OpenData, OpenGov and Italian Spaghetti (part 2)

You must hear the roar of the battle! She wrote Michel Foucault

The declination of transparency has its exceptions, you know. Other than rumors!

Online data, data only available via the web and we can use CMS in web design. Just assert: An Insight into CMS can help you to know how to create a Pro web… without reflecting on the eminent issue that is the basis of transparency and is not look, but find! But we are still grappling with spaghettiopendata that I know so much about SPAGHETTI WITH clamsA’mmare (ie fake spaghetti vongole, ie no clams!): Spaghetti without OpenData! Because? Because sooner or later the accounts with the original request will have to do it! Open Data Signori! Not given to the need and in all sauces!

The spaghettiOpenData line just can not … digest it!

Types of data and information each one says about it. What matter !, In fact then the “open date” umbrella is always open! Beware! Be careful not to shield it with what is not open date, and more so about what is only open but not connected with the rest of the data crammed into other databases, making the information (the data at first instance) not completely clear, contenting us half trying to look but not find!

Give, these strangers, but not too! Here is the encyclopedia of the mirrors of the various data possibilities.

given legally open (opendefinition.org): so it is allowed to reuse anybody’s data and without any restriction, even of a commercial type, that is without copyright, that can be used for future jobs

technically open : machine readable not only available via APIs

Open Gov data : issued by gnaristic appminations, not those related to the subject matter of which they are quantitative expression

data socially open : data that can be found in the context of the context and on which there is exchange of jokes, impressions and evaluations among those who re-use the data themselves with any feedback.

public data (Italian terms): given by public entities because they are collected or used by public entities within their institutional ends. Public entities, by virtue of their institutional duties, collect and handle large amounts of information encoded in data, on citizens, businesses, institutions, territory and the main phenomena of the country’s life. The knowledge of such data is made up of a set of rules governing the usability of the subjects concerned by the public entities that collect and treat them. Consciousness involves two fundamental data quality: security, understood as a set of measures aimed at providing access to only data known to a subject, and usability,
A known knowledge can be made known to the subject concerned by three modes of exchange: access, communication, dissemination. Access allows the subject concerned to directly access the data; communication is to send the data to one or more authorized and predetermined recipients; the dissemination is to make the data available to an indefinite number of subjects, including through their publication, in the traditional form or on the internet. The growing availability of digital data and the spread of Internet technologies have led to a review of the problem of awareness, due to the increased accessibility, the lack of territorial limits and the reduction in cost. New technologies, however, while favoring the usability of data,

The evolution of technologies offers many modes of representation for data. Data, both when stored and processed using traditional techniques, and, as always assumed in the following, when represented in such a way as to be processed directly with digital computers, take different forms:
1. structured data having acceptable format in the fields and archives of a database;
2. documents, ie unstructured data, characterized by being expressed in natural language.

The term public data can be interpreted in different meanings:
– publicly accessible data: this definition refers to the absence of confidentiality requirements, and therefore reflects an aspect related to the legitimacy of consultation by interested parties in any case. It is therefore applicable to a subset of data held by public entities (for example: laws);
– data held by a public entity: refers to the public nature of the data controller (for example: personal data of individuals and companies) which may also be the producer. This is the concept adopted by the European Commission’s ‘Green Paper on Public Sector Information in the Information Society’;
– data of interest of a public entity: refers to the public nature of the user in the interest of the community and relates to information which, even if held by a private individual, must be detained in conditions made accessible to public entities for the pursuit of institutional ends (for example, tracking data for telephone contacts).

Public data can be classified according to different features. Here are some of these:

1. Identification: Data may be referenced to individuals, individuals or legal entities, identified or identifiable, in which case personal data (sensitive data or not) or are not attributable to individual natural or legal persons, in which case data anonymous.

2. presence in public or similar registers: In some cases the data comes from public registers, lists, documents or documents that can be accessed by anyone, without any conditions. They are held or formed by one or more public entities, under a law or regulation; the norm that is the basis of know-how in these cases can provide for particular access modalities or time constraints that must be respected even in case of communication or dissemination of data. The only fact of finding personal data, such as the e-mail address, in a public Internet space, does not make it known to anyone and therefore does not authorize the free use of the data.

3. aggregation and generalization: The data are said elementary when they represent an aspect of reality that can not be rethought, given the hypotheses adopted, to simpler aspects; statistics when they are the result of processing through aggregation functions on elementary data. An example of elementary data is the age of a person; Examples of statistical data are the number of subjects with that age in a specific territory and the average age of the population of a municipality.
According to a different process of abstraction, usually termed generalization, the metadata and the data patterns are distinguished. Metadata is a property of a data set. A data schema consists of describing a set of data classes and relationships between them. Eg:
– a picture of a timetable is a set of data;
– the property for which arrival and departure times are represented by two digits per hour and two digits per minute is a metadata;
– knowledge of the relationship between trains and cities connected by trains, through the time of arrival and departure of trains to or from these cities respectively, is the schema of the data of the framework

4. degree of processing: a) raw data: data collected but not yet subject to significant processing and therefore found essentially in the form in which they were acquired; b) Basic data: Data already processed to make it possible to process them outside a single system or single technology, usually by subjects other than the one that collected them, for example:
– standardized postal addresses; – names of professionals enrolled in a record; c) enriched data (or processed): data resulting from search and comparison operations with information of different origin but related to the same phenomenon. Examples of enriched data are: – the tax position of an enterprise as shown by various databases of the Ministry of the Economy and Finance; – the data contained in the family status of a citizen; – consumer price index linked to a city; – a map obtained from raw photogrammetric data.

5. Benefits for interested parties to access them: Essential data are public data that citizens, businesses and other private operators must be able to exercise in order to exercise their rights. Essential data can be both anonymous, like standards, and many statistical data, as well as personal (most administrative data). Examples of essential data: 1. existing laws and regulations; 2. the most important or necessary national statistical data for individual or collective decisions; 3. the personal data held by the public and concerning the applicant; 4. the indications needed to take advantage of the services provided by public entities and to verify the status of the administration (portals and unidirectional access points to services, organization charts, postal addresses, telephone numbers, etc.).

There are four distinct situations as far as the knowledge of data held by public subjects is concerned: 1. data known to anyone; 2. give limited confidences to certain public subjects, or to some professional categories or other categories of subjects; 3. data known under Article 22 of Law no. 241/1990 by those who have a personal and concrete interest and for the protection of legally relevant situations; 4. data known to the sole public entity that holds them, covered by secretarial or statistical confidentiality or subject to special protection by law no. 675/99.

Data quality is a feature that users want. Connected to the know-how are two qualities: security and usability. Additionally, it is important that the data are up to date, that is accurate, that is, they correspond correctly (if possible) to the observed phenomenon, or at least accurate, that is, they are a good approximation.

But while it is possible to determine whether the data are legally open and technically open in an extremely simple way, how can one determine the socially open level of a given data? Agreed that the data should only be available by everyone, but putting the data on a public or local public sector website is not enough, then it is useless if the data is not simply found. Because of this, it is necessary to make use of public registers, government data catalogs, optimized search engines with high degree of indexing, semantic management, even from metadata, from microformats, to the level of linked open data.

Large databases managed by public entities, often national coverage, make it increasingly easy to produce new data from existing ones by simple elaboration. Such operations can be carried out for institutional purposes within a single subject, or at least partly exploiting data from other subjects. And then what happens? Do these data serve if they are not re-usable by everyone?

Do not call them open if they are not! We say rather that here in Italy the battle for transparency is carried on, but if like a zibaldone we collect all the data, let’s not run behind the open battle given simply because this is the trend of the moment with good pace of the followers hunters !!

Diffused always by those who make a principle or a law simply a flag .. because you know: the flags follow the wind!

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