass growth curve. In particular, he showed the existence of a senescence phase, which has been poorly documented since then. From this work and from more recent ones, we can define a general pattern of earthworm growth shape (Fig. 6a).

After birth (B), juveniles (J) usually follow a lag phase, then a steep, exponential or linear, growth. In long living species, the J phase can show a staircase shape pattern (Lakhani and Satchell, 1970).

When sexual maturity is attained, adults enter the reproduction stage (R) and growth is strongly reduced and sometimes becomes negative. After some time, adults enter a senescence stage (S), during which time body size decreases and sexual organs disappear.

[Figure 6]

The variety of monitoring schemes of earthworm body growth curve

In most of the publications, growth was monitored only during a fraction of lifespan. Three broad monitoring schemes can be identified (Fig. 6b).

The type 1, called Instantaneous Growth Rate (IGR), is the most often used. It is calculated as the difference of (usually log) body mass between two dates (e.g. Eriksen-Hamel et al., 2009). The monitoring generally occurs during the juvenile stage. The exact age at the beginning and at the end of the monitoring vary among studies and is often not reported. This kind of data was not included in EGrowth database because it is not suitable for fitting growth rate models.

The monitoring scheme type 2 is the most common after type 1. It refers to cases were biomass was measured on more than four occasions, the first measurement being during the juvenile stage, but not at birth. Monitoring ends either during the juvenile stage (J monitoring scheme) or during reproduction stage (JR monitoring scheme). Measures were rarely taken until senescence (JS monitoring scheme) or until death (JD).

In the third broad monitoring scheme, monitoring started at cocoon hatching. This monitoring scheme is the most informative because the age of the individuals is known. Unfortunately, this monitoring scheme is also the least frequent, probably because of the fact that obtaining a batch of synchronized hatchlings with the same age is difficult. Some techniques such as storing cocoons at low temperature has been proposed to solve this difficulty (Bouwman, 1998). However, it still requires to breed the individuals in advance, to wait for cocoon production and for hatching. This can be a severe difficulty in species with slow population dynamics. Within this monitoring scheme, a variety of monitoring length also exists. A number of studies only cover the juvenile stage (BJ).

These studies are particularly useful to estimate the age of the individuals in the monitoring scheme type two, in which age at the beginning of the monitoring is not reported. However BJ data are usually not sufficient to estimate the shape of the growth shape without making strong assumption about the shape of the growth curve. In BR monitoring scheme, individuals are monitored until their mature stage, before senescence occurs. In this case, the growth curves are usually monotonic:

there is no decrease in body mass. This is the most frequent sampling design in monitoring scheme type three. A very limited number of studies monitored growth rate from birth to senescence (BS) or from birth to death (BD). These monitoring schemes are however the most informative as they give estimates of life spam and the complete shape of the body growth curve.

Overall, the great majority of studies in EGrowth lasted between 100 and 200 days (Fig. 7a), but some lasted over 900 days (e.g. Mulder et al., 2007). Most sampling frequency fell into one of the three following classes: every week, every two weeks, or every month (Fig. 7b).

[ Figure 7]

Drivers of body growth

Nearly all studies were performed in controlled conditions in the laboratory. Only seven growth curves, all from temperate areas, were obtained in the field (Table 1). In the laboratory, by far the two most frequently documented factors are the type of food provided (368 curves) and the toxic effect of a variety of products (330 curves). These experiments typically come from studies on vermicomposting and from ecotoxicology. The effect of air temperature (140 curves) and density (134 curves) are also well studied. All other factors are much less well studied. Surprisingly, the effect of soil properties on earthworm growth has been poorly studied (48 curves), whereas it would greatly help to understand the distribution of earthworm species in nature. Overall, 172 curves comes from experiments that tested two or more factors.

[Table 1]

In ecotoxicology studies, the most frequent treatments are related to the type of waste used as substrate for growth, manure being the most studied (Table 2). The effect of copper and cadmium are the most documented among metals, while Moxidectin, Glyphosate and Dieldrin are the most studied among pesticides.

[Table 2] 4. Nine recommendations for reporting body growth curves

The absence of a general framework to report data on growth has resulted in a heterogeneity of available information among studies in the EGrowth database. This has occasionally resulted in the discarding of growth curves because of the absence of critical information. It has also resulted in missing values in the database, which limits the possibilities of disentangling the effect of different factors, like temperature and humidity. This issue could easily be avoided in the future if simple guidelines were followed systematically when reporting results. The general good practices on how to report data for their reuse in meta-analyses have been proposed in various articles and are summarized by the FAIR principle: Findable, Accessible, Interoperable and Reusable [START_REF] Gerstner | Will your paper be used in a meta-analysis? Make the reach of your research broader and longer lasting[END_REF][START_REF] Penev | Strategies and guidelines for scholarly publishing of biodiversity data[END_REF][START_REF] White | Nine simple ways to make it easier to (re)use your data[END_REF][START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF]. Here I focus on the specific aspects in reporting growth curves for their easy re-use and integration in databases. I propose a tentative template for reporting such type of results, accompanied by a To Do checklist (Supplementary material).

Reporting the experimental design

It is critical to clarify as much as possible the experimental design. For this, four components need to be clearly explained: 1° the type of treatments that vary between curves, if any (e.g. temperature or food). 2° the treatment levels for each treatment (e.g. for temperature: 5°C, 15°C, 25°C). If there are more than one factor, the combination of levels covered (full factorial, incomplete design) must be specified. 3° the number of independent measures (replicates) per level of treatment. This is usually the number of containers per level of treatment. 4° the number of individuals per replicate: density within containers (e.g.: 3 ind.replicate-1).

If you will allow it; I would like to remind you here of a few points on experimental design. First, individuals in the same container cannot be considered as independent because of block effects.

Using the individuals' data without accounting for this block effect leads to pseudo replication [START_REF] Hurlbert | Pseudoreplication and the Design of Ecological Field Experiments[END_REF]. Indeed, all individuals in the same container are affected similarly by any variation of the container, and hence are not independent. When it is possible to track individuals separately, we can use the raw data and integrate a block effect (i.e. container) in the analysis. However, for earthworms, this is generally not possible without using tags [START_REF] Mathieu | Methods for studying earthworm dispersal[END_REF]. The correct procedure in general is thus to use the averaged biomass by replicate, but this results in a significant loss of statistical power. In order to circumvent this problem, a solution is to distribute individuals in as many as possible separate replicates, rather than grouping them in a limited number of replicates.

This maximizes the power of the study without increasing its cost (the number of individuals does not change). Following these lines, it is more efficient to put only one individual per container rather than several ones. Beyond improving the power of statistical tests, it removes all effects of density dependence such as competition and reproduction, which can interfere with the treatments, and which can vary according to species [START_REF] Uvarov | Inter-and intraspecific interactions in lumbricid earthworms: Their role for earthworm performance and ecosystem functioning[END_REF]. In the end, it gives a better picture of the effect of treatments on growth, all other things being equal. Replicates with only one individual per container thus facilitate the comparison among studies and among species.

What is biomass and how to report it

Reporting biomass is not as trivial as it can seem at first. Indeed biomass can change significantly with body moisture, gut content, and fat content, without having an ecological or evolutionary significance. Hence, it is important that the type of biomass reported is clearly mentioned. The ideal unit for comparing studies is dry weight of gut voided individuals. This removes the issues of body moisture and gut content, which represent a large proportion body weight and which can vary substantially regardless of dry biomass. However, this unit is not practical because it requires gut voiding then killing a fraction of the individuals at each monitoring event.

A good compromise is to report fresh biomass of non-gut voided individuals, and to measure separately, on a batch of individuals of varying size, gut content as a function of fresh biomass (e.g. [START_REF] Bolton | Burrowing, feeding, egestion and energy budgets of Allolobophora rosea (Savigny) (Lumbricidae)[END_REF]Curry and Bolger, 1984), and the relationship between the biomass of fresh gut voided and dry gut voided individuals [START_REF] Saussey | Contribution à l'étude des phénomènes de diapause et de régénération caudale chez Allolobophora icterica : Savigny Oligochète lombricien[END_REF].

Whatever the type of biomass reported, it is crucial to clearly define it, and to use appropriate terminology and units. For instance, in a number of publications the word "growth" is used instead of biomass. Growth is a variation of biomass over a period of time (units can be mg.day.-1), not a biomass at a given time (units: mg).

Reporting age rather than time

Ideally, body growth curves relate body biomass to age. This permits one to estimate growth parameters that depend on age, such the age at growth spurt -the maximum growth rate [START_REF] Parks | A Theory of Feeding and Growth of Animals[END_REF]. These kind of parameters are sometimes the only ones affected by a treatment. For instance, a particular treatment can produce a shift in phenology such as a decrease of age at maturity, and change population dynamics, without impacting maximal body size.

Reporting age is also needed for comparing growth models across studies. Indeed, parameters in growth models are often age dependent. Hence, they can only be correctly estimated if age is given in data, rather than time since the beginning of the experiment. This is particularly important when the study do not start from the birth of the individuals. In such case, if the age at the beginning of the growth curve is not given, it is difficult to translate the time axis into age. This is particularly true for studies based on IGR. Without age and weight, it is not possible to integrate IGR data in growth curve databases.

In conclusion, reporting age offers a lot more possibilities for modelling and for comparison across studies. If age is not known, then reporting the biomass of juveniles at hatching or at the first date of monitoring can be very helpful to rescale the time axis into age.

Reporting phenology

Phenology is an important aspect of body growth patterns. Indeed a number of standard indicators, such as biomass at maturity, are defined in relation to the stage of the individuals. Changes in stages can be indicated by arrows on curves (Klok and de Roos, 1996) or directly as a table giving the proportion of individuals at each stage, at each date (e.g. Elvira et al., 1996). When data are presented in an aggregated way, the most useful information is the age at which 50% of the individuals reached a given stage. Reporting the death or quiescence of animals is also interesting because it allows computing the mortality curve, and to take into account density dependent processes. This can be indicated by a sign such as stars on graphs, or directly in the raw data.

Reporting taxonomy

The correct identification of species is critical for comparing data and building databases. However, taxonomy is still being actively updated, with sometimes major revisions. Most earthworm classifications has been developed on morphological or anatomical traits, and recent molecular studies are revising many taxa (e.g. [START_REF] Csuzdi | Molecular phylogeny and systematics of native North American lumbricid earthworms (Clitellata: Megadrili)[END_REF][START_REF] Domínguez | Underground evolution: New roots for the old tree of lumbricid earthworms[END_REF]. The problem is particularly accurate for growth experiments because identifying living individuals is very difficult, and in most cases not possible until maturity. This might not be problematic to compare treatments within a study, but it becomes a matter of importance when comparing results across studies and across species. Most taxonomical errors probably consist in aggregating different species into a single species, because of cryptic species. This leads to artificially increasing intraspecific variability while decreasing interspecific differences. The problem occurs even in the most studied species such as E. foetida, L. terrestris, and A. caliginosa, whose taxonomical status has been revised several times. As it is almost impossible to check the identification of species in previous studies, caution should be taken in any comparative analysis. The only option to circumvent this difficulty in future studies is to systematically store a few individuals from each study in pure alcohol, and to check identifications with molecular techniques. This is necessary to reach a homogenous and standardized classification across studies.

4.6 Reporting the conditions of the experiments: the need for metadata

Understanding the drivers of IBSV requires that environmental conditions in which data were acquired are reported. This information should cover at minimum the air temperature, the soil pH, soil moisture, expressed in clear units, the number of individuals per container, and the dimensions of the container. Soil humidity in particular is often poorly reported although it is a critical condition for earthworm growth. The type of food, its amount, and the frequency at which it was provided should also be mentioned. These data should ideally be given for each curve and, if possible, for each measurement date. Indeed the conditions such as temperature or the number of individuals sometimes vary during the experiments.

Reporting raw data

An obvious limiting factor for building databases on body growth curves is that data are usually only presented graphically. Extracting data from graphs is tedious, time consuming and needs to be done by a careful and experienced worker. In addition, digitalizing growth curves is sometimes not possible when data overlap, or when artwork resolution is too low. A much more efficient way would be to publish the data also in tables. Ideally, data should be stored in long term repositories such as Zenodo or Figshare, which offer four key features to reuse data in the future: long term storage, easy identification and citation of data trough unique identifiers (DOI), easy finding of datasets through key words in metadata, and easy download of data. Alternately, raw data can be presented in supplementary material associated with the manuscript. In both cases, it is critical to use non-proprietary format such as comma separated text (.csv) or tab separated text (.txt), because they will always be readable from all platforms.

Report individual growth curves

It is much more useful to publish the raw data of each growth curve or each replicate, rather than their average. It clarifies the experimental design. It gives more power to analyses and simplifies them. Indeed, it is much straightforward to include all data in analysis than using aggregated data, which must be weighted by error bars and sample size. In addition, it reduces the digitalization errors, particularly when data overlap in graphs.

Conclusion

The EGrowth database shows that a large amount of data on earthworm body growth is already published. By centralizing these data, EGrowth should help the development of process based models of earthworm ecology. EGrowth also reveals knowledge gaps that challenge the applicability of such models to real situations in nature. In particular, there is a clear lack of data from the field.

This kind of data is critical for assessing the performances of models. In addition, the effect of critical environmental factors for earthworm growth, such as soil properties and soil humidity, have been little studied so far. Lastly, little is known about rare species that are not dominant, particularly regarding large or endemic species. In order to integrate new data, EGrowth will be updated in the future. For this, a framework to report future data on earthworm body growth is proposed.

4. 9

 9 Report error bars and their unitDespite previous reminders about the necessity of reporting error bars in graphs (e.g.[START_REF] Cumming | Error bars in experimental biology[END_REF], they are still absent in a number of publications. When reporting averaged data is the only possibility, it is vital to show the error bars of the mean. It is also critical to clearly mention the type of error bar that was used (e.g. standard error, standard deviation, confidence interval) and the sample size, otherwise errors bars cannot be used.

  Applied Soil Ecology and Megadrilogica were checked manually through table of contents for articles containing data about earthworm growth rate. Articles that were not available

	intensive internet search was carried out through different databases, mainly Web Of Science,
	Scopus, Google Scholar and Researchgate. References cited within the articles were also searched
	for online and in various libraries in France (MNHN and IRD Bondy) and USA (UCSB and Stanford).
	Key words such as earthworm growth rate, body size, and ones related to earthworm ecotoxicology
	-a field which offers a large amount of data-were used to retrieve publications. In addition, all
	issues from the most relevant journals -Pedobiologia, Soil Biology & Biochemistry, Biology and
	Fertility of Soils,
	2. Material and methods
	2.1	Database construction
	Data were searched in articles published in peer reviewed journals and in PhD theses from 1900 to
	2016. Articles were searched in different ways, with the goal to be as exhaustive as possible. An

in a digital format were scanned, and the text was extracted through an OCR process (Optical character recognition). Then figures and tables with relevant data were extracted, digitalized with the software DataThief, and exported into spreadsheets. Point data from figures were reported with their associated error bars when available. Error bars were converted to Standard Error bars.

Acknowledgement

The author would like to thank all the authors who kindly replied to his emails and furnished additional details on their data, and Naoise Nunan for English editing.

List of publications used to build the EGrowth database on earthworm body growth variability

Contact: Jerome.mathieu@upmc.fr a) List of publications from which data were included in the EGrowth database

Checklist for reporting growth curves of Earthworms

Reference : Mathieu J.

Biomass

The type of biomass is clearly mentioned (dry vs fresh biomass).

The gut content status is mentioned (void gut vs non voided gut).

The unit of the biomass is given. "Growth" and "biomass" are used appropriately.

The biomass at hatchling is reported. The biomass at first date of the survey is given numerically (not only on graphs), with the error bar.

Bonus

Gut content was estimated.

Body moisture was estimated.

Age and time

The age of the individuals at the first measurement is clearly given. The meaning of the X axis (age or time since the beginning of the measures) is defined.

The units of the time/age axis are clearly mentioned (e.g. days, weeks).

If IGR (Instantaneous growth rate) only is reported,

Its formula is mentioned.

The absolute biomass are also reported. The age at each measurement is reported.

Full clitellum development is indicated. Death of individuals is indicated.

Taxonomy

A couple of individuals are stored in alcohol for molecular identification.

Conditions of the experiments (metadata)

All these information are reported:

Geographical origin of the specimens. Geographical coordinates in decimal degrees. 4).

Stages covered by the monitoring (B,J, JR,JRS,JRSD and so on, see figure 4).

Data

Raw data and metadata are available either as supplementary material or in a data repository. Data are presented in txt or csv files.

Data are given at the highest resolution possible (either by individual or by replicate) rather than as average per treatment.

Error bars are given if averaged data are presented. The nature of errors bar is clearly stated.


domain: chim info math phys sde shs stat

text:
Loïc Bertrand
email: loic.bertrand@synchrotron-soleil.fr

Sophie David
email: sophie.david@synchrotron-soleil.fr

Serge Cohen

The web directory of IPERION CH instruments and databases aims at providing researchers with information related to resources (instruments, databases and datasets) available within IPERION CH through a package of categories/metadata that help categorising them according to their different dimensions. A database model has been created.

Introduction

This report aims at presenting the approach and the work done related to the IPERION CH web directory, which is described in the Technical Annex (p. 29) as follows: "preparing and maintaining a web directory of IPERION CH instruments/databases, which could also function as an extension of the MERIL portal of European RIs". The creation of the web directory is one of the tasks of the Access Board, which is also described under WP2-Task 2.1 (cf. Technical Annex p. 29). The WP2 deliverable "First edition of the web directory of IPERION CH instruments and databases [M12]" (Technical Annex, p. 72) has to be submitted by April 30 th . The work has been coordinated by CNRS-IPANEMA.

First, we will give an overview about the context to explain more precisely our approach (instruments and databases, links to activities of other WPs, intended users, definitions, aim). Then, we will present the database model, based on an Entity Relationship model ( § 2. and Annexes 3 and 4), and in particular the categories that will be integrated and that are based on:

 The questionnaires collected in the frame of the Data Management Plan (DMP) (called hereafter the "Questionnaire" cf. Annex 1).  Our analysis of existing portals describing or allowing access to shared infrastructures, such as MERIL (portal.meril.eu), Wayforlight (www.wayforlight.eu) and CERIC (www.ceric-eric.eu).  The re3data project (www.re3data.org), which is a directory of research data repositories (currently more than 1.500, consulted on 21.04.2016). This project presents very clearly and well referenced the metadata and values it uses.  Existing lists (they will be introduced in the sections where they are relevant).

Context

A web directory is a catalogue of web sites. As it will be clarified through the upcoming pages, our efforts have shown the interest to extend the work, in the frame of IPERION CH, to datasets, and more generally, in the future, to all types of resources of IPERION CH.

Instruments

Concerning the instruments, certain of them are described on the respective web sites of the responsible institution. E.g. the BNC web site: www.bnc.hu, or more precisely: www.bnc.hu/?q=node/7. Those instruments can also be described within a specific project as, for example, on the Wayforlight web site that is linked to the European CALIPSO project: www.wayforlight.eu. Another example is the CERIC ERIC web page that lists all instruments available within the consortium: www.cericeric.eu/index.php?n=Users.Facilities.

It is important to note that, in the cases of Wayforlight or CERIC, the described instruments are mostly of the same kind:

 On the Wayforlight web site, information about the beamlines of synchrotrons and European Electrons Lasers participating in CALIPSO are available.  On the CERIC ERIC web site, the first set of categories is related to their transnational access (TNA) procedure: the procedure is based on the opposition between "Facilities providing access only to multiple instrument proposals" and "Facilities providing access to multiple and also to single instrument proposals".

The second categorisation is made by geographical information (according to the beamline locations). The last list consists of the facilities' beamlines.

An interesting fact is that, in both cases, categorisations/descriptions are firstly devoted to beamlines and not to facilities.

Analysing those web sites is useful to easily gather certain types of information; anyhow, it is also important to be able to establish a link between the instruments and their associated technique(s). Furthermore, it is crucial to keep in mind that many other instruments than synchrotrons and lasers are used in the field of Heritage Science. Therefore, the web directory has to integrate a diversity of instruments that does not yet exist on any other web site.

Databases

As for the databases, the results of the Questionnaire show that in fact very few databases exist. Focusing only on the databases would have provided too little content.

That is why we have preferred to take into account information about the datasets and databases, that we received from the data providers. Once the quantitative question solved (we have information about 88 datasets and databases), another series of problems comes up:  few datasets or databases are available online;

 not all data are digitised;  for a large number of datasets or databases, several difficulties linked to the format, storage, availability, access, etc. have still to be solved. As a consequence, the web directory cannot only be a catalogue of web links to databases or datasets. On another hand, we can transform the information collected through the Questionnaire into categories/metadata that will afterwards be organized in a database making it possible to classify, find and identify the databases and datasets of IPERION CH.

Link to the activities of WP12 and the future E-RIHS infrastructure

WP12 has to produce a "Database on CH conservation and research institutions and stakeholders (M18)" (cf. Technical Annex of IPERION CH p. 70). a) This deliverable is expected to be implemented by autumn 2016. Hence, in common agreement with WP12, the extended implementation of the web directory will be done in the context of WP12. Consequently, this step focuses on the conceptual design of the web directory. b) A clear link between data of the web directory and those of the database provided by WP12 has to be established from now on.

Link to the activities of WP8

WP8 is expected to realise activities, such as "Organisation/presentation/sharing of analytical scientific data (raw spectra, chemical maps/images etc.)" from case studies (cf. Technical Annex of IPERION CH p. 55). The web directory concept that we are presenting could be a first tentative in this frame, and results from WP8 activities could later on be added.

The intended users

At this stage, the main target group for the use of the web directory are researchers. Those might be researchers participating in the IPERION CH project as well as external researchers.

The type of information is useful within the IPERION CH project, and also outside. It will help researchers to:

 Get a better overview about the partners' resources, to get inspired by best practices, etc.  Provide more thorough information to the communities about available resources and to help researchers accessing those, especially through the TNA procedure.

Once all information about CH conservation and research institutions and stakeholders collected, the second target group using the database could be research institutions, funding agencies and cultural institutions.

Definitions and distinctions

The following terms are important for the overall understanding of this report; therefore, we have provided specific explanations.

Database

We use this term in its strictest sense. A database is a collection of items: raw and analysed data, publications, reports, etc. They are organised through a specific model (hierarchical model, relationship model).

Dataset

A dataset is a collection of raw or analysed data.

Data provider

The data provider is an instrument (which includes the team that helps to operate instrument) and/or a laboratory group and/or museum department, and/or a cultural institution group, etc. that produces data. These data can be put together into one/several datasets or databases. The data provider is also the responsible entity to give access to a dataset/database. In certain cases, access to the datasets or databases can be subject to specific constraints and restrictions (intellectual property, embargo, etc.).

Access provider

Access providers are infrastructures that offer a wide range of services that provide access to one/several instruments and/or integrate one/several data providers. In IPERION CH, an access provider may be a cultural institution, a museum, a laboratory, a technical platform, a facility1 , etc. The term of access provider identifies precisely the role of IPERION CH partners.

Hosting organisation

The access providers are hosted by hosting organisations, which host the infrastructure or its coordination centre. Hosting organisation is also used in MERIL. Keeping this entity will allow to link our work with the future database of WP12.

It is crucial to make the below listed distinctions. 1/ Distinction between access providers and data providers:

1. The data providers produce data, but they do not systematically provide access to them within the community. 2. Certain access providers do not offer instruments (museums, institutions), but give access to their data (through their data providers).

2/ Access providers and the IPERION CH partners As the IPERION CH partners are diverse (different kinds of instruments, different kinds of institutions, etc.), it is important to have a "body" which allows us to deal with them in a unified way. The notion of access provider plays this role.

3/ People and competences

When we speak about instruments, we first consider an object that has been designed to accomplish a certain task, operation, etc. We barely consider the teams or the groups that make this instrument work. Once again, it is important to associate people (and their competences) to be able to deal with the diversity of IPERION CH partners. So, data providers are first considered as groups of people, linked to instruments (beamlines, lasers, neutron instruments) or institutions (museums, cultural institutions, etc.).

The IPERION CH project counts 19 access providers that are listed in Annex 2.

Rephrasing the extension of the web directory

According to this context, it is important to rephrase the extension of the web directory. a) A database of instruments, databases and datasets made available by data providers of the IPERION CH project has to be conceived.

b) The problems outlined through the Questionnaire (digitisation, storage, access format, etc.) lead to the need of creating a package of metadata that will help to categorise the datasets/databases. c) Once created, this database has to be compatible with the database that will be produced within WP12. That means that a clear connection between instruments and datasets/databases and the CH conservation and research institutions and stakeholders has to be established.

Taking all that into account, we have chosen a bottom-up approach starting from the resources provided by the IPERION CH partners. Therefore, a wide range of data received through the Questionnaire will be reused.

The main objective of the web directory can be summarised as follows:

Provide researchers with information related to resources (instruments, databases and datasets) available within IPERION CH through a package of categories/metadata that help categorising them according to their different dimensions.

Model

We have used the Entity Relationship model to model our database 2 . The result is illustrated through a scheme in Annex 3, § 3.2. The entities and attribute values are specified in Annex 4.

Given the purpose of our work, and for the sake of simplification, as the descriptions of databases and datasets will be the same (in terms of metadata), we will identify a unique entity dataset/database.

Three main entities

The following section gives an overview about the three main entities we have chosen, explaining briefly their relation with the other entities 3 .

1) A dataset/database is produced / made available by a data provider; it can be related to one or several specific materials; data grouped into one set can consist of one or several types of data of one or several formats; these data can be produced with one or several techniques via one or several instruments; the data is of interest for one or several communities, they are located, and they are accessible according to several conditions. One or several publications can be associated to the data.

2) The data provider has an operational status. He produces data of one or several types of one or several formats. He is responsible to provide access to the community to one or several datasets/databases. He depends upon an access provider. He has expertise in one or several fields and can offer services. He might have national or international collaborations. One or several publications can be associated to the data provider.

3) Being a facility, the access provider is of a specific type (virtual, distributed, singlesited). Its activities are related to one or several scientific fields. Its users might come from the private and/or scientific sector. An access provider is hosted by a hosting organisation.

2 We express our gratitude to Luis Arean (IPANEMA) who contributed with fruitful discussions.

-An entity [in the Entity Relationship model] is a concrete or abstract thing from the perceived reality, for which we want to keep information. An entity exists autonomously. Each entity has specific properties, which are called attributes.

-A relation is a connection between two or more entity occurrences, for which we want to keep information.

-An attribute is a characteristic or a quality of an entity or of a relation. It can take one or several values.

-A value is a symbol, which represents an elementary fact.

In the scheme (Annex 3, § 3.2.), we have not indicated the primary keys. And only essential attributes have been indicated. These 4 definitions have been adapted from M. Boughanem, http://www.httr.upstlse.fr/pedagogie/cours/bd/ea.pdf.

3 These three main entities are marked in blue in Annex 3, § 3.2.

The scheme below illustrates these levels of organisation:

To identify our entities, attributes and their values, we were departing from the categories or metadata proposed by the MERIL portal, the Wayforlight web site, the project re3data, several lists established in the frame of the LABS-TECH and EU-ARTECH projects, several lists which have been established within CNRS-IPANEMA, descriptors of two journals, and lastly the responses to the Questionnaire. Each entity will be described in the following sections.

The entities: Access Provider and Data Provider

Categories in MERIL

The MERIL portal is an inventory of openly accessible Research infrastructures (RIs) in Europe from diverse scientific domains such as archives, statistical offices, biobanks, satellites and particle accelerators. According to MERIL, a Research infrastructure can be defined as follows:

"A European Research Infrastructure is a facility or (virtual) platform that provides the scientific community with resources and services to conduct research in their respective fields. These research infrastructures can be single-sited or distributed or an einfrastructure, and can be part of a national or international network of facilities, or of interconnected scientific instrument networks."4

This description is mainly adapted to IPERION CH's access providers. But our objective being to indicate the production and access level, as well as to provide resources instead of general infrastructure descriptions:  The MERIL categories we have chosen (actually, most of them) will be distributed between the three entities: Hosting Organisation5 , Access Provider and Data

Provider. One category is linked to the three entities ("contact person"). Others can be linked to several entities (e.g. "scientific domain" is appropriate for hosting organisations and access providers). Finally, certain categories are rather linked to the hosting organisation characterisation (e.g. "funding source"), the access provider characterisation (e.g. its type), whilst others relate to the Data Provider entity (e.g. "(operational) status").  It will be necessary to add other entities/attributes helping to describe the techniques, formats, policies, access, type of materials, etc. "Funding source", as mentioned above, will be linked to the Hosting Organisation entity.

Following elements from the MERIL portal have not been integrated: A/ "Keywords" -"RI Categories" -"Grand societal challenges":

 Collecting at this stage (free) keywords is not a priority.  The category does not target the field of Heritage Science (cf. the 71 RI categories in MERIL, cf. portal.meril.eu).  The proposed values are not relevant to the field (cf. values for the category "Grand societal challenges"). B/ Equipment As mentioned above, "equipment" covers parts of what we call Data Provider.

Other entities and attributes

It has seemed appropriate to us to add other entities or attributes that are more specifically linked to the IPERION CH project and/or the future infrastructure E-RIHS.

A/ The Access Provider entity

Concerning the Access Provider entity, it is important to specify to which platform(s) it belongs. The attribute PlatformName will have the following values: ARCHLAB, FIXLAB, MOLAB, to which could in the frame of E-RIHS be added DIGILAB and EXPERTLAB.

B/ The Data Provider entity

As mentioned above, we want to highlight the people that operate the instruments and/or that generally produce data, and their competences.

That is why: a/ The DataProviderType attribute mentions groups of people, possibly related to an instrument or an institutional entity (beamline group, laboratory group, museum group, etc.). b/ It is relevant to relate the Data Provider entity to different entities representing the Data Provider competences:

 The Expertise entity: a Data Provider always has an expertise but not always it is offered as a service; this information should be accessible.  The Publication entity (which is also related to the Dataset/Database entity). This is another way of representing the expertise of the Data Provider.

As it produces data, the Data Provider entity must be related to the Data Type entity, which indicates the type of data that the Data Provider is supposed to produce. The Data Type entity will also be related to the Dataset/Database entity. And the data formats will be linked to the data types.

The Dataset/Database entity

Following entities are linked to the Dataset/Database entity:

 Meta-material  Meta-technique  Data Type => Format  Policy  Access  Location  Target Community  Publication
In the next parts, our choices in relation to particular entities with specificities will be explained.

One of the important elements to take into account is the number of items among the existing controlled vocabulary. Indeed, too many items can harm the project's operational capability, "losing" the Access Provider/Data Provider or users.

The attributes of the Dataset/Database entity will be presented in Annex 4, § 4.4.

The Meta-material entity

For this entity, we have analysed two sources:

-the CAMEO database; -the list of materials which has been set up in the framework of the LABS-TECH and EU-ARTECH projects, which has been refined within CNRS-IPANEMA.

CAMEO

The CAMEO project -The Conservation and Art Materials Encyclopedia Online (cameo.mfa.org/wiki/Main_Page), led by the Museum of Fine Arts Boston, gives access to several databases and collections. One of these deals with materials, and we will describe it briefly. At stake of this collaboration was, amongst others, the development of the international perimeter of CAMEO, with the translations of material names. As IPERION CH is in line with both projects, and due to the fact that linguistic aspects are relevant dimensions in the European Union, we considered it to be interesting to further explore the content of the database and to analyse to which extent this information would be (easily) reusable.

The database of materials gathers various information on ancient and contemporary materials, which can be found in artistic or archaeological objects, in objects relevant for anthropological analysis, or in architectural complexes.

It gives access to more than 10.000 terms (exactly 10.373, consultation on 07.04.2016), for which we have a description/definition, terminological information (synonym, equivalent), translations of each term (in languages including French, Spanish, Italian, Portuguese, German, Dutch, Greek, Polish, Hungarian), and a list of bibliographical references. In some articles, additional information can be provided (for example, the article "Lead": Other properties / Hazards and Safety / Additional information; the article "Acajou": Additional images, etc.).

It is worth repeating that we have first to categorise 88 datasets/databases (even if this number will increase in the future). In this context, the number of terms in CAMEO is a major obstacle for its reuse in the framework of the web directory: The dimension of precise and granular distinctions is too small.

That is why we have chosen to retain a list of meta-materials instead of materials. Hence, we analysed the list of materials provided within the project LABS-TECH.

Projects LABS-TECH and EU-ARTECH -Update

The projects LABS-TECH and EU-ARTECH (the list has been established by Jean-Louis Boutaine) have provided a list of 19 meta-materials names, plus the value Other.

The number of names has an order of magnitude so that data providers and users can look at or use them easily.

After 2009 (at the end of the project EU-ARTECH), and until 2014, Loïc Bertrand and Marie-Angélique Languille have refined the list. In 2011, it has been published in [START_REF] Bertrand | Cultural heritage and archaeology materials studied by synchrotron spectroscopy and imaging[END_REF] From 2014 until now, it has been continuously updated, integrating systematic analysis of the publications. This work allows to refine some names, and also to add new names, which are of particular interest for E-RIHS, that includes palaeontology. Ultimately, we have decided to keep this last list6 .

The Meta-technique entity

Wayforlight

Regarding the entity Meta-technique, we have first inspected the Wayforlight web site (www.wayforlight.eu), which has been set up in the framework of the CALIPSO project.

The techniques are gathered in a list of meta-techniques, what make the use and access easier. In the end, we have 8 names of meta-techniques, covering 45 technique names. Yet, we cannot reach a consensus with this classification7 .

In any case, it lacks other techniques, which are displayed by the access providers of IPERION CH, and which are not related to synchrotrons or lasers.

However, we will get back to this in the next section, as a query by technique seems to be very interesting.

Projects LABS-TECH and EU-ARTECH

The second list that we have analysed has been set up in the framework of the projects LABS-TECH and EU-ARTECH (the list has been established by Jean-Louis Boutaine). It gathers 66 terms8 .

This list covers the whole field of Heritage Science. Nevertheless, it has not been updated since 2009: a certain amount of techniques, such as OCT, NMR, STXM, etc., are lacking. Besides, in the framework of our project, the number of items is too high and in consequence not easy to use.

A categorisation of the techniques has also been suggested, but once again, it is difficult to reach a consensus. The generic terms do not refer to the same type of properties.

For instance, the following different elements are on the same level: the aim of the technique: "Dating", the interaction mode with the material: "Ion beam analysis", the property of non-destructiveness, what we analyse: "Surface techniques analysis", etc.

We have also looked at other lists and classification (classification proposed by [START_REF] Régert | Physico-chimie des matériaux du patrimoine culturel -Partie 1[END_REF]; descriptors used by the Journal Analytical Chemistry or by the Journal of Archaeological Science). Anyhow, due to a lack of time, we are not able to suggest a finalised categorisation of these terms. We have started to figure out ways, which could be fruitful: in terms of "type of information associated to the materials", "approach with/without contact", "invasive/non-invasive approach", etc. This work will be continued, in order to be finalised by the phase of implementation of the web directory.

In the scheme of the database (cf. Annex 3, § 3.2.), we have kept the term Metatechnique, because it is our objective. However, in the Annex 4, § 4.4.3., we present a list of techniques (and not meta-techniques), i.e. the list established by Jean-Louis Boutaine, though it is incomplete.

The entities: Data Type, Format, Policy, Access, Location, Target Community and Publication

All following attribute values of the entities stated below have been obtained from the Questionnaire, possibly merged with other lists, and from the re3data project; some of them have been rephrased in order to have a more generic and operational character.

A/ The Data Type entity More than 30 names have been collected from the Questionnaire's analysis. We have chosen more generic terms, which cover all cases, and which are used in the Digital Humanities (text, sound, image, etc.), or suggested in the re3data project (structured graphics). The value Other allows to cover unexpected cases 9 .

B/ The Format entity

The potential values of the attribute FormatName come from the Questionnaire and analyses of Marie Puren. The values we kept are indicated by type of data 10 .

C/ The Policy entity

The potential values of the attribute Dataset/DatabasePolicyType have been adapted from the re3data categories. The value Other allows to cover unexpected cases 11 .

D/ The Access entity

The potential values of the attributes Dataset/DatabaseAccessType and DataAccessRestriction come from the re3data project. The value Other allows to cover unexpected cases 12 .

E/ The Location entity

The Location entity refers to the "place" where we can access data. The potential values of the attribute LocationName come from the Questionnaire 13 .

F/ The Target Community entity

The potential values of the attribute TargetCommunityName result from analyses of Marie Puren 14 , which have been merged with a list established by Loïc [START_REF] Bertrand | List of domains, Working document[END_REF].

The value Other allows to cover unexpected cases.

G/ The Publication entity

We have added the Publication entity, an issue which has not be addressed within the Questionnaire. The potential values of the attribute PublicationReference are bibliographical references 15 .

Browsing in the database

Keeping in mind the aim of this project, namely having access to the resources which are made available by IPERION CH partners, and considering our scientific field, it seems totally appropriate to enter in the database by the name of a meta-material or a metatechnique (when the list will be available).

The result of this first query would be a list of datasets/databases (names of dataset or database), corresponding to the proposed research criteria: all datasets and databases that deal with Textile for instance. The data provider of each dataset or database could be associated at this stage already.

A browsing system using filters would allow limiting the search, through entities such as Access, Data Type, Format.

The dataset or database could be represented in the form of a sheet with different tabs, which would present the associated information. This sheet model could also be used to organise the presentation of data providers and access providers.

This browsing system is briefly described. It is broadly based on the Wayforlight web site, which displays very clearly and effectively lots of information. In order to have more concrete ideas, see Annex 5.

Conclusion and next steps

On the basis of the definitions and distinctions we established basically about dataset, database, data provider, access provider, we have indicated the aim of our project: providing researchers with information related to resources (instruments, databases and datasets) available within IPERION CH through a package of categories/metadata that help categorising them according to their different dimensions.

To do so, we have analysed different portals/web sites, the documentation of the re3data project, several lists (established within the LABS-TECH project, the EU-ARTECH project, the CAMEO project, CNRS-IPANEMA; as well as descriptors of journals), and we have reused the results from the Questionnaire. The result is a database model, based on an Entity Relationship model. This model will be implemented in a few months.

We will use the upcoming months to:  Create a list with meta-techniques departing from the list of techniques we already have at our disposal.  Adopt the attribute values amongst all IPERION CH partners.  Reflect upon possible ways to integrate other resources such as project proposals submitted by scientists applying to use the facilities in the frame of TNA.

Scheme

Note: when we connect the database above to the database on "CH conservation and research institutions and stakeholders" (WP12), the cardinalities of some of the relations between the entities might be reviewed, as well as the relations between some of the entities.

Annex 4 -Description of entities and attributes

Conventions

In line with the notation system proposed by the re3data project, we adopt following conventions for the attributes:

  4.2. The Access Provider entity .....................................................................23 4.2.1. Attributes .......................................................................................23 4.2.2. The Hosting Organisation entity ........................................................24 4.2.3. The Funding Source entity................................................................24 4.2.4. The Type of Use entity .....................................................................24 4.2.5. The Scientific Domain entity .............................................................24 4.2.6. The Type of Access Provider entity ....................................................25 4.3. The Data Provider entity ........................................................................25 4.3.1. Attributes .......................................................................................25 4.3.2. The Research Institution entity .........................................................25 4.3.3. The Status entity ............................................................................26 4.3.4. The Expertise entity ........................................................................26 4.3.5. The Publication entity ......................................................................26 4.3.6. The Research Service entity .............................................................26 4.3.7. The Data Type entity .......................................................................26 4.3.8. The Format entity ...........................................................................27 4.4. The Dataset/Database entity ..................................................................27 4.4.1. Attributes .......................................................................................27 4.4.2. The Meta-material entity ..................................................................27 4.4.3. The (Meta-)technique entity .............................................................28 4.4.4. The Target Community entity ...........................................................30 4.4.5. The Policy entity .............................................................................30 4.4.6. The Access entity ............................................................................30 4.4.7. The Location entity..........................................................................31 4.4.8. The Publication entity ......................................................................31 Annex 5 -The Wayforlight web site .................................................................32

  First, one has to keep in mind that CAMEO has had closed links with some European projects in Heritage Science: in 2006, the Museum of Fine Arts in Boston developed a collaboration with EU-ARTECH (Access, Research and Technology for the conservation of European Cultural Heritage), a collaboration which had been renewed in 2010, when the project CHARISMA (Cultural Heritage Advanced Research Infrastructures) was launched.

  list of beamlines, gathered under each facility. Beamlines and facilities are linked to more detailed information. 2) Further browsing and filtering, on the left: a) Number of beamlines providing XPS and other techniques b) Access to other meta-techniques (and techniques) 3) Further browsing, in the centre: c) Access to the description of a beamline d) Access to the description of a facility

Entity: Data Provider Name of the Attribute Name in MERIL In relation with (entities)

  The list below shows the categories we have chosen from the MERIL portal (www.esf.org/fileadmin/Public_documents/MERIL/MERIL_Portal_Principles_v.04.03.14 .pdf). They are separated according to whether they will constitute entities or attributes in the web directory database (cf. Annex 3, § 3.2. and Annex 4):

	Entity: Access Provider
	Name	Name	In relation	Name
	of the Attribute	in MERIL	with (entities)	in MERIL
	AccessProviderName	Full Name	Type of Access Provider	Type of RI
	AccessProviderAcronym	Acronym	Scientific Domain	Scientific Domain
	AccessProviderContactPerson	Contact person	Type of Use		Type of Use
	AccessProviderLocation	Location	Hosting Organisation	Hosting Organisation
	AccessProviderCountry	Location
	AcessProviderWebsite	Website
	AccessProviderDescription	Description
	AccessProviderPicture	Picture
	AccessProviderNationalUsers	National Users
	AccessProviderEuropeanUsers	European Users
	AccessProviderExtra-	Extra-European
	EuropeanUsers	Users
					Name
					in MERIL
	DataProviderName	Full Name	Scientific	Scientific domain
			domain
	DataProviderAcronym	Acronym	Research	-Networks and International RI
			Institution	Memberships
				-Cluster
	DataProviderContactPerson	Contact person	Status	Status
	DataProviderWebsite	Website
	DataProviderDescription	Description
	DataProviderPicture	Picture
	DataProviderNationalUsers	National Users
	DataProviderEuropeanUsers European Users
	DataProviderExtra-	Extra-European
	EuropeanUsers	Users

Annex 2 -List of the Access Providers of IPERION CH

	1 BM	The British Museum	UK	ARCHLAB
	2 BNC-WIGNER	Magyar Tudomanyos Akadémia Wigner Fizikai Kutatóközpont	HU	FIXLAB
	3 C2RMF	Centre de recherche et de restauration des musées de France	FR	ARCHLAB
	4 C2RMF	Centre de recherche et de restauration des musées de France	FR	FIXLAB
	5 FORTH	Foundation for Research and Technology Hellas	GR	MOLAB
	6 CNR	Consiglio Nazionale delle Ricerche	IT	MOLAB
	7 IPCE	Instituto del Patrimonio Cultural de España	ES	ARCHLAB
	8 KIK-IRPA	Koninklijk Instituut voor het Kunstpatrimonium	BE	ARCHLAB
	9 LRMH	Laboratoire de recherche des monuments historiques	FR	MOLAB
	10 MTA Atomki	Magyar Tudomanyos Akadémia Atommagkutató Intézet	HU	FIXLAB
	11 NCU	Uniwersytet Mikołaja Kopernika Toruń	PL	MOLAB
	12 NG	The National Gallery	UK	ARCHLAB
	13 OPD	Opificio delle Pietre Dure	IT	ARCHLAB
	14 PRADO	Museo Nacional del Prado	ES	ARCHLAB
	15 RCE	Ministerie van Onderwijs, Cultuur en Wetenschap -Rijksdienst voor het Cultureel Erfgoed	NL	ARCHLAB
	16 RWTH	Rheinisch-Westphälische Technische Hochschule Aachen	DE	MOLAB
	17 SMK-CATS	Statens Museum for Kunst -Centre for Art -Technological Studies and Conservation	DK	ARCHLAB
	18 SOLEIL	Synchrotron SOLEIL	FR	FIXLAB
	19 SPK	Stiftung Preußischer Kulturbesitz-Staatliche Museen zu Berlin-Rathgen Forschungslabor	DE	ARCHLAB

. The Access Provider entity 4.2.1. Attributes Name of the Attribute

	4.2
	1/ Notation of minimum and maximum instance number of the attribute
	0-1	no instance or one instance maximum
	0-n	no instance or multiple instances
	1	one single instance
	1-n	at least one instance or multiple instances
	2/ Controlled vocabularies
	1*	if an attribute value is linked to a controlled vocabulary from the MERIL
		portal
	1	if an attribute value is linked to a controlled vocabulary
	3/ Other constraints
	4.2Instance	Type	Controlled	Other
					vocabulary	constraints
	AccessProviderName	1	String
	AccessProviderAcronym	0-1	String
	AccessProviderContactPerson	1	Contact
	AccessProviderLocation	1	String
	AccessProviderCountry	1	Enum	1
	AccessProviderWebsite	1	URL
	AccessProviderDescription	1	Free text	max. 200 words
	AccessProviderPicture	0-n	Picture	max. 3
	PlatformName		1-n	Enum	1
	AccessProviderNationalUsers	0-1	Integer
	AccessProviderEuropeanUsers	0-1	Integer
	AccessProviderExtraEuropeanUsers	0-1	Integer
	Controlled vocabulary for AccessProviderCountry
	See ISO 3166-1 alpha-2
	Controlled vocabulary for PlatformName
	ARCHLAB
	DIGILAB
	EXPERTLAB
	FIXLAB
	MOLAB

.2. The Hosting Organisation entity Name of the Attribute Instance Type Controlled vocabulary

						Other
						constraints
	HostingOrganisationName		1	String
	HostingOrganisationAcronym		1	String
	HostingOrganisationContactPerson	1	Contact
	HostingOrganisationLocation		1	String
	HostingOrganisationCountry		1	Enum	1
	HostingOrganisationWebsite		1	URL
	HostingOrganisationDescription		1	Free text		max 200 words
	HostingOrganisationPicture		0-n	Picture		max. 3
	Controlled vocabulary for HostingOrganisationCountry
	See ISO 3166-1 alpha-2
	4.2.3. The Funding Source entity
	Name	Instance	Type	Controlled	Other
	of the Attribute				vocabulary	constraints
	FundingSourceName	1-n		String
	4.2.4.

The Type of Use entity Name of the Attribute

		Instance	Type	Controlled	Other
				vocabulary	constraints
	TypeOfUseName	1-n	Enum	1*
	Controlled vocabulary for TypeOfUseName
	Research/Science
	Private/Industrial
	4.2

.5. The Scientific Domain entity

  Also related to the Hosting Organisation entity and to the Data Provider entity.

	4.3
	Name	Instance	Type	Controlled	Other
	of the Attribute			vocabulary	constraints
	ScientificDomainName	1-n	Enum	1*
	Controlled vocabulary for ScientificDomainName
	Information Science and Technology
	Biological and Medical Sciences
	Earth and Environmental Sciences
	Physics, Astronomy, Astrophysics and Mathematics
	Chemistry and Material Sciences
	Engineering and Energy
	Social Sciences
	Humanities and Arts

.3. The Status entity Name of the Attribute Instance Type Controlled vocabulary

	Structured graphics
	Text
	Other					Other constraints
	StatusName		1	Enum	1*
	StartDate 4.3.8. The Format entity	1	Date
	Controlled vocabulary for StatusName Name Instance	Type	Controlled	Other
	of the Attribute Operational Being upgraded FormatName	1-n	Enum	vocabulary 1	constraints
	Controlled vocabulary for DataTypeName
	4.3.4. The Expertise entity Data Type FormatName : values
	Database		Oracle format -SQL -XML -Other
	Image	Name	Instance BMP -ENVI -IRS -JPEG -PNG -RAW -TIFF -WMF -Other Type Controlled Other
	of the Attribute Raw Data	vocabulary D -DAD -JCAMP-DX -LIS -NetCDF -NEXUS -NGS -ProcSpec -constraints
	ExpertiseDescription	1 SEQ -SP -SPC -WFM -WXD -ZVI -Other Free text	max. 200 words
	Sound		OPUS -SPX -Other
	Structured graphics IPJ -SPA -Other
	4.3.5. The Publication entity Text ASCII/TXT -CSV -DOC -DOCX -JAVA -MS -RTF -RTX -Other
	Other		Other
	Also related to the Dataset/Database entity.
	Name 4.4. The Dataset/Database entity Instance	Type	Controlled	Other
	of the Attribute				vocabulary	constraints
	PublicationReference	0-n	Bibliographical Reference	max. 5
	4.4.1. Attributes
	4.3.6. The Research Service entity Name Instance	Type	Controlled	Other
	of the Attribute			vocabulary	constraints
	Name Dataset/DatabaseName	Instance 1	Type String	Controlled	Other
	of the Attribute Dataset/DatabaseResponsible	1	Name	vocabulary	constraints
	ResearchServiceName Dataset/DatabaseContactPerson	0-n 1	Enum Contact	1*
	Dataset/DatabaseDescription	1	Free text	max. 200
	Controlled vocabulary for ResearchServiceName	words
	Dataset/DatabaseURL		0-1	URL
	Access to a technology Dataset/DatabaseIdentifier	0-1	Identifier
	StartDate Access to a laboratory Dataset/DatabaseSize User support Dataset/DatabaseSizeLastUpdate Training CitationName	1 1 0-1 1	Date Integer Date Bibliographical
					Reference
	LicenceName		0-1	String
	4.3.7. The Data Type entity LicenceURL	0-1	URL
	Name	Instance	Type	Controlled	Other
	of the Attribute				vocabulary	constraints
	DataTypeName	1-n	Enum	1
	Controlled vocabulary for DataTypeName
	Database
	Image
	Raw data
	Sample
	Sound

Onwards, access providers and facilities are used as synonyms.

http://portal.meril.eu/converis-esf/static/about#More. Consulted 07.04.2016.

Henceforth we use capital letters when we make reference to the entities of the database.

Cf. Annex 4, § 4.4.2.

Besides, the proposed classification is certainly not always coherent, as we find IR Microscopy at the same level as Medical Application.

Cf. Annex 4, § 4.4.3.

Annexes Annex 1 -The Questionnaire

The Data Management Plan (DMP) of IPERION CH (DMP, [START_REF] Romary | Data Management Plan[END_REF] was one of the project's first deliverables provided to the European Commission. It has been submitted on December 30 th 2015.

We sent two questionnaires to all partners of IPERION CH to conceive the DMP:

 "One survey about general data management practices in each facility (12 questions) about: number of data sets, reference contact person, data property and access, cost estimation for data management, volume and lifetime of the data.  A more detailed survey for each homogeneous data set within each facility (30 questions) about: data set description, standards used, metadata information, access, sharing, archiving, security and protection, references" (DMP, p. 8) Additional results have been received throughout the beginning of 2016, leading to the fact that we dispose today of all the needed information about the Data Providers and 88 datasets/databases. The Questionnaire's results were analysed by Laurent Romary and Marie Puren. This information is very relevant for the categorisation of instruments, databases and datasets accessible in the frame of IPERION CH.

Cardinalities

 The cardinalities are specified at the extremities of each relation.

 For the sake of clarity, we have kept those relations where one of the cardinalities is either (0,1) or (1,1) if the minimum of the smallest pair and the maximum of the other pair is not (0,1) or (1,1).

Types of the attributes

 String, Free text, Enum(eration), Integer, Picture (for images), URL, Address, Name, Contact, Bibliographical Reference, Date, Identifier.

Colours

 Our three main entities are marked in blue (cf. above).

 Yellow entities refer to categories that exist also in the MERIL portal. Some of the MERIL entities have been transformed into attribute and in consequence not marked in yellow.  Green entities are those through which a query in the data base will be made.

The scheme has been created with the software ArgoUML (v0.34).

The Type of Access Provider entity

Result

The beamline is described through a sheet (possibly with tabs).

Result

Information about the facility (access provider).