Sudipta Goswami

Dipten Bhattacharya

Gail N Iles

Barnali Ghosh

Anastasiia A Prytuliak

Benoit Malard

Gopes C Das

B Ouladdiaf

T Chatterji

Anomaly in structural noncentrosymmetry around T N in bulk and nanoscale BiFeO 3

Keywords: numbers: 75, 70, Cn, 75, 75, -c

Using high resolution powder neutron diffraction data, we show that there is a distinct anomaly in the structural noncentrosymmetry around the magnetic transition point T N for bulk and nanoscale BiFeO 3 . It appears that the structural noncentrosymmetry -which gives rise to the ferroelectric polarization -is suppressed by ∼1% in bulk sample and by ∼12% in the nanoscale sample at the onset of magnetic order. This observation shows that the multiferroic coupling improves in nanoscale sample which is expected to brighten the application prospect of nanoscale BiFeO3 in nanospintronics based sensor devices.

In recent times, clear evidence of strong multiferroic coupling at room temperature has been observed in a single crystal of BiFeO 3 in a seminal work. 1 However, whether the ferroelectric order and multiferroic coupling survive in nanoscale system or not has not been clearly determined. The results available in the literature are controversial. While direct determination of ferroelectric domain structure and its switching by piezoforce microscopy (PFM) offers a strong evidence of presence of ferroelectric order in a thin film of just ∼2 nm thickness, 2 results of synchrotron x-ray diffraction, on the other hand, show melting of Bi-sublattice even at a size limit of ∼18 nm. 3 Likewise, while powder neutron diffraction under 5T magnetic field at room temperature yields an evidence of suppression of polarization by ∼7%, 4 x-ray diffraction on a sample of comparable particle size does not show any clear signature of multiferroic coupling. 5 Therefore, a careful experiment appears to be essential in establishing the survival of ferroelectric order and multiferroic coupling in nanoscale BiFeO 3 . In this paper, we show, with the help of systematic laboratory x-ray, synchrotron x-ray, and powder neutron diffraction data, that indeed multiferroic coupling survives and even improves in the nanoscale BiFeO 3 . We report our observation of clear anomaly in structural noncentrosymmetry around the magnetic transition point T N for both bulk and nanoscale BiFeO 3 . The extent of anomaly increases in the nanoscale.

The precursor powder for bulk and nanoscale BiFeO 3 has been synthesized by solution chemistry route. 6 The powder was compacted and sintered at 830 o C for 5h for preparing the bulk sample. The nanosized particles were synthesized by heat-treatment at 450 o C for 2h. The samples have been characterized by laboratory x-ray diffraction, scanning electron and transmission electron microscopy. The representative SEM image of the bulk sample is shown in Fig. 1a while the TEM image for the nano-sized particles are shown in Fig. 1b. The high-resolution TEM image shown in Fig. 1c shows the nanosized particles to be single crystalline. The magnetic measurement has been carried out across 300-800 K in order to determine the magnetic transition points T N of the samples. In Fig. 2, we show the magnetization versus temperature plot. The T N appears to have dropped down from ∼625 K in bulk to ∼575 K in nanoscale particles. The transition width, of course, has increased from ∼50 K in bulk sample to ∼150 K in nano sample. The broadening of the transition zone results from distribution in the particle size. In order to determine the multiferroic coupling in these samples, laboratory x-ray, synchrotron x-ray, and powder neutron diffraction patterns have been recorded at several temperatures across the respective T N s. The synchrotron data have been collected at the ID31 beamline of ESRF, Grenoble while the powder neutron diffraction data were recorded at the E9 diffractometer of HZB, Berlin.

For determining the accuracy of atom positions, especially of lighter atoms such as oxygen, estimated from the laboratory and synchrotron x-ray data with respect to that determined from the powder neutron diffraction data, we compared the oxygen positions obtained from these experiments for the bulk BiFeO 3 . In the supplementary document, we provide the patterns and their refinements as well as the atom positions obtained from the refinement. 7 The results of the refinement are given in Table-I of the supplementary document. The comparison of the oxygen position -obtained from three different experiments -shows that while O x and O z positions determined from synchrotron x-ray and neutron diffraction data are comparable within ∼0.5%, the laboratory x-ray data yield a result which differs by ∼2-3%. Interestingly, in the case of O y position, both the laboratory and synchrotron x-ray data yield results comparable within ∼0.5% while result obtained from neutron diffrac- tion differs by a staggering ∼20%. This is quite surprising. It appears that the use of laboratory x-ray and even synchrotron x-ray data for determining the position of oxygen atoms cannot lead to a correct result. We have, therefore, restricted ourselves in using powder neutron diffraction data while investigating the structural noncentrosymmetry in bulk and nanoscale BiFeO 3 . In Fig. 3, we show the representative powder neutron diffraction patterns at several temperatures across the T N s and their refinement for both bulk and nanoscale samples. The patterns could be refined by considering the space group R3c with hexagonal unit cell. The space group does not change from bulk to nanoscale. The propagation vec-tor for the magnetic structure is (0, 0, 0) both for bulk and nanoscale samples. The refinement of the diffraction patterns yields the structural details as well as magnetic moment per Fe ion in Bohr magneton (µ B ). For the bulk sample, the magnetic moment increases from The off-center displacement has also been calculated by noting the center of positive and negative charges in a unit cell. 5 The plot of net off-center displacement as a function of temperature is shown in Fig. 5. This is the central result of this paper. For both the bulk and nanoscale samples, clear anomaly could be seen around the magnetic transition points. The off-center displacement appears to have been suppressed at the onset of magnetic order around T N . While the extent of suppression for the bulk sample is ∼1%, in nanoscale sample it shoots up to ∼12%. Earlier x-ray diffraction data by others did not show any clear anomaly in offcenter displacement around T N even for a bulk sample. 5 Therefore, it appears that the high resolution powder neutron diffraction data are more useful in determining the position of the oxygen ions and hence the off-center displacement in a cell accurately. Because of antiferromagnetic spin cycloid in bulk BiFeO 3 , it was predicted that the linear multiferroic coupling could be nearly zero. 8 Indeed, the polycrystalline samples investigated in the past did not depict a strong coupling. 9 However, in a single crystal of BiFeO 3 one observes a very strong coupling. 1 The origin of this strong multiferroic coupling in bulk BiFeO 3 appears to lie in the coupling between polarization resulting from the spin cycloid 10 and that from the polar domains. In the nanoscale sample of size smaller than 62 nm, the spin cycloid becomes incomplete and hence one should observe a weak coupling. However, it has been observed that incomplete spin spiral gives rise to a ferromagnetic component and hence larger magnetization. Large magnetization is associated with the rotation of nonferroelectric oxygen octahedra around the [111] axis via Dzyaloshinskii-Moriya exchange interaction. 11 This rotation, in turn, is coupled with the rotation of the polar domains. Therefore, enhanced magnetization could give rise to an enhanced multiferroic coupling.

Improved multiferroic coupling in nanoscale (∼22 nm) BiFeO 3 brightens the prospect of using this system in nanospintronics-based devices. It remains to be seen, however, whether the multiferroic coupling improves further as the size is reduced or does it decrease because of fluctuations in the magnetic and ferroelectric domain structures. The optimum size limit at which one observes strong multiferroic coupling needs to be determined and is a future challenge. In summary, we report here that from high resolu-tion powder neutron diffraction data, we could see clear anomaly in off-center displacement around the magnetic transition point T N for both bulk and nanoscale (∼22 nm) BiFeO 3 . This was not observed earlier from the x-ray diffraction data. The extent of anomaly, i.e., suppression of polarization at the onset of magnetic order increases from ∼1% in bulk to ∼12% in nanoscale. The coupling between the spin cycloid driven polarization and polarization from polar domains gives rise to the multiferroicity in bulk system. In nanoscale system, on the other hand, enhanced magnetization due to suppression of spin spiral leads to an enhanced rotation of nonferroelectric oxygen octahedra around the [111] axis. This rotation, in turn, gives rise to the multiferroic coupling observed via interactions between nonferroelectric and ferroelectric domains. It remains to be seen whether the coupling improves further or not if the particle size is reduced further. One of the authors (S.G.) acknowledges the Research Associateship of CSIR, Govt of India, during this work.

FIG. 1 :FIG. 2 :

 12 FIG. 1: (color online) (a) The scanning electron microscopic image of the bulk sintered pellet of BiFeO 3 ; the average grain size ∼0.5 µm; (b) the transmission electron microscopic image of nanoscale particles of BiFeO3; (c) the high resolution transmission electron microscopic image of a nano-sized particle of BiFeO3; the particles are single crystalline with (012) planes perpendicular to beam direction.

  ∼3.239 µ B /Fe at room temperature to ∼3.979 µ B /Fe at T N whereas for the nanoscale sample it is ∼3.979 µ B /Fe at room temperature and reaches ∼4.143 µ B /Fe around T N and then drops sharply. In Fig.4, we show the variation of the structural parameters such as lattice volume, bond length, and angle across T N s for both the samples. Evidently, in both the cases clear anomaly could be seen around respective T N s. Therefore, onset of magnetic order appears to have influenced the structural parameters significantly. This could result from magnetostrictive effect. The lattice volume depicts a mere change in slope in variation with temperature across T N for bulk sample while a clear volume contraction could be noticed around T N in nanoscale sample signifying first order nature of the transition in nanoscale. It is also possible to note that at any given temperature the lattice volume increases from bulk to nanoscale. And while there are two types of (Bi-O) bonds -long and short -within a unit cell for bulk sample, in nanoscale sample, one observes long and short (Fe-O) bonds. The long (Bi-O) bond shortens with the decrease in temperature below T N and the length of the short one increases in bulk sample. Likewise, in nanoscale sample, the long (Fe-O) bond shortens while the short bond stretches with the drop in temperature below T N . Therefore, there is an anisotropy in the striction effect both in between two types of bond as well as in between Bi-O and Fe-O bonds. When two types of bond merge into one, it depicts a drop in length both in bulk and nanoscale sample.

FIG. 3 :FIG. 4 :

 34 FIG. 3: (color online) The powder neutron diffraction data and their refinement at several temperatures across T N for (a) bulk and (b) nanoscale BiFeO 3 .

FIG. 5 :

 5 FIG.5:The anomaly in noncentrosymmetry around T N both for bulk and nanoscale BiFeO 3 .


Christine Plumejeaud-Perreau
email: christine.plumejeaud-perreau@univ-lr.fr

Eric Quinton

Cécile Pignol

Hector Linyer

Julien Ancelin

Sébastien Cipière

Wilfried Heintz

Mathias Rouan

Sylvie Damy

Vincent V Bretagnolle

Plumejeaud-Perreau

Julin Ancelin

Towards better traceability of field sampling data

Keywords: Data traceability, QR code, labels printing, samples management

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

Introduction

A new information system for managing samples and their associated data has resulted from a French national collaboration between "Zones Ateliers" (http://www.za-inee.org/). They are the French nodes of The European Long-Term Socio-Ecological Research (LTSER) Infrastructure since 2006, (http: 5 //www.lter-europe.net/), a research network applying intensive monitoring schemes in LTSER sites, with a widespread global coverage composed of a vast number of sites [START_REF] Vanderbilt | Fostering ecological data sharing: collaborations in the International Long Term Ecological Research Network[END_REF]) that aims at fostering biodiversity and environmental data sharing.

One of the peculiarities of this research network is a very strong anchoring 10 in the field experiments, with numerous samples of various kinds collected in 2 outdoor conditions. Contributing to the computation of Essential Biodiversity Variables [START_REF] Proença | Global biodiversity monitoring: From data sources to Essential Biodiversity Variables[END_REF]) -one of the tasks taken up in the Zones Ateliers and presented here as an example -requires the use of standardized field protocols, striving to solve data quality issues. While the many differences observed in sampling protocols may be resolved by post-collection data harmonization techniques [START_REF] Henry | Integrating ongoing biodiversity monitoring: potential benefits and methods[END_REF]), such mathematical methods can't always solve the mismatches and errors due to a lack of traceability of field experimental data. Between the time when geolocalized specimens are sampled from biotic or abiotic resources, and when the resulting data ends up in analysis software to be published with a study, many manual operations have occured. It turns out that people involved in the data life cycle (acquisition, integration, analysis, re-analysis) are very diverse (researchers, trainees, short-term contracts) and tend not to have an overview on the full processing chain of samples. Most often there is a lack of documentation because writing documentation is time consuming for a monitoring task often done with few resources. This situation generates errors when handling samples and consequently some loss of information. To prevent errors, automatic and systematic documentation of the data processing chain is essential and will help to guarantee the quality of analyses and the scientific conclusions drawn from these data. That said, traceability of field experimental data remains a challenge nowadays to conduct reproducible research (Wilkinson et al. (2016)), [START_REF] Mcnutt | Liberating field science samples and data[END_REF]).

Our software named COLLEC-SCIENCE solves this by offering a traceability feature of both samples and associated data along the whole processing chain.

Compared to existing solutions in laboratories, the labelling solution has been adapted to outdoor working conditions, with a robust support for the management of storages and movements of samples. It provides sufficient flexibility to withstand the wide variety of descriptions of the field samples in order to retain the contextual information associated with the samples, which are important parameters of the subsequent analyses. It aims at transmitting those contextual data along the processing chain through an appropriate mechanism (such as inheritance or copy). This paper is structured as follows. The second part explains the detailed requirements for managing samples and associated data, collected through a enquiry that we have led during one year amongst "Zones Ateliers" participants.

The third part provides a state of the art by reviewing existing solutions. The fourth part is devoted to the description of the software that we have developed and tested on the ground. The last part concludes with a summary of our results, and a discussion about the prospects of this information system.

Analysis and synthesis of requirements

Consulting with the participants took place in 2016, involving field technicians and researchers who work on very different research subjects : from geosciences (soils chemistry, oceanic dynamics, paleo-climates) to ecology and biology fields (micro-mammals, bats, insects, birds, weeds, crops systems). We organized seven monthly 2-hours videoconferences, initially to talk freely about the problems and possible solutions with regard to the traceability of the data; then we organized two face-to-face meetings a month apart at the end of the cycle on two sites (Chambery and Besançon) targeting the following two examples:

• geological sediment samples extracted from lakes for abiotic samples

• monitoring of live taxa samples for biotic samples.

During those two meetings, we showed a prototype in order to help to refine the needs in the face of a concrete experimental situation. All the project's documentation (in French) and resources have been shared throughout the POPS platform (http://www-iuem.univ-brest.fr/pops/projects/za?jump=welcome),

which is an open-source Web platform favoring collaboration on research projects.

Labelling samples with a digital identifier

In terms of traceability of the data associated with the samples, it appears that the tracking of the samples and their successive transformations would first require an information system devoted to sample management. In laboratories working in the fields of ecology and geosciences, there are no (or rarely) databases of samples allowing for the identification of the owners, the day and place of collection, nor automatic ways to know where they are stored.

In general, samples are labelled to ensure their identification in the storage media. These labels must not only allow this identification but also provide additional information that will allow the samples to be tracked. Therefore, a system for labelling samples is desirable. The labels have to include human-readable information and machine-readable code (usually a barcode) that identifies and retrieves label information in a dedicated database. This type of system also should also make it possible to track the evolution of sample inventories, by dating the entry and exit events of the storage shelves. This can be useful for anticipating shortages of containers, or saving place by throwing out-of-date samples away. Tracking the movements of samples is really a key point to ensure the data sharing throughout various laboratories and institutions, because it facilitates the sample exchanges.

Mobility and robustness

The particularity of our requirements is the collection of samples in outdoor conditions. Therefore, the printing system must be transportable and robust: the printer must be lightweight; the labels themselves must withstand rain, sun and temperature variations. In fact, some samples remain out several weeks in the ground before being picked up for analysis. Compared to this constraint, thermal transfer printing on plasticized paper is a necessity. The equipment may become very expensive because there is a wide range of choices. However, the selected equipment has to be affordable because numerous devices are required in order to be used anywhere at anytime. Simultaneously, the device for data acquisition and label printing must be small and transportable, like a smartphone or a tablet. An important feature is that the device can be used even in off-Internet areas, in difficult access areas such as high in the mountains, or in arctic areas, and the device may require an electric battery for its power supply.

Flexibility and adaptability

In particular, such an information system should facilitate the capture of information on the sample, namely the contextual data. We believe that this kind of information, which before was only recorded on paper in a field notebook, should now also end up with the label in a database. Therefore, the computerized management of the sample becomes the opportunity to ensure the management and the traceability of those contextual data. In order to match current metadata standards, the date and geographic location of the collection are essential, as well as the name of the collector (author or principal investigator) and the project's reference that has funded the collection work. This will be done to ensure compatibility with the Dublin Core Metadata Initiative (http://dublincore.org/) or the DataCite initiative (https: //www.datacite.org/).

The main difficulty comes from the specificities and heterogeneity of the information related to each protocol. The to-design IT tool has to be flexible enough to capture this information. Between collections of insects, live micromammals, or cores of geological sediments, researchers measure very different characteristics, so they need very different attributes to describe their observations (Table 1). These attributes are part of their own specific protocol that keeps evolving each year (the units used to describe a measure may change, the observed characteristics may change, etc.). Many efforts are made in order to standardize monitoring protocols of biodiversity: for instance, inside the GEOBON network [START_REF] Van Swaay | Guidelines for Standardised Global Butterfly Monitoring[END_REF]) with the observation of butterflies, or inside the French Agency for Biodiversity that works at referencing existing protocols [START_REF] Gazay | Catalogue de méthodes et protocoles d'acquisition de données naturalistes[END_REF]). Nevertheless, there is a great diversity in the protocols, and due to the inherent creativity of research process, this is a never ending process. This is particulary true in the context of this environmental research that tests hypotheses through full-scale experiments.

The best practice for data traceability would be to save those data in a database in a digital form directly in the field [START_REF] Schuh | Integrating specimen databases and revisionary systematics[END_REF] Furthermore, metadata about the variables inside digital forms are often missing because variables are hard-coded inside softwares, and, most of the time, nobody has wrote down a glossary of all recorded variables, due to the general shortage of resource and time. The description of samples using metadata is often postponed (if not entirely overlooked). In particular, the description of the units used for measurements is rarely explicit. Our project aims at describing the meaning of those attributes (e.g. their description, their type, their unit of measurement) as soon as the form is specified by the researcher himself. This will automate the documentation of the variables, and, consequently, increase the quality and traceability of the data, saving time for all. This will create therefore a new way of working: labelling samples will become the alibi to collect the contextual data describing the samples, that we will name 'metadata'.

Tractability and inheritance of samples description

The last requirement is the automatic transmission of those metadata (e.g.

the contextual data describing the sample) along the processing chain. In fact, inside the physical and digital cycle of samples, as described in [START_REF] Lehnert | IG Physical Samples and Collections in the Research Data Ecosystem[END_REF]), the samples are continuously re-used, sub-sampled to extract new samples, that are in their turn re-sampled and analysed (Figure 1). Then, contextual data will go into a database with the data coming from the analyses. This contextual data needs to be replicated along the chain, without errors. That is why we think that the automatic copy (or inheritance) of those contextual data for a sub-sample, which will also be labelled and stored before or after its analysis, is a good way to obtain traceability of contextual data associated with samples.

Synthesis of the requirements

Our system should ensure user authentication, inventory management, label printing and labelling, as does any Laboratory Information Management System (LIMS). It must also be transportable in off-net areas. It should allow for the edition of user-defined forms, therefore adapted to each type of samples. It has to ensure the documentation of collected variables (e.g. the metadata), and their transmission along the processing chain of samples and associated data.

This must be done technically independently of the operating system of the purchased hardware (Android, IOS, Windows 10), since many monitoring and research sites have already invested in some equipment. The solution must be cheap (under 1000 euros per unit at least), since the sets of devices are numerous, at least 100 units per year, all French sites considered.

Review of solutions available in 2017

Most of the involved laboratories turned out not to have a defined reference repository, with a plan and numbering of buildings, rooms and cabinets in which the samples are stored. This problem is international and has been acknowl- Targeting this samples traceability problem, there are indoors solutions, classified as LIMS, which are intended to ensure the management and traceability of the samples. They are used extensively in the medical and life sciences fields [START_REF] Campbell | Development of the ISBER Best Practices for Repositories: Collection, Storage, Retrieval and Distribution of Biological Materials for Research[END_REF]), in the so called "Bio Banking" domain [START_REF] Müller | From the evaluation of existing solutions to an allinclusive package for biobanks[END_REF]), but they are rarely used in environmental research domain. When those solutions rely on a proprietary and integrated computer system, it is not possible to deploy them in an academic research for environmental domains. When looking at commercial products, the cost is also an obstacle (Triplet and Butler (2012a)): for instance, "Samples" is a software for sampling management sold at 2703 euros12 by Dutscher company.

Furthermore, those commercial products still do not address our requirements for mobility nor traceability along the samples chain. "LabCollector" LIMS (http://labcollector.com/labcollector-lims/features/) offers only a restricted mobility, since a Wifi access point must be available to link toward the Intranet of the laboratory in order for it to work.

However, there are some open-source LIMS softwares that are worth mentioning: [START_REF] Dondeh | Review / Evaluation of LIMS/Biobank Open source systems[END_REF]), [START_REF] List | Efficient Sample Tracking With Open-LabFramework[END_REF]), among which "Baobab" [START_REF] Bendou | Baobab Laboratory Information Management System: Development of an Open-Source Laboratory Information Management System for Biobanking[END_REF]), "EnzymeTracker" (Triplet and Butler (2012b)), "Specify" (http://specifyx.specifysoftware.org/), and "OpenSpecimen" (https:

//www.openspecimen.org). They have in common that they use the Web as an interface for sample management. But they do not offer a mobile feature in off-Internet areas, or when it is done, it is on a particular brand of device (such as Apple iPad for "Specify"). Some of them seem very attractive: the need to customize forms is addressed by "OpenSpecimen" for instance. Most often, they are focusing on one particular domain of activity (biological research collections for Specify, clinical studies for "OpenSpecimen"), and they go often far beyond our needs by integrating data analysis results, very specifically to their activity domain, and the result is a very complex software. IT technical support for one of these solutions is expensive: "OpenSpecimen" provides a basic product support for 15000 dollars US per year.

Concerning the collection of data in the field, there are electronic field forms generated by applications embedding a database [START_REF] Ancelin | Géo-Poppy[/i], un serveur web SIG portable pour le recueil de données terrain[END_REF];

Prud'homme ( 2016)); [START_REF] Quidoz | Séminaire Système d'information embarqué, cahier/carnet de terrain et de laboratoire électronique : quelles interactions avec les bases de données ?[END_REF]). Those applications offer various interesting potentialities including the automatic capture of dates and georeferenced location in the field, but none of them has the possibility to print labels with a digital identifier so far. Moreover, the collected data does not integrate automatically with a sample management system nor they do provide any metadata about the attributes that are part of the form.

Concerning the traceability and citability of physical samples, we are looking also at the earth sciences/geology fields that may be the most advanced discipline, thanks to the use of the International GeoSample Number (IGSN) system (www.igsn.org), which was established to provide globally unique identifiers for geological samples. An infrastructure for the management of unique persistent identifiers for geosamples has been deployed and named System for Earth Sample Registration (SESAR), with a dedicated Web site (www.geosamples.org).

The utility of this system is acknowledged by the earth sciences community, and it has become more and more used since its beginnings around 2010 [START_REF] Gil | Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance: Geoscience Paper of the Future[END_REF]; [START_REF] Devaraju | A Digital Repository for Physical Samples: Concepts, Solutions and Management[END_REF]). Using the "handle system", it provides online tools for users to register samples, submit and manage their sample metadata and search the metadata catalog. Biodiversity fields have lot of lessons to learn from this successful experiment [START_REF] Cox | Specimen Identifiers in Related Disciplines: What can Biodiversity Learn from and Offer to Other Fields?[END_REF]). The principle is similar to Digital Object Identifier (DOI): it aims at maintaining a link between the physical sample uniquely identified at the world scale and its metadata describing contextual information about its collection in a landing page. This is very inspiring, even though our current design is focusing on the process of meta-data creation and transmission along the sample's life cycle, which is a step before presenting them in a final landing page like done through DOI or IGSN identifiers.

COLLEC-SCIENCE: design and implementation

The org/index.php/OWASP_Zed_Attack_Proxy_Project) and did not detect any particular weakness. Furthermore, the physical encoding (printed QR codes) is an important insurance against hacker-caused corruption of databases: it offers a way to repair, since analogue printed QR codes are impossible to be corrupted digitally. The software design was well documented in the GITHUB repository, with many comments on the code also (in French however). But this solution was not mobile then.

Mobility of the system

In the beginning, COLLEC-SCIENCE was a Web LIMS running only on a central server in a laboratory. In order to support a mobile solution working in outdoors conditions, far from any Internet or even mobile network, we have worked for embedding the software and its database inside cheap small computers, namely the Raspberry Pi (https://www.raspberrypi.org/), designed to be small (400 g) and affordable (60 euros for a complete kit). In order to ease the process of installation of the embedded software inside the Raspberry Pi, we have worked with Docker (https://www.docker.com/) to build an open-source container including the whole set of required libraries, codes and database (https://github.com/jancelin/docker-collec), in order to get in just one click the installation and configuration for a mobile device.

The mechanism of data synchronization is simple enough for the operators having no IT skills: it is an export of samples's metadata in a Comma-Separated Values (CSV) file through the Web interface, that can be easily re-imported inside any server's database, using also the Web interface. At the import of a CSV file, the program checks for the uniqueness of the identifier of the samples:

it is composed of the unique identifier (UID, which is a serial ID generated by the Postgres database, an unique integer in the local database) and the name of the local database dbuid origin which is a parameter of the application that can be easily configured through the Web interface. When samples already exist in the local database, they are updated, else they are created.

Using a QR code in the labels

The software is based on a barcoding method, using labels that are machine readable. We have designed a label containing a QR code that encodes the sam- Following their use-case (size of the label, external identifiers or not, known additional information), researchers have the freedom to put any information in the template of the QR code that they design using FOP (Figure 3). To address the robustness requirement, we have selected thermal transfer printers, and corresponding paper and ink quality. We detail them with the brands and the chosen models because many combinations exist between paper and ink types, and they can lead to failures in term of resistivity to the labels.

Our choice was to use a small desktop printer, namely the Zebra GX430 T model, that can print from very narrow (33.8 mm) up to wide (109.2 mm) labels, in thermal transfer mode with a resolution of 300 dpi, with the Bluetooth and USB capacity to push data to print. Our tests confirm that ink provided by the AXR R 7+ standard resin ribbon from ARMOR company, or the ZipShip 5095 resin ribbons from Zebra company is a good choice. We combined it with the Z-Ultimate 3000T paper, a pearl white and gloss polyester thermal transfer label with permanent acrylic adhesive. We made various tests of resistance:

we put the labels inside a water cup for three months, we dried them in an oven at 100 • C, we have let them rot under the sun and rain for 2 months. The labels were still readable and scan-able after that. The printer with the ribbon

and its paper roll weigh 2.5 kg. In 2016, it was possible to buy another model, The idea is that all contextual data associated with a sample are very different according the kind of monitoring, and it is required to give sufficient degrees of freedom to the protocol designers, in order to adapt the list of attributes by themselves. We have proposed to use alpacajs (http://www.alpacajs.org/), a javascript library providing easy forms for JQuery Web based applications.

We have integrated this feature in COLLEC-SCIENCE so as:

• Protocol designers can build their own form using the Web interface; they have the choice between various types for each attribute: number, text, Booleans and list of choices (CSV files can be used to import the content of lists).

• The corresponding form template which is called Metadata is an object

Metadata having a name metadata name and a JSON schema named metadata schema in the data model of the Figure 5. This schema is associated with the kind of sample (Sample Type) collected by the researcher (insects traps, cores of sediment, etc.), and it can be imported to/exported from COLLEC-SCIENCE through the Web interface.

• Moreover, for each attribute known by its name, this JSON schema contains also a description, a unit of measurement, and a type. This information is quite important for further analysis of the samples.

• When editing a sample of a certain kind, the JSON schema is automatically interpreted in the Web interface, and this allows operators to fill and store digital field forms stored within the metadata field of the Sample object.

In order to make easier the filling of each sample contextual data, and therefore gain time, the last entry stays in memory and pre-fills all the form. The operator has just to change a minimum of information like the human defined identifier named identifier in the database 13 . For an ergonomic usage, he can also choose to acquire the GPS position automatically (wgs84 x, and wgs84 y fields of the Object whom Sample inherits). The dates of the sampling (sampling date) and of the import in the database (sampling creation date) are automatically pre-filled also. In order to ensure compatibility with other systems that may not use the same system for structuring and describing samples, COLLEC-SCIENCE provides CSV export and import functionalities of the samples and their description, in batch mode. The fact that the JSON schema of the metadata can be shared between many actors is also a benefit for compatibility between softwares. There is no standard at this time for describing a sample despite a started initiative by [START_REF] Lehnert | IG Physical Samples and Collections in the Research Data Ecosystem[END_REF]), and waiting for that kind of standard, we argue that it is a valuable proposal allowing more diversity in the kind of samples.

Moreover, in order to manage the exchanges of samples between laboratories or actors using different sampling management system, we implemented a special feature for supporting many external identifiers for one sample. As shown on the Figure 5, each sample (Sample) has its own unique identifier (UID) in the local database named (dbuid origin), but you can also describe it by the various identifiers (object identifier value) of any kind (Identifier Type) by which it is known in other information systems, such as a IGSN number of the SESAR's registry in our previous example, or a DOI or ARK identifier that would be given to a biological sample. This is done through an association between the Object whom Sample inherits, and zero to many Object Identifier having a textual value object identifier value, being described by their own kind of category (Identifier Type) having a name (identifier type name) and a code (identifier type code) such as IGSN or DOI for instance.

The sub-sampling feature of COLLEC-SCIENCE allows for the derivation of primary metadata and it follows the lineage of the samples. For instance, a Barber trap pot labelled with the date and location of the collection may produce as many new sub-samples as the number of trapped insects, each insect being identified and stored as a new sample in an Eppendorf mini-tube. COLLEC-SCIENCE allows for derivation of these new samples from the starting Barber trap pot, by getting the first data (location, time, etc.), and the operator adds only the required new data such as the species of the insect, the name of the person who identified, and the expiration date of the sample (the Figure 6 compares the two schemas for this example).

Further analysis may be conducted without producing new samples: they won't be stored into the COLLEC-SCIENCE automatically because this is not the purpose of COLLEC-SCIENCE. However, every one can get back all these contextual data about each samples through an export in a CSV file. This CSV file can be used to initiate a new database dedicated to specific analyses.

Results and Discussion

Results

It seems to us that a solution based on a Web interface, using an embedded interface inside a small computer such as Rapsberry Pi could really be an opportunity for saving IT resources [START_REF] Ancelin | Géo-Poppy[/i], un serveur web SIG portable pour le recueil de données terrain[END_REF]) and for developing COLLEC-SCIENCE and associated devices (printers, scanners, paper and ribbons for labels) have been deployed in real testing conditions in 6 sites:

• at Bordeaux, in the EABX laboratory of IRTEA;

• at Besançon, in the Chrono-environment laboratory for Zone Atelier "Arc

Jurassien";

• at Chizé, in CEBC laboratory for Zone Atelier "Plaine & Val de Sèvre";

• at Chambéry, in EDYTEM laboratory for Zone Atelier "Alpes";

• at Toulouse, in DYNAFOR laboratory for Zone Atelier "Pygar";

• at Brest, in LETG laboratory for Zone Atelier "Brest-Iroise";

Three kinds of samples are manipulated in those tests:

• fish samples coming from the Gironde estuary, next to Bordeaux;

• tubes of geological sediment, either coming from lakes (Chambéry, Besançon), or from the seabed (Brest);

• insects catched using Pantrap of Barber traps (Chizé, Toulouse, Besançon). have not yet been analysed in order to time the operations that our solution takes, compared to usual ways of working of field operators. Also, it would be interesting to quantify the space that can be saved in refrigerators, cold rooms, freezers, shelves of any types that are often totally full of outdated samples.

Discussion

We are deeply conscious that we must now lead the path towards open-data and interoperability of samples description [START_REF] Moritz | Towards mainstreaming of biodiversity data publishing: recommendations of the GBIF Data Publishing Framework Task Group[END_REF]), [START_REF] Gil | Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance: Geoscience Paper of the Future[END_REF]), (Wilkinson et al. (2016)). There is a need for a balance between freedom for innovative research, and requirements for interoperability. The vast choice of Metadata standards and formats remains for the moment very linked to the nature of the collected thing (Willis et al. (2012)), and it is not pos-sible to select one or another if your requirement is to be as much generic as possible. Interoperability often means the use of controlled vocabularies, like those proposed in environmental thesauri (GEneral Multilingual Environmental Thesaurus14 , EnvThes15 , etc.). However, most of the researchers don't know that they exist, or they are unable to make a choice between the various thesauri and ontologies. Furthermore, the protocol design process is really a fast one, it implies some creativity in the field, and may invent new variables that may not be already listed in controlled vocabularies. For the moment, COLLEC-SCIENCE gives sufficient freedom and flexibility to researchers for adapting quickly to diverse situations, giving them the taste for using the tool.

In our point of view, labelling samples is only the alibi for collecting metadata information about contextual data describing the samples.

That is why COLLEC-SCIENCE asks to provide a quadruplet composed of (name, description, unit of measurement, type) for each new metadata attribute.

The fact that some information such as the spatio-temporal location of the sample, the name of the project, etc., are mandatory, and that the system allows for the storage of a protocol description in PDF format will help to map data with international standards that are being elaborated inside a Research Data Alliance group of interest for instance [START_REF] Mcnutt | Liberating field science samples and data[END_REF]; Lehnert (2017)). Furthermore, the quadruplets composed of (name, description, unit of measurement, type) can be further automatically explored by semantic engines to map automatically towards metadata standards.

Concerning the sample's traceability and citability, collecting metadata for samples as done in COLLEC-SCIENCE, and linking them to a unique identifier like the IGSN or DOI very early in the data life cycle of the samples becomes our next objective. COLLEC-SCIENCE, which can support multiples identifiers for the same sample, and promote a distributed model of deployment, could then play a role for citability of samples. COLLEC-SCIENCE could use the handle system as done for IGSN or DOI, and offer landing pages for samples, and export metadata for samples that match the requirements that have been defined in the last extension of the ontology for observations and sampling features model, standard of Open Geospatial Consortium (OGC) (Cox (2016)). This would therefore converge our efforts towards those recently produced for taxon samples [START_REF] Güntsch | Actionable, longterm stable and semantic web compatible identifiers for access to biological collection objects[END_REF]), in order to follow the Linked Open Data principles.

Conclusion

Ensuring traceability of both field experimental data and laboratory sampling to conduct reproducible research is still a challenge at the present time.

In this paper, we describe the need for an information system for samples management, capable of storing all samples' locations and their movements between various containers, as any LIMS do nowadays in indoors conditions. We underline also the need for an integrated solution working in the outdoors, on the field, often without any Internet connection.

However, our strongest requirement is the fact that the model of the samples database has to be very generic in order to handle the wide diversity of the handled samples, offering digital forms to fill in outdoor conditions the contextual data linked to the samples. The main objective is to prevent errors due to manual copy of crucial information (such as the geo-location, the creation date or the project that was funding the research) when sub-sampling primary samples. The derivation capability of contextual data for the sub-samples was reported therefore as an important feature, in order to keep the lineage along the samples processing chain.

The analysis of existing solutions showed that no open-source and free software or device could totally answer to those requirements, by looking either in the domains of LIMS or the domain of digital field forms development.

COLLEC-SCIENCE, the new information system we propose, benefits from a collective design of people working in Zones Ateliers sites and addresses those practical requirements. Our next efforts will tend towards semantic interoper-ability of the data and citability of samples stored with COLLEC-SCIENCE.

Acknowledgments

This research has been funded since January 2016 thanks to "Zones Ateliers", a research infrastructure program labelled SOERE at French national level by the ALLiance for ENVironmental Research (AllEnvihttps://www.allenvi.

fr/groupes-transversaux/infrastructures-de-recherche/les-soere/liste-des-soere).

We wish to thank all people that gave us feedbacks in the meetings, and thus helped to design collectively the system, in particular Arnaud Caillo, Francis Raoul, Anne Clémens, Florence Dujardin, Françoise Le Moal, and Emmanuelle Montargès-Pelletier. Many thanks also to the three reviewers whose remarks help up to improve substantially both the form and the content of this paper.

Figure 1 :

 1 Figure 1: Traceability along the sample processing chain.

  edged by[START_REF] Mcnutt | Liberating field science samples and data[END_REF]). The Research Data Alliance consortium is now hosting a new group working on this question : "Physical Samples and Collections in the Research Data Ecosystem IG" 11 . This group promotes the use of a unique persistent identifier for samples, that may also be written on the label of each sample.The study of the various bar-coding systems show that QR code (abbreviation of Quick Response Code) is an adequate candidate. This system has had a great success in famous institutes such as the Smithsonian Institution (Thompson (1994)), and is used at a wide scale in Costa-Rica with the INBio project[START_REF] Salin | Costa Rica's INBio: towards sustainable use of natural biodiversity[END_REF]). QR code is an international standard (ISO/IEC 18004:2015) for two-dimensional codes, allowing for the encoding of 4296 alphanumeric characters, much adapted to the management of biological collections, (Diazgranados and Funk, 2013). Its usage is wide-spread through industry and business, therefore there are many free QR code reader applications for reading a QRcode on any smartphone or tablet. In France, some labelling systems are in use for samples created and manipulated in laboratories: BarCode at INRA[START_REF] Salin | Costa Rica's INBio: towards sustainable use of natural biodiversity[END_REF]) using a 1D-code, and GeCol (https://gecol.ird.fr/) at the French Institute for Research and Development. However their identifiers are unique only inside their own database, and they promote only a centralized model of deployment. When setting up a new database using those systems, one may have accidentally create overlaps between old identifiers and new ones. Furthermore, no traceability features are built in, nor options to produce metadata for variables describing samples.

  development of COLLEC-SCIENCE follows the open-source development model. The codes are freely available on GITHUB (https://github.com/Irstea/collec), and well documented(Quinton (2017b)) in French and English (the Web interface is fully internationalized with a support for both languages). Our collaboration with the national Research Institute of Science and Technology for Environment and Agriculture (IRSTEA), who agreed to integrate additional modules and ideas, has produced a software that meets all the requirements previously described.4.1. A LIMS with a Web interfaceThe very first version of COLLEC-SCIENCE, in August 2016, was already an attractive kind of LIMS solution, Web-based and using open-source components (PHP 7, Postgres 9.5, Apache, HTML5 and CSS with bootstrap.js, JQuery.js, and various javascript plug-ins). Basically, the main feature was the management of sample inventory (in/out of samples) with a classic but efficient management of containers (hierarchically organised) describing the location of the sample storages. It had also the capability of printing and reading labels for samples, of any formats, with a QR code encoding the sample's ID. The "Formatting Objects Processor" (FOP, https://xmlgraphics.apache.org/fop/) module allows for the design of any label in an autonomous way: users can write themselves the textual content, and design the labels's shape and size.French research institutes are very concerned by data security and the protection against hacking of Web applications(Quinton (2017a)). That's why the solution offers only an HTTPS access. A secure authentication was implemented, with the option of using Lightweight Directory Access Protocol (LDAP). The administrator role can always create a login/password for any user if required. The application has been designed to withstand so-called opportunistic attacks according to the nomenclature from the Open Web Application Security Project (OWASP, https://www.owasp.org/index.php/Main_Page) called ASVS v3 (Van der Stock et al. (2014)). For each release delivery, attack tests were carried out with the ZapProxy software (https://www.owasp.

  ple's ID, but also encapsulates the descriptive information about context of the sampling (project, location, time, principal investigator, etc.) in a JavaScript Object Notation (JSON) format. Using COLLEC-SCIENCE, the unique identifier (uid ) of the sample and the name of the database (db) are mandatory, but like shown on the Figure2you can choose to encode other important variables inside the QR code. Our point of view is that databases may be lost in the future due to technical deprecations, but encoded data in the QR code won't be lost. For instance, the QRcode can contain the human defined sample identifier (id ), the name of the project (prj ) that has funded the sampling, the geographic coordinates (x, y) and the creation date of the sample (cd ), the name of the Principal Investigator for this sample (pi ). COLLEC-SCIENCE is also able to manage external identifiers, such as the IGSN, and thus you can paste it in the QR code.

Figure 2 :

 2 Figure 2: A label sample used for cores of sediments with the associated QR code.

Figure 3 :

 3 Figure 3: Using the FOP properties to design the label and QR code data.

  compatible with the paper and chosen ink, namely the P4T Zebra printer, which weighs only 1.3 kg. One of the most important features of COLLEC-SCIENCE is the capacity to define a digital field form adapted to each case study. In fact, a part of the data describing the sample are common to all samples and the ones with an asterisk 355 in red * are mandatory, as shown on the Figure 4. The other part of these data, under the heading "Associated metadata" are defined by the protocol designer.

Figure 4 :

 4 Figure 4: A screen shot of the interface describing a sample (a trap pot).

Figure 5 :

 5 Figure 5: Extract of the data model used by COLLEC-SCIENCE.

Figure 6 :

 6 Figure 6: Examples of JSON Schema structuring the description of contextual attributes (metadata) associated with the samples.

430

  something generic. Thus we have collaborated with IRSTEA(Quinton (2017b)) in order to adapt a free new open-source solution for samples management, and we achieved this in one year. The result is both a free and open-source software, namely COLLEC-SCIENCE, and a set of tests and recommendations for devices, equipments and their installation using Docker's facilities. Some templates for metadata and labels are provided to start with, with a demonstration Web site: https://demo.collec-za.org.

Table 1 :

 1 Three examples of attributes lists that are dissimilar.

	). However, one of

Main author of the software COLLEC-SCIENCE, a software using a secured framework taking into account all the guarantees against hacking.

Specialist of data curation in the geology domain, Cécile has brought many useful remarks for the design of the solution from a user point of view.

Trainee under Plumejeaud' supervision who has worked on the metadata's module to implement and integrate it within COLLEC-SCIENCE.

Julien has helped to build Docker containers and images for COLLEC-SCIENCE.

Hired as Research Engineer during 4 months, Sébastien has produced a benchmark for labels and printing solutions, in order to ensure their resistivity at the best cost ratio.

Currently administrating and using the solution using COLLEC-SCIENCE for the French eLTER network.

Mathias is in charge of all interoperability and standards conformance aspects for the project.

Assistant professor in computer science, Sylvie is currently involved in the specifications of the solution and its enhancement for a better user's experience.

Scientific leader of the eLTER-France program and the "Zones Ateliers" network, Vincent has funded the work of this group.

https://www.rd-alliance.org/groups/physical-samples-and-collections-research-data-ecosystem-ig

https://www.dutscher.com/frontoffice/product?referenceId=779944

The local unique identifier (UID) is shown but can't be edited in the interface.

https://www.eionet.europa.eu/gemet/en/themes/

http://vocabs.ceh.ac.uk/evn/tbl/envthes.evn

Code parcel Code line trap Depth top Code crop Number of traps Depth bottom Session Code individual Type of sediments Kind of trap Weight Number of runs Observer Sex


Uncertainty notation guidelines for scientific publications.

Greg Henning 1,⋆ 1 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France.

Abstract. In the following document, I present the motivation for defining a few guidelines regarding uncertainty notation. The guidelines are given in section "The rules"

Why the need for guidelines?

As it turns out, the scientific world has not given itself a set of rules for expressing uncertainties. This is understandable as we cannot reasonably expect all (or even a large number) of fields and teams to have the same requirements, and therefore agree on a fixed set of convention.

Nethertheless, the number of documented conventions is rather small, even when looking at specific places. A quick web search for uncertainty notation guidelines yields a surprisingly poor amount of documents going much further than statements like "The uncertainty of a value is its estimated standard deviation."

More surprising, scientific journals such as EPJ's, Physical Review's and even Nature, in their writing guidelines (see in references at the end) barely mention uncertainty. When they do, it is mostly to remind authors that some uncertainties should (not "must") be given. Even Wikipedia is short on the subject.

Consequently, the notation and meaning of uncertainties sometimes seems to be an unwritten tradition, passed down from Adviser to Student, generations after generations. Somewhat fittingly, "uncertainty" often is a confused notion.

In fact, the reference document on the matter, the Guide to the expression of uncertainty in measurement (known as "GUM") gives, in its section 7.2.2, four ways of stating a value with uncertainty:

• x with an uncertainty of u x ,

• x (u order x ), where u order x is the value of the uncertainty referred to the corresponding last digits of x.

• x (u x ), • x ± u x ⋆ e-mail: ghenning@iphc.cnrs.fr
Apart from a comment discouraging the last form, the GUM only calls for an explicit and full documentation of the value, its uncertainty and how it was obtained. This leaves the scientific community with plenty of liberty to write (within the scope of accepted but unwritten conventions) the uncertainty in a way he prefers. Unfortunately, the convention used is rarely explicited. The consequences are:

• Given their fuzzy definition, uncertainties are often secondary, both when conducting the work and reading the reading publication.

• Comparing data with given uncertainty can require an important work to understand what the stated numbers represent and put them to the same standard.

Recognizing the fact that any publication needs an explicit and clear set of rules to write and read uncertainty, I propose in the following a convention. It is obviously not the the only way (and probably not even the best).

I strongly encourage anyone to sit and consider adopting a standard on uncertainty notation that fits his needs. The following conventions are not fixed and will be updated as needed and according to feedbacks to this paper.

Notation convention

In the following convention, I will follow the principle that Explicit is better than implicit

Types of uncertainty

In this convention, I consider two general distribution shape that each deserves its own specific notation: Flat and Gaussian.

• A flat uncertainty means the probability distribution of the variable is uniform from a lower to an upper limit around a value (not necessarily symmetric).

• A Gaussian uncertainty means that the probability distribution follows a normalized Gaussian shape around the central value (not necessarily symmetric).

Other shapes (triangular, log-normal, Lorenztian, Maxwell-Boltzmann, Poisson, . . . ) should be explicited when needed with all the parameters of the distribution

The Rules 1. Uncertainty is given in full. That means that the x (u order x ) notation is not accepted. There is no reason why the uncertainty should be given in a shorten way, or that the reader would have to do his own calculations to recover the actual uncertainty number.

2. Uncertainty is given in the same unit as the value it refers too. That means an uncertainty should never been given as relative. Relative uncertainty is a comparison tool, not a way of expression.

3. Gaussian uncertainty is expressed like x (u x )or x( -ux +ux ) in case of asymetric distribution. In x (u x ), u x is the standard deviation (1σ) of the distribution. 2 4. Flat uncertainty is expressed like x ± δ xor x -δx +δx in case of asymetric distribution.

Uncertainties are combined by convolution of probability distribution functions

The final combined uncertainty is not (necessarily) the quadratic sum of its components.

6. "Statistical" uncertainty refers to an uncertainty from lack of static, but does not presuppose a given shape or value It's not because a value is labelled as statistical that it will have a Gaussian uncertainty with a standard deviation of √ N .

Reference documents on uncertainties: