Keywords: Satellite image retrieval, spatio-temporal ontology, mutli-modality ontology, ontology matching. How Satellite Image

This paper presents spatio-temporal multi-modality ontology for indexing and retrieving satellite images in the high level to improve the quality of the system retrieval and to perform semantic in the retrieval process. Our approach is based on three modules: (1) regions and features extraction, (2) ontological indexing and (3) semantic image retrieval. The first module allows extracting regions from the satellite image using the fuzzy c-means (FCM) segmentation algorithm. The second module allows indexing the satellite image by a spatio-temporal ontology which represents sensors, spatial objects, visual features (spectral signature, texture, shape, etc.), spatial relation between objects, temporal variation and semantic relation between terms. The last module allows retrieving satellite images by comparing the ontological model of the requested satellite image with the ontological model base. We adopted several algorithms for measuring the lexical and the structural similarity degree between ontological models. Our approach attempts to fully describe the semantic content of the satellite image and to perform the quality of the retrieval system.

Spatio-temporal multi-modality ontology for indexing and retrieving satellite images

Wassim Messaoudi 1,2 , Imed Riadh Farah 1,2 , karim Saheb Ettabaa1,2 , Henda Ben Ghezala 1 and Basel Solaiman 2

Introduction

With the recent advances in image acquisition and sensor technology, the amount of satellite images is constantly increasing at different levels of temporal, spatial and spectral resolution. They are used in a large variety of applications including, geologic, land cover mapping, agriculture, military, environmental assessment, etc. In order to deal with these data, it's necessary to develop appropriate information systems to efficiently manage these collections. Automatic content extraction, classification and content-based retrieval are highly desired goals in intelligent remote sensing databases. Satellite image retrieval becomes an active area of research for several years. The goal is to create systems capable of interactively retrieving images that are semantically related to the user's query from a database. Several approaches have been developed such as text-based and content-based image retrieval.

Traditional text-based approaches, based on the keyword matching of the text metadata descriptions of images, are usually relatively simple and easy to use, but the retrieval process requires subtle query expressions, and the task of manually annotating the current volume of satellite imagery would involve a large amount of time and expense [START_REF] Ruan | Semantic-Based Image Retrieval in Remote Sensing Archive: An Ontology Approach[END_REF][START_REF] Hyvönen | Ontology-based Image Retrieval[END_REF].

During past few years, Content-based Image Retrieval [START_REF] Ruan | Semantic-Based Image Retrieval in Remote Sensing Archive: An Ontology Approach[END_REF] (CBIR) technique has been proposed to overcome the obstacles of text-based approach methods. This technique uses the visual contents of an image such as color, shape, texture, and spatial layout to index and represent the image [START_REF] Kong | The Study on the Semantic Image Retrieval based on the Personalized Ontology[END_REF]. CBIR system provides an intelligent and automatic solution for efficient searching of images. However, the low-level image features are often too restricted to describe images on a conceptual or semantic level. This is due to the 'semantic gap' between the limited descriptive power of low-level image features and the richness of user semantics [START_REF] Hyvönen | Ontology-based Image Retrieval[END_REF][START_REF] Kong | The Study on the Semantic Image Retrieval based on the Personalized Ontology[END_REF].

Many methods have been developed to reduce the 'semantic gap' between lowlevel features and high-level image semantics such as relevance feedback [START_REF] Kong | The Study on the Semantic Image Retrieval based on the Personalized Ontology[END_REF], using data mining techniques to associate low-level image features with high-level concepts [START_REF] Rui | Image retrieval: current techniques, promising directions, and open issues[END_REF], region based techniques [START_REF] Mezaris | Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback[END_REF] which allow the user to specify a particular region of an image and request that the system retrieve images that contain similar regions, and ontology approach [START_REF] Ruan | Semantic-Based Image Retrieval in Remote Sensing Archive: An Ontology Approach[END_REF][START_REF] Hyvönen | Ontology-based Image Retrieval[END_REF] to describe the semantic content of the satellite image. It allows an explicit, formal, machine readable semantic model that defines the classes (or concepts) and their possible inter-relations specific to some specified domain.

In this paper, we present a spatio-temporal multi-modality ontology-based approach allowing semantic satellite image retrieval and modeling the spatio-temporal content of the satellite image. Our approach is based on three modules: (1) regions and features extraction, (2) ontological indexing and (3) semantic image retrieval.

The proposed spatio-temporal multi-modality ontology represents sensors, spatial objects, visual features (spectral signature, texture, shape, etc.), spatial relation between objects, temporal variation and semantic relation between terms. Our approach attempts to reduce the semantic gap between visual features and semantic concepts and to fully describe the semantic content of an image.

This paper is organized as follows: section 2 gives an overview of preview works on ontology-based image retrieval; section 3 presents an overview of ontology; section 4 describes the proposed approach and discusses their modules; section 5 presents our conclusions.

Related word

In this section, we present some previous works on ontology-based image retrieval. Ontology is designed to capture shared knowledge and overcome the semantic heterogeneity among domains.

In [START_REF] Popescu | SemRetriev -an Ontology Driven Image Retrieval System[END_REF], authors proposed a system which combines the use of an ontology annotating images repository and CBIR techniques. The system incorporates an ontology which is built by extracting the term hierarchy ranging under placental in WordNet [START_REF] Miller | Nouns in WordNet: a Lexical Inheritance System[END_REF] and separates the leaf synsets in this ontology.

In [START_REF] Hu | proc. Of the15th IEEE International Conference on Tools with Artificial Intelligence[END_REF], authors present an ontology-based medical image annotation with description logics for semantics-based reasoning and image retrieval.

In [START_REF] Simou | A Visual Descriptor Ontology for Multimedia Reasoning[END_REF], authors proposed the Visual descriptor ontology which represents the structure of the MPEG-7 visual part. The goal of this ontology is to enable machines to generate and understand visual descriptions which can be used for multimedia reasoning. This ontology contains four main concepts, which are Region, feature, visual descriptor and meta-concept.

In [START_REF] Mezaris | Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback[END_REF] authors proposed an ontology approach to object-based image retrieval. In this system, low-level features describing the color, position, size and shape of the resulting regions are extracted and are automatically mapped to appropriate intermediate-level descriptors forming a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts of the user queries.

In [START_REF] Town | Language-based querying of image collections on the basis of an extensible ontology[END_REF], an image retrieval approach based on an extensible ontology is proposed. Querying is achieved by combining ontological concepts (e.g. size, location, color, semantic category). This combination is constrained by a grammar. Mapping between image data and concepts is based on supervised machine learning techniques.

In [START_REF] El Adnani | A multiple layered functional data model to support multiple representations and interoperability of GIS: application to urban management systems[END_REF], authors proposed a generic multi-layered ontology to describe key features of urban applications. The ontology contains layers which are composed of a generic functional structure and one or more domain ontologies.

In [START_REF] Wang | Image retrieval with a multi-modality ontology[END_REF], Authors investigates the design and use of ontology for multimodal similarity matching and ranking for multimedia retrieval on the Web in the animal domain. A new ranking mechanism is proposed to measure conceptual similarity in the ontology, integrating both textual and image features.

Authors in [START_REF] Hudelot | Fuzzy spatial relation ontology for image interpretation[END_REF] proposed ontology of spatial relation to guide image interpretation. This ontology is enriched by fuzzy representations of concepts, which define their semantics and allow establishing the link between these concepts and the information that can be extracted from images.

Zhou et. al [START_REF] Zhou | Image Retrieval based on object's orientation spatial relationship[END_REF] proposed an approach for computing the orientation spatial similarity between two symbolic objects in an image. Wang [START_REF] Staab | Multimedia Ontology. Summer School in Multimedia Semantics (SSMS2007)[END_REF] proposed a new spatial-relationship representation model called two dimension begin-end boundary string (2D Be-string), based on previous research in 2D String [START_REF] Lee | Using 3D Spatial Relationships for Image Retrieval by XML Annotation[END_REF].

Ontology overview

Ontology describes a particular reality with a specific vocabulary, using a set of hypothesis related to the intentional meaning of the words in this vocabulary.

Gruber [START_REF] Gruber | A translation approach to portable ontologies[END_REF] defines the ontology as "a specification of a conceptualization". The conceptualization is the couching of knowledge about the world in terms of entities (things, the relationships they hold and the constraints between them). The specification is the concrete representation of this conceptualization. One step in this is the encoding of the conceptualization in a knowledge representation language.

The role of ontology is to capture domain knowledge in a generic way and provide a commonly agreed upon understanding of a domain. The common vocabulary of ontology, defining the meaning of terms and relations, is usually organized in taxonomy. Ontology usually contains modeling primitives such as concepts, relations between concepts, and axioms.

In ontology, concepts are the fundamental units for specification, and provide a foundation for information description. In general, each concept has three basic components: terms, attributes and relations. Terms are the names used to refer to a specific concept, and can include a set of synonyms that specify the same concepts. Attributes are features of a concept that describe the concept in more detail. Finally relations are used to represent relationships among different concepts and to provide a general structure to the ontology [START_REF] Gruber | A translation approach to portable ontologies[END_REF]. The goal is to create an agreed vocabulary and semantic structure for exchanging information.

An ontology O is a 4-tuple (C, R, I, A) , where:

C is a set of concepts, R is a set of relations, I is a set of instances, A is a set of axioms

Concept, sub-concept, depth :

Let Θ be the set of concepts, ≤Θ is a partial order between concepts.

∀ (C i ,C j ) € Θ 2 , C i ≤ΘC j means that C i is a sub-concept of C j, ρ(C) is the depth of the concept C in the hierarchy.

Classes of attribute

Let Φ be the set of attribute classes. A is the set of all attributes. For a set of attribute classes α ⊆ Φ, A α ⊆ A is the set of attributes of each class in α.

Values and weight of an attribute

Let a € A α be an attribute of a class in α ⊆ Φ. We define Vc : A α → [ℜ ;ℜ] so that Vc(a) is the range of values for 'a' in the concept C € Θ.

Let ω(a,C)be the weight associated to the attribute 'a' for the concept C.

Ontology levels

Ontologies are classified in four groups, according to their dependence on a specific task or point of view [START_REF] Garino | Formal ontology and information systems[END_REF]:

Top-level ontologies describe very general concepts like space, time, matter, object, event, action, etc., which are independent of a particular problem or domain: it seems therefore reasonable, at least in theory, to have unified top-level ontologies for large communities of users.

Domain ontologies and task ontologies describe, respectively, the vocabulary related to a generic domain (like medecine) or a generic task or activity (like image interpretation), by specializing the terms introduced in the top-level ontology.

Application ontologies describe concepts depending both on a particular domain and task, which are often specializations of both the related ontologies. These concepts often correspond to roles played by domain entities while performing a certain activity.

Proposed approach

We consider a satellite image base and a image query as shown in figure 1; we aim to retrieve the similar satellite images to the query taking into account the quality of the system retrieval, the size of the data base, the speed of the retrieval process, the image semantic, etc.

Fig.1. Satellite Image retrieval

We propose an ontological approach for modeling the spatial content of the satellite image and providing semantic in the system retrieval. The goals of our system are:

• Extraction of the salient regions from the satellite image using a segmentation algorithm. • Extraction of visual features from the extracted regions.

• Index the satellite image by ontological models which describe its semantic content: regions and spatial relation between them, visual descriptors of each region, etc. • Retrieve satellite images in the semantic level by measuring the similarity between ontological models.

Our system (figure 2) works in two modes which are the off-line and the on-line mode. In the first mode, the whole satellite images base are indexed by developing an ontological model base. In the second mode, similar satellite images are retrieved. Our system is based on three modules which are region extraction, ontological modeling and semantic satellite image retrieval.

Satellite image base indexing

The first step in our system is to index each satellite image in the satellite images base. The idea is to develop ontological models for each satellite image in order to constitute an ontological model base.

Regions and features extraction

This step aims to extract salient regions from the satellite image using the Fuzzy C-Means method (FCM) which is proved to be very well appropriate to deal with the satellite images. We obtain a set of homogeneous regions as shown in figure 4. Next, we extract several features from regions such as spectral signature, texture, moments, etc.

Fig.4. Satellite image segmentation

Ontological Modeling

The goal of this step is to index the satellite image by developing ontological models which describe its semantic content, basing on the extracted regions from the satellite images.

In order to integrate both the low level image features and the high level images objects, we propose to develop spatio-temporal multi-modality ontology (figure 5) which describes:

• Sensors • Spatial objects • Spatial organization • Visual features: spectral signature, texture, shape • Shape information as area, perimeter, moments, orientation, etc.

• Spatial relation between objects • Temporal relation between objects • Semantic and lexical relation between terms

Fig.5. Spatio-temporal multi-modality ontology

Satellite scene ontology

This ontology represents spatial objects in the satellite image and their semantic hierarchy [START_REF] Farah | Satellite Image Retrieval Based on Ontology Merging[END_REF] such as land zone, humid zone, river, urban zone, etc. the typical relation between objects is the "is-a" taxonomic relation which describe the semantic hierarchy between them. Each object has a set of attributes such as name, length, area, orientation, etc.

Sensor ontology

This model allows representing types of sensor according to their functioning mode [START_REF] Farah | Satellite Image Retrieval Based on Ontology Merging[END_REF].

Visual features ontology

This ontology allows representing the visual features of objects (regions) in the satellite image such as spectral signature, texture and shape, etc. in the high level. We propose this ontology:

Spatial relations ontology

This ontology allows representing spatial relations between objects in the satellite image. Table 1 shows some topological relations in the RCC-8 [START_REF] Cohn | Qualitative spatial representation and reasoning: An overview[END_REF] calculus. Other types of relation can be cited such as distance relation (the river is near from the forest), position relation (the bridge is on the river) and the direction relation (the forest is at the left of the urban zone). We proposed in [START_REF] Farah | Satellite Image Retrieval Based on Ontology Merging[END_REF] an ontology describing these types of spatial relations (Figure9):

Spatial relation Meaning

Graphical representation

DC(x,y) X disconnected from Y EC(x,

Temporal ontology

This ontology allows representing time between objects in the satellite images. Allen [START_REF] Allen | Maintaining knowledge about temporal intervals[END_REF] introduced an interval-based temporal logic, which considered objects/eventsalonga1D time axis as a set of temporal intervals based on comparative relations. Allen [START_REF] Allen | Maintaining knowledge about temporal intervals[END_REF]

Semantic relation between terms

This ontology allows representing lexical and semantic relation between terms in the spatial ontology using several lexical resources such as WordNet [START_REF] Miller | Nouns in WordNet: a Lexical Inheritance System[END_REF], GeoNames [START_REF]GeoNames Ontology[END_REF], etc. WordNet is a lexical resource about English words, which is often used in natural-language processing and information retrieval application. The core concept in WordNet is the synset which groups words via a number of semantic relations such as hypernyms, hyponyms, holonym, meronym, and lexical relation including antonyms, etc. Genames [START_REF]GeoNames Ontology[END_REF] is a geographical database which provides information about places in the world such as name place, country, geographic coordinates, etc. For example the term River in WordNet has this structure:

Sense: a large natural stream of water etc. Coordinate terms: rivulet, rill, run, runnel, streamlet --(a small stream) etc.

Hypernyms: stream, watercourse--(a natural body of running water flowing on the earth)

Hyponyms: (has instance) Amazon, Amazon River --(a major South American river, etc.

Meronuyms: (has part): waterfall, falls --(a steep descent of the water of a river)

Nebhana River in Geonames: Name: Oued Nebaana,Oued Nebana,Wadi Nabhanah,Wādī Nabhānah, Country: Tunisia, Geographic coordinate: N 35° 57' 0''E 10° 3' 0'' We consider a satellite image which contains a river, an urban zone and a forest. The river is adjacent to the urban zone and the forest, forest and the urban zone are disconnected. The forest has a polygon shape, green color, etc. Figure 11

Semantic retrieval

This step allows retrieving similar satellite images to satellite image query basing on their ontological models: two images are similar if their ontological models are similar. To retrieve similar images, we compare the ontological model of the satellite image query with each ontological model in the database. Therefore, we need to measure similarity degree between ontological models.

The measures for similarity computation can be divided into two general groups [START_REF] Hariri | A New Structural Similarity Measure for Ontology Alignment[END_REF] including lexical measures which compare entity labels, and structural measure which compare taxonomic hierarchy. Several approaches have been developed such as ASCO [START_REF] Bach | On ontology matching problems (for building a corporate semantic web in a multi-communities organization[END_REF], Anchor-Prompt [START_REF] Noy | The prompt suite: Interactive tools for ontology merging and mapping[END_REF], OLA [START_REF] Euzenat | An integrative proximity measure for ontology alignment[END_REF], S-Match [START_REF] Giunchiglia | S-Match: an algorithm and an implementation of semantic matching[END_REF], Glue [START_REF] Doan | Learning to map between ontologies on the semantic web[END_REF], etc.

In our system, we choose to compare ontological models using ASCO algorithm which measure the linguistic and structural similarity. This algorithm identifies the pairs of corresponding elements in two different ontologies. These pairs may be pairs of concepts (classes) or relation in the two ontologies.

The matching process of ASCO is composed of several phases. The linguistic phase applies linguistic processing techniques, and uses string comparison metrics, and lexical databases to compute the similarity of two concepts or two relations. The computed linguistic similarities are input for the structural phase. In this phase, ASCO tries to exploit the structure of ontology taxonomy for modifying or asserting the similarity of two concepts or relations.

The similarities of classes or of relations are iteratively propagated to their neighbors in the tree of ontology which is built from the hierarchy of classes and the hierarchy of relations. When the propagation terminates, if the similarities between classes or relations exceed a threshold, they are considered as similar.

After applying this algorithm, we arrange the computed similarity degree of ontological models. Then, the system displays satellite images corresponding to these ontological models.

Conclusion

In this paper, we presented spatio-temporal multi-modality ontology for modeling and retrieving satellite images in the high level to improve the quality of the system retrieval and to perform semantic in the retrieval process.

Our system works in two modes which are the off-line and the on-line mode. In the first mode, the whole satellite images base are indexed by developing an ontological models base. In the second mode, similar satellite images are retrieved. The system is based on three modules which are region extraction, ontological modeling and semantic satellite image retrieval. Our ontology allows indexing the satellite image by a spatio-temporal ontology which represents sensors, spatial objects, visual features (spectral signature, texture, shape, etc.), spatial relation between objects, temporal variation and semantic relation between terms.

Our approach attempts to reduce the semantic gap between visual features and semantic concepts and to provide an automatic solution for efficient satellite image retrieval.

Fig. 3 .Fig. 2 .

 32 Fig.3. Satellite Image base Indexing

Fig. 6 .

 6 Fig.6. Spatial Objects Ontology

Fig. 8 .

 8 Fig.8. Visual feature OntologyEach concept in the visual feature ontology contains the low information value. For example, the forest object has green color, circular form and area of 30 cm².

Tab. 1 .

 1 ,y) X tangential proper part of Y NTPP(x,y) X has tangential proper part Y RCC-8 Topological spatial relation

Fig. 9 .

 9 Fig.9. Spatial Relation OntologyFor example, the forest zone is adjacent to the Madjerda River and it's at the left of the urban zone.

Fig. 10 .

 10 Fig.10. Allen's temporal Ontology We choose to adopt Allen's approaches to model the temporal relation between objects.

  shows the ontological model of this scene.

		Scene				Spatial relation
	Terrestrial zone	Humid zone		Topologic relation	Direction relation
	Construction	Forest	River	Adjacency	Disjunction	At the left
				relation	relation
	Urban zone			Adjacent-Med-Jen
				Medjerda
			Forest-
			Jendouba		Adjacent-
		Jendouba		Med-For
						Disjoint-
						For-Jen
						Left-For-Jen
					Object
	! "		" #$
	…		%&	Spectral signature	Shape descriptor
			HasColor	Green	Geometric Form	Spatial property
			HasShape Polygon	Polygon,	Area
		HasArea 50
	Medjerda	River,Oued	Medjerda,Oueda	Medjerda,Wad		Is-a Relation
	Majardah,Wad Mejerda,Wadi Majardah,Wād Majardah,..Tunisia,		Instance
	N 37° 7' 0''E 10° 13' 0''				Has property
		Fig.10. Ontological Model of Jendouba Scene

Laboratoire de Recherche en Informatique Arabisée et Documentique Intégrée (RIADI) Ecole Nationale des Sciences de l'Informatique, Campus Universitaire de La Manouba,

Manouba, Tunisie wassim.messoudi@riadi.rnu.tn, riadh.farah@ensi.rnu.tn, henda.bg@cck.rnu.tn


Jérôme Denis
email: jerome.denis@telecom-paristech.fr

Samuel Goëta
email: samuel.goeta@telecom-paristech.fr

Neil Postman

Exploration, Extraction and 'Rawification' The Shaping of Transparency in the Back Rooms of Open Data

With the advent of open data initiatives, raw data has been staged as a crucial element of government transparency. If the consequences of such data-driven transparency have already been discussed, we still don't know much about its back rooms. What does it mean for an administration to open its data? Following information infrastructure studies, this communication aims to question the modes of existence of raw data in administrations. Drawing on an ethnography of open government data projects in several French administrations, it shows that data are not ready-at-hand resources. Indeed, three kinds of operations are conducted that progressively instantiate open data. The first one is exploration. Where are, and what are, the data within the institution are tough questions, the response to which entails organizational and technical inquiries. The second one is extraction. Data are encapsulated in databases and its release implies a sometimes complex disarticulation process. The third kind of operations is 'rawification'. It consists in a series of tasks that transforms what used to be indexical professional data into raw data. To become opened, data are (re)formatted, cleaned, ungrounded. Though largely invisible, these operations foreground specific 'frictions' that emerge during the sociotechnical shaping of transparency, even before data publication and reuses.

Yet in the field of science studies, we have in general focused attention on what scientists do with data, rather than on the mode of data production and storage (Bowker, 2000, p. 661).

Transparency in action and the production of open government data

For some years now, public administrations "raw data" have entered politics. As it is presented as an essential means for a renewed transparency, an instrument for a new institutional accountability, its diffusion to the widest audience has become a crucial stake for policy makers around the world. Open government data indeed has been at the center of several transparency policies and has been established as a legal obligation in many countries1 . This led to the creation of many web portals in which one can find extremely various datasets, disseminated by governments, municipalities, institutions and some corporations.

Several studies analyzed open government data policies as a new step towards the generalized transparency that emerges in many fields, feeding an "audit culture" [START_REF] Power | The Audit Society: Rituals of Verification[END_REF][START_REF] Strathern | The Tyranny of Transparency[END_REF]. In this perspective, the opening of public data and the creation of devices that encourage its reuse both the State, the shape of which becomes unstable, and the citizens, who are positioned as "data publics" [START_REF] Ruppert | Doing the Transparent State : open government data as performance indicators[END_REF]. Because it extends the field of accountability, open data can be considered as a flagship tool of a new form of governmentality, which shapes the figure of an informed citizen [START_REF] Barry | Political Machines. Governing a technological society[END_REF]).

Yet, how open government data actually circulate, and moreover how the data are concretely "opened" remain largely overlooked. The installation of open government data portals, which provide real infrastructures dedicated to the dissemination of data, required important technical and organizational transformations. Within administrations, teams have been created, departments redesigned. What is the nature of these transformations that occurred in the back rooms of open data? What does it takes for the data to actually be "opened"? Who are the invisible workers that handle them and take care of them (Denis, Pontille, 2012)?

To answer these questions, it is useful to step back to what happened in some sciences, in which publishing and exchanging data have become crucial matters. Indeed, data collection and diffusion have been progressively decoupled from the publication of results in many scientific disciplines, becoming a kind of scientific production of its own [START_REF] Bowker | Biodiversity Datadiversity[END_REF]. This new emphasis on data was accompanied by major shifts in the organization of scientific work [START_REF] Wouters | Big science data policies[END_REF][START_REF] Hine | Databases as Scientific Instruments and Their Role in the Ordering of Scientific Work[END_REF][START_REF] Edwards | A Vast Machine[END_REF]. Following the first Information Infrastructures studies, we think it is useful to bring to the light such concrete issues in the production of open data, insisting notably on the frictions which occur when producing and exchanging data [START_REF] Edwards | A Vast Machine[END_REF][START_REF] Edwards | Science Friction: Data, Metadata, and Collaboration[END_REF], the political aspects of databases management [START_REF] Bowker | Sorting Things Out: Classification and Its Consequences[END_REF] and the necessity to respecify the vocabulary of "raw data" [START_REF] Gitelman | Raw Data" is an Oxymoron[END_REF]. This is a way to understand how transparency is actually shaped in public administrations, even before its diffusion and its use.

The aim of this communication is thus to bring to light the sociotechnical "thickness" of open government data, and to understand the political, technical and organizational issues carried by the transparency it performs. For that purpose, we will build on an ethnographic enquiry made of direct observations, in-depth interviews and documents analysis in several French administrations involved in open government data projects.

Here, we present three crucial steps that punctuate work in the back rooms of open data: identification, extraction and 'rawification'. These three steps show that data is not an available commodity within administrations, which would be naturally ready for its public diffusion, mechanically producing transparency. The data very existence is far from being obvious, and instead of being an intrinsic property, "rawness" is the outcome of numerous operations.

Exploration

Unlike scientific practices, which are entirely turned into the production of data, administrations rarely consider data as a self-evident matter. Not only the perimeter of the data that are potentially candidates for opening is debated and shifts for each open government data project, but the very knowledge about their existence, their nature and their location is equally problematic. Before asking the question "how are we going to open this dataset or this one?", the persons in charge of implementing an open government data program are facing an even more abysmal interrogation "what data do we possess?"

In the process of opening public data, identification is thus a crucial and complex operation, which rarely summarizes to the more or less fastidious gathering of well-defined entities that one should simply chase within the organization. Even if some people dream of an exhaustive inventory of all the data that are present in an administration, the gathering actually occurs as an uncertain exploration during which data identification is progressively settled, through interactions with internal services.

When we started talking with them about open data, ideas of data sprung out from services as well as from our side as we already thought a bit about the question […] For example, when we met the office of services to the local population, we already knew what would be easy for them to open and that it would be interesting to have data regarding names of children born in the city. It is something that had already been done in other cities, so we knew it could be interesting to release them too. We also were interested in data regarding elections, because this is something that the city possesses precisely, such as figures per poll stations, etc. And this is something I think was not really done elsewhere […] So it is true that we came to them saying "this, this is what is truly interesting" but it was overall an exchange. The same with service X, we came asking them "what is possible for you to open initially?" We went step by step because everything was not simple to do. (An open data project manager in an intercommunality)

Thus, data inventory is by no means mechanical. It implies meetings and discussions, sometimes negotiations, which are linked to various matters. Among these, the degree of sensitivity of data, that is the risk its opening represents, is often crucial. The concern for transparency might encounter detrimental consequences led by the publication of certain data. It is particularly the case of data concerning infrastructures as explained by a civil servant in charge of roads in a French city:

We know there are some folks with disreputable jobs who will shut down public lighting on some street lamps by fumbling into the cables and will get rid of the right cables so that they can do their business quietly, without having too much light to disrupt them. So if we were to open [data on] cabling, they could know where the juice for each electrical cabinet would come from and where it lights, they would break this electrical cabinet and have a whole neighborhood in the dark to do business quietly. Well, it's a bit far-fetched but well. The risk exists and well, there is not really much interest to liberate this data. (A database manager on roads)

Thus, neither data are obviously available in the services, nor is it selected on simple criteria, which would be defined in advance. Through its progressive and collective exploration, data is co-elaborated. Identification itself is thus a crucial part of the opening process.

Two aspects are important to understand what is at stake with this exploration step. First, it has organizational consequences. These are not only specific types of data that are identified. Places within the organization are also designated in this process, as well as persons, who are set up as responsible of these data and their circulation. Second, as a process, data identification is more a matter of instantiation than it is of genuine designation. This identification process is generative. It engenders a certain reality [START_REF] Law | Seeing Like a Survey[END_REF], a perimeter of data that is not only established as open ("openable", in the first place) but also as "data" at all.

Extraction

Once the data candidate to opening are identified, one still have to "grasp" them, which is, again, not self-evident. The data do not become available by their mere identification: they remain encapsulated in databases and their release requires them to be extracted from the software that makes them visible to their users.

Relational databases provide dedicated professional interfaces, called "user views" [START_REF] Codd | A Relational Model of Data for Large Shared Data Banks[END_REF]. Aimed at simplifying the use of the database, these multiple views allow a variety of uses [START_REF] Dagiral | Voir pour savoir. Concevoir et partager des « vues » à travers une base de données médicales[END_REF], while preventing from accessing to the physical organization of the data [START_REF] Castelle | Relational and Non-Relational Models in the Entextualization of Bureaucracy[END_REF]). The users are thus dependant on these views and rare are the databases equipped with an automatic extraction feature, which would allow gathering the data independently of the software interface. To allow this extraction, people must delve beyond visualization interfaces, in the guts of hard drives, at the roots of databases.

In fact, you have to understand that what is complex is that, at the beginning most of the systems and softwares we bought here, they are absolutely not conceived for doing open data. So it is complicated. We must, ourselves, develop small tools [moulinettes], all sorts of things that allow us to bring out data properly. (A transport database manager)

Furthermore, even if broad principles can be found in the way data is actually stocked on hard drives, this "physical view" is always specific. Extraction tools have to be custom made for each database software, and sometimes each version of each software.

What you have to tell yourself is that nothing is universal in that stuff. That is the way you sort your data is like the way you sort your socks at home, everyone can sort them in a different manner. We all have the same drawer but we all sort them differently. (Database manager).

Therefore, to implement an open government data policy, one must be capable, once data identified, to bypass the databases one way or another and to harvest the data directly from their storage space. Such extraction work performs the second step of the progressive instantiation of data. It shows again that raw data is not naturally available in administrations, and that transparency does not rhyme with immediacy.

This point is essential to question the idea that public data would be sleeping resources, which only has to be "freed" in order to be exploited. This vision of data as a "commodity" (Ribes et Jackson 2013) is jeopardized not only by the cost it represents for extracting but also by the ambiguity of what the technical providers deliver to institutions by providing them their databases software. These providers own the database paths and the storage systems of their databases: inaccessibility of data is at the core of their business model. A large part of extraction work therefore consists for institutions to regain control of data by disarticulating the sociotechnical assemblages that tie data to some private companies.

"Rawification"

We know that the term "raw data" is ambiguous and that, once we look closely at the practices it is associated to, it can be likened to an oxymoron [START_REF] Bowker | Biodiversity Datadiversity[END_REF][START_REF] Gitelman | Raw Data" is an Oxymoron[END_REF]).

It is precisely the case of open government data projects which analysis makes visible the series of transformations data are subject to so that they become raw. We will comment here three operations which take part to what some open data people call "rawification" work: reformating, cleaning and ungrounding.

Extracting databases does rarely suffice to obtain data that are ready to be opened. Their opening must come from formatting, which ensures they are readable by the most common tools used by developers, but also by the general public. Obviously, this very idea of a 'suitable' format hides a complexity we will not unfold here. During our enquiry, CSV (comma-separated values) was the most frequent standard, chosen notably because it is an open format allowing its use by every spreadsheet programs. We heard during a meeting starting an open government data project this phrase, which epitomizes how important is formatting in the process of producing open data: "to me raw is CSV." However, translating a dataset into CSV is nothing but self-evident. Each export from a proprietary format from original databases or a spreadsheet software to its desired format leads to a series of problems and requires adjustments which ensure that initial data are not corrupted in the process.

Before or after being formatted, data are subject to another type of treatment: they are "cleaned"2 . In open government data programs, cleaning concerns several aspects. It first consists in correcting mistakes within the datasets: values that are considered as abnormal, and "holes" in files (blank values). Cleaning also means harmonizing data. As we have seen, databases, and so datasets, appear in different formats and versions within the same institution. They are generally manipulated by departments that produce and deal with data in their specific way. Thus entities a priori identical can appear in the databases with different units, even different identifiers. As in the case for sharing scientific data on a large scale [START_REF] Baker | Infrastructuring Ecology: Challenges in Achieving Data Sharing[END_REF], producing open datasets involves bridging these gaps and building coherence between differences and redundancies within multiple datasets.

Typically, on the elections dataset: between the files from the previous elections and the old stuff (we went back until 2004), files were not presented the same way. It was very stupid things but sometimes the column name was ever the name of the candidate or sometimes the name of the party or both, so I try to standardize all of this so that all files look similar and are structured the same. (An open data project manager in an inter-communal structure)

This is an important aspect of open data. Open government data programs literally challenge some data, which if it was published as it was, would be perceived of poor quality, even though their users within the organization have nothing to say against it. This is a widely discussed issue in STS and beyond, about "bad records" [START_REF] Garfinkel | Studies in ethnomethodology[END_REF] and "false numbers" [START_REF] Lampland | False numbers as formalizing practices[END_REF]. In organizations, professional data is not accurate or true in itself. The low degree of its precision or its lack of harmonization have no impact on its efficiency, sometimes the opposite. are many "good organizational reasons", in the words of Garfinkel, that this kinds of data persist, simply because their accuracy and even their "truth" are grounded in the practices of those who manipulate and mobilize them. It is the confrontation between practices with specific issues that might lead to stigmatize such or such data as false or bad. In that sense, open government data programs can be considered as tests. By migrating data to a new framework, they potentially foreground issues that were not much relevant in the initial context of its use. Absences that were never noticed become negligence, approximations or duplicates without importance become mistakes or redundancies. Cleaning data is the cost to pay to avoid these issues and to transport data from one framework to another. The idea of cleaning emphasizes another essential aspect of the framework towards which data is meant to migrate. As it is cleaned, data becomes generic, cleared from the scoria resulting of the situated activities it used to feed. Cleaning is a first step towards the universality which open data promotes. The deletion of ambiguities and blanks produce datasets suitable to virtually any usage.

Such a projection of data towards universal uses is fed by another operation, very close from cleaning: ungrounding. Besides producing data for which nothing can be reproached in terms of coherence and completeness, rawification work involves erasing the traces of previous uses.

This Excel file, they worked on it. I have to say that their process is a bit complicated. Basically, their software delivers figures and they annotate it in a file to establish their global statistics. So it really was their working document. However, that is not what we wanted. What we wanted are the rawest data, which is no comments, no charts, no formatting, really the day-to-day data, statistics. (An open data project manager in an inter-communal structure)

Ungrounded, data is delivered from the marks of the practices it initially was tied to. This operation constitutes another dimension in data instantiation. Once more, the scope and the very definition of data is specified while components such as colors, comments and sometimes sub-categories are sidelined, being performed as non-data. Thus, following the cleaning of data, ungrounding practices achieve a process of purification by which data progressively becomes "raw". It enacts an essential dimension of the fabric of transparency: data intelligibility.

Therefore, to become open, data has to be raw, but to be raw, most of the data have to be "rawified", that is formatted, clean and ungrounded. These qualities are not intrinsic: they imply complex sociotechnical work which itself requires, as in the scientific collaboration projects, that skills and positions in the division of labor are being invented [START_REF] Baker | Infrastructuring Ecology: Challenges in Achieving Data Sharing[END_REF].

Such operations may have even more important consequences on the organization itself. In some administrations, the steps that we just described are indeed sometimes the occasion to rethink some of the organizational processes, in order to reduce downstream work on data. Integrating some of "rawification" work is generally presented as a means to modernize and rationalize administrations, which is not without raising crucial issues. Placing formatting, cleaning and ungrounding operations upstream is a way to force people to work with generic data, losing the quality of their grounding. In other words, in this configuration, transparency is not thought as the result of dedicated operations, but as a unique horizon shared by every activities in administrations, independently from the specificities of jobs and data. This inversion assumes the risk of installing the same situations Garfinkel once described (1967), where tensions between hardly compatible frameworks of meaning tend to parasite professional practices.

Conclusion

In this communication, we showed that, conversely to what certain injunctions legal dispositions imply, the government data are not "already there" entities, which would be predisposed to mechanic openness. The ethnography of concrete activities on which open government data programs rely shows that data is progressively instantiated as open data.

We have insisted on three crucial steps of this instantiation -exploration, extraction and "rawification", which underline the sociotechnical operations data is subject to. In sciences, these transformations have been studied as means of mutating raw data into refined certified data, ready to be handled in specialized activities [START_REF] Edwards | climate science, uncertainty and politics: Data-laden models, model-filtered data[END_REF][START_REF] Walford | Transforming Data: An Ethnography of Scientific Data from the Brazilian Amazon[END_REF]). In the case of open data, the process is somehow reversed: it involves transforming sets of professional data, which already had a long social life, into raw data, open to many uses. Therefore, raw data is not a starting point here, but the result of several "delocalization" operations.

To conclude, we may get back to the vocabulary of raw data. Once we highlighted the operations that take part in the shaping of such raw data, should we consider that it does not exist "in reality" and that the mere notion of raw data is a fiction, even an illusion?

Undoubtedly not, unless we seek to impose a definition of raw data that is eventually not shared with the persons concerned. Instead, we think raw data should be understood as an oxymoron [START_REF] Bowker | Biodiversity Datadiversity[END_REF][START_REF] Gitelman | Raw Data" is an Oxymoron[END_REF])… but an oxymoron that the data workers who shape transparency on daily basis have to deal with. This is a way to surface invisible work involved in raw data and to highlight the tensions both data and their workers encounter during the series of transformations that lead to raw data, the cost of which is never completely measured in advance.

The claim to diffuse raw data was formulated at the Sebastopol meeting in

2007, which laid down the foundations of open data. Among the principles, the second underlines the importance to access data without treatment called "primary". « Data Must Be Primary: Data are published as collected at the source, with the finest possible level of granularity, not in aggregate or modified forms. » ("8 Principles of Open Government Data". http://www.opengovdata.org/home/8principles). The Lough Erne G8 held in 2013 led to the signature of a charter, which stipulates that open data will be the default practice of administrations, an obligation combined with detailed action plans in signatory countries.

The vocabulary of cleaning, associated with the idea of quality data, is widespread in scientific fields.It was recently subjected to an in-depth ethnographic work on the case of scientific work in the Amazon rainforest[START_REF] Walford | Transforming Data: An Ethnography of Scientific Data from the Brazilian Amazon[END_REF]