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Écrit par PETAZZI Catherine, Gestionnaire PC2, PC3, Masters Vendredi, 06 Mars 2015 08:37 La répartition des étudiants pour les examens du C2i - 2ème semestre 2014-2015 est affichée. Écrit par PETAZZI Catherine, Gestionnaire PC2, PC3, Masters Mercredi, 18 Février 2015 11:48 Demande de transfert (départ et arrivée) : dépôt au bureau de la scolarité : du 2 mars au 20 avril 2015 inclus- Dossier "départ" : lettre de motivation et pièces justificatives pouvant appuyer la demande ainsi que l'accord écrit de l'université d'accueil- Dossier "arrivée" : lettre de motivation accord écrit de l'université d'origine en vue de l'inscription à l'université de bourgogne Écrit par PETAZZI Catherine, Gestionnaire PC2, PC3, Masters Vendredi, 23 Janvier 2015 10:54 En raison des examens de l'UFR Pharmacie, le cours du vendredi 23 janvier 2015 est reporté au vendredi 20 mars 2015, même heure, même salle. Écrit par PETAZZI Catherine, Gestionnaire PC2, PC3, Masters Lundi, 12 Janvier 2015 08:43 Le cours de l'UE 12 Hormonologie et reproductiton du mercredi 14 janvier 2015 se déroulera à 16h comme prévu initialement Mise à jour le Jeudi, 08 Janvier 2015 16:33 Écrit par PETAZZI Catherine, cialis www.cialisgeneriquefr24.com Gestionnaire PC2, PC3, Masters Jeudi, 08 Janvier 2015 16:28 Le cours de l'UE "revêtement cutané" du mercredi 14 janvier 2015 de 14h à 16h est annulé et reporté au vendredi 16 janvier de 17h à 19h - Amphi courtois Mise à jour le Mardi, 16 Décembre 2014 16:26 Écrit par Lena PERTUY, PC2, PC3, Masters Mardi, 16 Décembre 2014 16:21 Au vu de l'imbroglio causé par la diffusion des notes des contrôles continus, les règles concernant tant les réclamations que la notation sont clairement établies dans le document ci-dessous. Mercredi de 13h30 à 17h00.

What is “data”?

By Grzegorz Młynarski

The most popular definition of data describes it as everything which is or may be processed by a brain or a computer.

This “processability” – in other words, usability after modification – is a sine qua non of data. We may sort data basing on variety of criteria, such as its content, set size, legal classification, or even the way we share it. Nowadays the most commonly used categories are open data, public data and so-called “Big Data”.

The term “data” may be ascribed to any words characterizing any item or phenomenon, to any numbers detailing parameters or quantity of the item and/or phenomenon, to any images presenting them, to any graphs illustrating their interdependence, any audio or video recording of them, and so on. The same phenomenon may be characterized by different sets of data, which, when described, presented and interpreted in a given way and in a given context, will provide information on the phenomenon they present.

Data concerning many (often similar) items with different parameters is collected in a database. This helps to store data in a sustainable format. Modified versions of the same file are archived and saved in a database, which makes it possible to recover data in case of a system breakdown or a breach of security. Databases are used in everyday life in data managing, product cataloguing in stores or public opinion surveys in pre-election period. Every database is managed with so-called datasheets (spec sheets) which store data organized according to a specific criterion. If the right data is compared and presented with appropriate software and filters, it is possible to draw some conclusions about objects described and to analyze data – in other words, process it in such a way that it may be understood and used further. We may classify databases either based on their structure type (relational, hierarchic, network, object-oriented) or on the way they are managed (e.g. operational or analytical).

The popularity of databases is due to the direct, easy, real-time access to information they provide. It is accessed by formulating proper queries, possibly by many users at once. For example, a popular Polish Internet site jakdojade.pl, a public-transport planner, enables hundreds of users querying about exactly the same or at least very similar routes to draw data from the same base in the same second. Analyzing this example we may notice that, thanks to users’ interface, a database – even a big one – may also be interactive. In other words, it may provide an answer to a query and model the content as well as the format of the answer based on the users’ choices. Another good example here is Google search engine. Popular, almost identical queries are simultaneously sent to the engine by many users at once, which makes the engine return the same or very similar results. A term related to that phenomenon is the afore mentioned Big Data. We use this term to talk about any aspect of analysis of a big, heterogeneous and dynamic collection of data. Big Data revolutionized ICT industry with so called “4Vs”: volume, variety, velocity and value. Google’s search engine’s database, which indexes Internet websites from all around the world, is over one hundred million gigabytes big – which explains the “big” in “Big Data” and the “volume” in “4Vs”. When a user enters a query, powerful Google calculators scan the database to find the most relevant answers: the engine analyzes various data: images, text files, numbers, maps, films, etc. – hence the “variety”. Everything happens in real-time. What’s more, indexing computers never stop modifying and supplementing the database; and to top it all, before displaying the answer, the engine’s script analyzes the user’s location and their previous queries – which is why the same query by two different users in different parts of the world entered on the same day but at different times may bring completely different results. This quality helps to imagine how valuable big databases may be for society and for business: users may rely on customized results and working on a set of data which is useful for them and their strategies; entrepreneurs may cherish from better communication with their clients and model their offer to their needs.

To arrange data we need to: determine the data’s category (lexical, numerical, etc.); put it in tables and develop systems for its automatic collection and insertion; collect it; catalogue and archive it; select, process and analyze it; distribute and publish it. A good example of how this works in practice is the DATA.GOV.UK (http://data.gov.uk) portal. This government project, which uses public data, was developed in order to help people understand how the country works and what is the general direction of the current political agenda. Data, shared in a clear and user-friendly way, becomes raw material which produces an economic and social value added. Over 9000 databases, accessible from all central administration departments and from other public sector institutes, as well as local administration offices may provide citizens information they need, whatever it may be. Data.gov.uk. allows you to access raw data, which may later be used for developing useful applications. This helps the society solve specific problems and enables it to monitor the efficacy of political reforms.

Further reading:

1. Adam Maria Gadomski, Global TOGA Meta-Theory: http://erg4146.casaccia.enea.it/wwwerg26701/gad-dict.htm

2. D. Eaves, Find, Play, Share: http://eaves.ca/2009/09/30/three-law-of-open-government-data/

Grzegorz Młynarski – a sociologist and a communication designer. He runs a workshop of urban transformations called “Sociopolis”. Author of a book “Open government in Poland. The backstage of Opengov program” (available in Polish under the title “Otwarty rząd w Polsce. Kulisy programu Opengov”). He organizes workshops and consultations about implementing innovation, develops strategies of change management and conducts training sessions of social responsibility of business for firms.

Photo in this article was published by JD HANCOCK  on FLICKR under a CC BY 2.0 License