RU2763458C1 - Method for entering and multilevel verification of intensively supplied data in large scientometric information and analytical systems - Google Patents

Abstract

FIELD: information technology.

SUBSTANCE: computer-implemented method for entering and multilevel verification of large scientometric data into an interactive information and analytical system comprises the stages of: unloading the primary data on the results of scientific and technical, development and design, and innovation and technological activities; conducting automatic fuzzy search for duplicates in the preset primary data; conducting automated comparison of the authors of the results of scientific and technical, development design, and innovation and technological activities with the existing authors; affiliating the primary data with the existing objects; saving the primary data and creating a new object in an interactive information and analytical system; automatically verifying a new object.

EFFECT: increase in the quality of preprocessing of the data subject to entering and verification of the entered data.

3 cl, 3 dwg

Description

FIELD OF TECHNOLOGY

The present technical solution relates to the field of computer technology, in particular to a computer-implemented method for entering large scientometric data into an interactive information and analytical system.

BACKGROUND OF THE INVENTION

In the field of science and higher education, information and analytical scientometric systems, also called CRIS systems (Current Research Information Systems), are usually used to support the adoption of administrative and managerial decisions. Such systems accumulate data on scientific, technical, experimental design, innovative, technological and pedagogical activities not only of the organization as a whole or individual teams, but also on the results of its individual employees. When addressing issues of an administrative and managerial nature, as a rule, they rely on analytical data on organizations as a whole (the formation of rating indicators at different levels, analytical materials in certain areas of research, reporting materials, etc.), on data on structural divisions of the organization and for individual employees (for example, competitive election). To solve these problems effectively and adequately to the realities, it is necessary that a single, easily verifiable information space be formed permanently in time. Such a space is characterized not only by the fact that the data is verified using all available modern digital technologies and administrative mechanisms, but also by the fact that they are relevant, easily reproducible and verifiable, and also comprehensively reflect scientific, technical, developmental, innovative and technological activities. and work on the training of appropriate personnel of specialists by subjects at all levels of such activity (organization, team or individual employee).

The prior art solution is chosen as the closest analogue, RU 2569363 C1. This solution refers to information systems, namely to systems that allow analyzing the publication and publishing activity of employees of scientific and educational organizations.

The solution disclosed in the source is aimed at creating an automated information system that allows collecting and accumulating data on their publications in the context of employees of the organization, analyzing publication activity with various levels of detail, building custom reports in accordance with the forms required by the Ministry of Education and Science of the Russian Federation for the results of intellectual activities of scientific and educational organizations.

The proposed solution is aimed at eliminating the shortcomings of the state of the art and differs from the known solutions in that the proposed method provides prompt pre-processing of the input data and high-quality verification of the input data.

SUMMARY OF THE INVENTION

The technical problem to be solved by the claimed solution is the creation of a computer-implemented method for input and verification of large scientometric data into an interactive information-analytical system. Additional embodiments of the present invention are presented in dependent claims.

The technical result consists in improving the quality of input data preprocessing and improving the quality of input data verification.

The claimed result is achieved through the implementation of a computer-implemented method of input and multi-level verification of large scientometric data into an interactive information and analytical system, which contains the steps at which:

uploading primary data on the results of scientific and technical, experimental design and innovation and technological activities through the upload file and/or through the interface of an external information system;

carry out an automatic fuzzy search for duplicates according to predetermined primary data in an interactive information and analytical system;

carry out an automated comparison of the authors of the results of scientific and technical, experimental design and innovation and technological activities with existing authors in an interactive information and analytical system;

carry out affiliation of primary data with existing objects in an interactive information and analytical system;

save the primary data and create a new object in the interactive information and analytical system;

carry out automated verification of a new object created in an interactive information-analytical system at different administrative levels.

In a particular embodiment of the described method, bibliometric and other reference information is automatically loaded via an upload file and/or via an interface of an external information system.

In a particular embodiment of the described method, information about the result of scientific and technical, experimental design and innovation and technological activities is automatically verified through the interface of an external information system.

In a particular embodiment of the described method, notifications are automatically sent about the addition of a new result of scientific, technical, experimental design and innovation-technological activities to users of the interactive information and analytical system who are the authors of this result.

DESCRIPTION OF THE DRAWINGS

The implementation of the invention will be described hereinafter in accordance with the accompanying drawings, which are presented to explain the essence of the invention and in no way limit the scope of the invention. The following drawings are attached to the application:

Fig. 1 illustrates a block diagram representing a data entry model on the results of scientific and technical, experimental design, innovation and technological and training activities.

Fig. 2 illustrates a block diagram representing a model of multi-level verification of data on the results of scientific and technical, experimental design, innovation and technological and training activities.

Fig. 3 illustrates a diagram of a computing device.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description of the implementation of the invention, numerous implementation details are provided to provide a clear understanding of the present invention. However, one skilled in the art will appreciate how the present invention can be used, both with and without these implementation details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to unnecessarily obscure the features of the present invention.

Furthermore, it will be clear from the foregoing that the invention is not limited to the present implementation. Numerous possible modifications, changes, variations and substitutions that retain the spirit and form of the present invention will be apparent to those skilled in the subject area.

The proposed solution, within the framework of a large, in terms of data scale, and complexly organized scientometric information and analytical system, provides input, preprocessing and storage of the most complete, verified data on the results of scientific, technical, experimental design and innovation and technological activities of employees, teams participating in it project executors and the organizations they represent, by using models and software mechanisms of preferential bottom-up input by the authors, followed by their automatic verification by the co-authors responsible (authorized by the organization) for maintaining information in the system.

The subject area within which the proposed method is used consists in the interactive collection, preprocessing and storage of a large amount of diverse data on the results of scientific, technical, experimental design, innovation-technological and pedagogical activities of subjects at different levels of its organization (carrying out), their permanent analysis and obtaining integral rating estimates of its effectiveness (efficiency).

The proposed solution is a way to enter large scientometric data into an interactive information and analytical system (hereinafter referred to as the System) and verify them at several mutually complementary administrative levels of organization of scientific and technical, experimental design, innovation and technological and personnel training activities for it, implemented as a software tool of the System.

The subjects of the proposed solution are the users of the System who interact with it in an interactive mode in order to enter and verify data: the initiators of the entry; co-authors of the result of the activity, which is entered into the system by the initiator; employees responsible for maintaining the System from structural subdivisions of the organization, of which the initiator is an employee.

The interaction of subjects in the input and verification processes is carried out through the use of software and hardware mechanisms (interfaces) that implement the proposed method.

Subject provisions characterizing the method.

1. Information-analytical scientometric systems in their broadest sense are considered as tools to support managerial decision-making in the field of scientific and technical, experimental design, innovation and technological activities and training specialists for it.

2. To solve various issues of a managerial nature (stimulating effective work, activities in priority areas, etc.), analytical data is required at different administrative levels: organization; structural subdivision; scientific team; individual worker.

3. Input and verification is one of the key components of the System for the formation of the most complete and verified information space.

4. A wide variety of types of results of activities to be recorded, formats and data sources that characterize them, as well as the intensity of their input.

5. The completeness (representativeness) of the data necessary for the validity of the results of their analysis is ensured by regular incentives (various competitive procedures, awards, etc.), which, in turn, require their reliable verification.

Scheme for the formation of a verified information space.

In order for the data to be used for solving administrative and managerial issues, they must be: verified at the stage of their entry and accumulation in the System database; relevant in time, easily restored at all lower administrative levels (up to the personal level), at which they are formed; are full.

Ensuring the completeness and relevance of data requires the presence of constant factors stimulating data entry (competitive procedures, incentive bonuses, and others). The existing incentive procedures are based on the mechanisms of rating assessments. In order for such estimates to be objective and correct, the data used to calculate them must be verified and up-to-date. It should be noted that the ratings are preliminary and do not replace the examination. The final ratings of efficiency (performance) should be formed on the basis of preliminary ratings, taking into account the results of their discussion by a panel of experienced experts.

As the basic principles for the use of scientometrics for the evaluation of scientific and technical, experimental design, innovation and technological and training activities, it is advisable to be guided by the provisions of the Leiden Manifesto, adopted by the international scientific community in the field of scientometrics in 2014.

1. The quantitative assessment of the activity of an individual employee is only the starting point for its subsequent assessment by a panel of experts.

2. The scientific and educational activity to be assessed and the corresponding indicators should be selected taking into account the goals and peculiarities of its organization in certain fields of knowledge.

3. Methods for evaluating activities should take into account national and regional characteristics, primarily the goals of its organization.

4. Data collection and analysis processes should be open, transparent and easy to understand and replicate.

5. The System should have mechanisms that enable employees whose performance is being assessed to verify and analyze the data that form the basis of the assessment.

6. Evaluation methods should take into account the fact that scientific disciplines differ from each other in publication and citation practices.

7. The quantitative assessment of individual employees should take into account their individual characteristics, including age, length of service, specifics of the field of knowledge, and others.

8. Evaluation methods should be free from indicators that have little effect on targets and the final result.

9. The enabling impact of the evaluation should be in line with the current goals.

10. Checking indicators for their compliance with the goals and objectives of the System should be permanent.

In order to unify and bring the methods of entering and verifying the System closer to international standards and requirements, they should be focused on the implementation of the listed principles.

1. When collecting and verifying data in large scientometric collections, preference should be given to ascending flows of their receipt, based on the personal or collective interest of the data source.

2. The processes of collecting and verifying data should rationally combine not only priority ascending “bottom-up”, but also descending “top-down” (from aggregated data to individual constituents) flows.

3. The processes of collection and verification should be based on a rational (balanced) combination for individual employees and for teams of incentives, both of a coercive (mandatory, binding) nature, and incentives based on personal and / or collective interest.

4. Methods and, accordingly, indicators and metrics for evaluating the effectiveness of the results of a researcher’s activities should include, in a balanced mode, the components that characterize the results of his activities in the training of specialists in the relevant field of science and, conversely, for teachers, the results of their scientific activities.

5. Methods for assessing the effectiveness of the results of the subjects of scientific activity (both personal and collective) should properly take into account its innovative component.

6. Subject to the principles of transparency (transparency) of general data for the "broad" scientific and educational community in accordance with the provisions of the regulatory and legislative framework of the Russian Federation, the mechanisms of the System must guarantee:

• to each scientist and teacher the confidential status (confidentiality) of personal data, as well as information that constitutes an assessment of his personal professional activity;

• confidentiality of aggregated data that is not subject to disclosure to each organization and government body, including the provisions of documents at the local and departmental levels.

7. The mechanisms and data in the System, as a matter of priority, should be focused on taking into account national characteristics, interests and strategic targets for the development of the scientific and educational potential of Russia, as well as the tasks of each individual organization that follow from this.

8. The methodology for creating, establishing, maintaining (operating) and developing the System should be based on the principle that simultaneously allows:

• create mathematical, algorithmic and software for individual functionally closed, new components and blocks of the System;

• ensure bringing newly introduced components and blocks of the System to the state of their functional self-sufficiency and demand in practice;

• to accompany the System in the process of solving practical problems, to ensure its modernization (refactoring, reengineering) based on the analysis of the results of operation without losing the functionality already present.

With the above principles in mind, the following are considered as basic requirements for methods to be used in entry and verification mechanisms.

• The authors of the results of activities entered into the System can and should participate in the processes of data entry and verification (bottom-up entry).

• In order to avoid additional verification difficulties (entry errors, duplicates, etc.), it is sufficient to enter data on each individual activity result once (“single entry”).

• Verification processes should actively use top-down data from trusted data repositories (catalogs, databases, etc.).

• The subsystem should allow entering the results of both scientific and technical, experimental design, innovation and technological, as well as activities for the training of highly qualified personnel for the relevant field of knowledge.

Requirements for the processes of user interaction with the System, for models and software interface at the stage of data entry and verification

• The model and the interface realizing its interest should allow entering information about the result in a visually understandable form for the user.

• Users associated with the result (co-authors) should be able to automatically receive, check and correct the entered information.

• Those responsible for maintaining information in the System at the place of work of the author or co-authors, when checking the correctness of the entered data and before confirming these data on their part, should be able to correct the information about the result on their own or ask the author to do this.

• When adding a result of an activity to the System, it should be checked for its absence in the System.

• If the structure of information about the result of activity provides for a connection with other objects of the System, then interface mechanisms should provide an opportunity to determine whether such objects exist in the System.

• If technically possible, the interface should allow loading data for the form from external systems by a given result identifier in these systems. This, however, does not eliminate the need for duplicate checking and author matching.

• The programming interface must be consistent with the position of the thesaurus (basic definitions) adopted in the System at the stage of its design.

• Software mechanisms that implement data entry and verification processes should prevent destructive and unregulated influences on data by unauthorized persons.

Problem domain models.

Model of the result of scientific and technical, experimental design, innovation-technological and pedagogical activities.

The model of information about the result of scientific, technical, experimental design, innovation, technological and pedagogical activities in general can be represented as follows:

• information about the authors of the result (name, contact details, affiliations);

• connection of the result with other entities (grants, dissertations, use of equipment, etc.);

• the output of the result;

• meaningful description of the result (title, annotations, keywords, full text, etc.);

• secondary analytical data about the result that appeared after the result was registered (quotation rates, the proportion of incorrect borrowings, other analytical data).

Sources of information about attributes that characterize various types of performance results

For each type of performance results, several main sources of information about the attributes that accompany (characterize) them can be distinguished.

The results are usually recorded in publications (publications), databases (patents, copyright certificates) or official documents (reading of training courses is reflected in the curriculum). In such sources, basic information about the result is recorded (usually in text form), and a registration number can also be assigned to it. Sources of this type can be used to check the correctness of information, primarily about the output data.

For some types of outcomes, there may be additional data sources that can be obtained by processing the primary outcome data. An example of such a source can be a bibliometric indexing system that receives data directly from publishers, indexes them and produces new information - journal impact factors, publication citation data, and others. As another example of a data source for post-processing of results, we can single out the Anti-Plagiarism search engine for incorrect borrowings and its analogues.

For the types of sources described above, software mechanisms for searching and validating data may be available. If there is a similar API and there is access to this interface, then automatic data validation is possible. If there is no such interface, then manual verification is possible (checking according to documents, checking on the official website of the source).

The second type of source of information about the result is its authors, who were directly involved in obtaining the result and check the links of this result with the profiles of its co-authors. In most cases, authors also take part in the verification process at the time of entry, which was noted above.

The third type of source can be the organization itself or a structural subdivision of this organization represented by an authorized representative who is a user of the system, who can check how the result relates to the research of the subdivision or check whether the article contains the specified affiliation of the author with the organization. In addition, these authorized users may have access to data across the entire organization or department, and they may perform additional checks and corrections on the data with a view of the big picture of activity. This type of source can also include internal information systems and databases of the organization, for example, a personnel accounting system.

Each of the above types of sources has unique information about the result, which the others do not have, or they are not its primary source.

Entering information about the result and primary verification.

Entering information about the result involves the use of data from sources and can be implemented with varying degrees of automation, depending on the capabilities of specific sources.

1. Input / upload of primary data.

This stage involves entering information without creating links with other objects of the system (Fig. 1, pos.1.1-1.4). Primary data is automatically parsed by a chain of parsers that run sequentially until one of them receives the parsed data or none of the parsers can complete the parsing. The main formats supported by parsers are: DOI, JSON, XML, HTML, BibTeX, bibliographic references.

1. If a DOI identifier is specified, then a request is made to the API of the official DOI Crossref registrar. The result of the request is result meta data in JSON format, which contains information received from the registering source, such as a publisher.

2. Bibliometric and bibliographic information systems, as a rule, allow exporting data in various formats. The standard format for bibliographic data is BibTeX and MODS XML, for which there is a set of bibutils utilities. These utilities allow you to translate bibliographic data between these and other bibliometric data formats (RIS, EndNote, Pubmed XML, ISI, Word, etc.). With the help of bibutils, the data is converted into the MODS XML format, and then parsed by the parser.

3. If the primary data is transmitted as a bibliographic reference, then it is automatically parsed using the FreeCite service. This service allows you to further train the parsing algorithm based on the results of user evaluation.

The described approach is modular and allows you to develop and connect additional modules for loading and processing primary data on the results. A programming interface is also provided for automating the procedure for manually filling out the form with primary data by the author.

2. Checking for the existence of duplicates.

According to the given primary data, an automatic fuzzy search for duplicates is performed (Fig. 1, pos. 1.5-1.6). Fuzzy search is a search for information in which information is compared to a given search pattern or a value close to this pattern. If duplicates are found, the result is not added to the System. Depending on the type of result, different algorithms for finding duplicates can be used. The following algorithm can be used as a basis. If among the result attributes there are its unique identifiers (DOI or an identifier in an external system), then, first of all, a check is made for the existence of duplicates with the same identifiers. Further search for duplicates is implemented using a fuzzy search for attributes such as the name of the result, the name of the source, the names of authors, etc.

3. Automatic comparison of authors with users.

At this stage, information about the authors is provided in text form, and it is necessary to compare the authors of the result with the existing authors in the System, some of whom may be users. Due to the fact that the primary information may contain various variations of names, initials, and also due to the presence of namesakes, it is necessary to take into account the previous joint mentions of the authors. Information about the authors is transmitted for parsing to a service based on the author-developed algorithm for resolving ambiguities in matching authors according to the graph of co-authorships. The algorithm lies in the fact that each author of a new result can be associated with a certain set of existing authors in the System based on the text proximity between their names and the names of the authors of the added result. The text proximity function can take into account transliteration variations, typos, abbreviated or omitted parts of the name, and other modifications. The optimal solution is such a set of authors, which achieves the maximum value of the quality function. The result of the algorithm is a set of comparisons of the names of authors and identifiers of existing authors in the System (Fig. 1, pos. 1.7).

4. Affiliations of the result with other objects in the System.

This sign refers to the establishment of links between objects. In addition to authors, the result can be associated with other objects of the System, for example, journals, collections of articles or conferences. According to the textual information presented in the primary data, it is necessary to find suitable objects of the corresponding type (Fig. 1, pos. 1.8). If the object is found in the System, then a comparison is made, if the object is not found, then a new object is created. In some cases, the automatic creation of a new object is undesirable, then instead of creating an object, an application will be opened to add such an object to the System. Such applications may be considered by the System administrators. For example, there is an object of type "journal", when entering an article, journals are searched for by output data, if an object of type "journal" with the same output data (ISSN, title) is found, then a connection is established with it. If not found, then a request for adding a journal with such output data is opened, which is considered by the System administrators.

5. Saving the result and notifying users.

A new object is created in the System corresponding to the added result (Fig. 1, pos. 1.9). The authors of the result who are registered in the System, as well as those responsible for maintaining information in the system at the place of work of all authors, are sent a notification that a new result has been added (Fig. 1, pos. 1.10). In the event of incorrect bindings or other errors, the authors and their responsible persons can make corrections or declare a disclaimer of authorship.

Subsequent stages of multilevel verification.

Verification by co-authors.

All co-authors, as well as users responsible for data maintenance by departments in which the authors work, have the opportunity to correct the result data (Fig. 2, pos. 2.4), as well as indicate additional data on the publication, such as affiliations of authors with organizations, affiliations publications with other results (projects, dissertations and others). If the result was linked to the author by mistake, then the user corresponding to this author can refuse authorship, and the publication will not be displayed in his profile.

Verification in external sources.

Data verification can be performed automatically using external information resources. Taking into account the fact that in bibliometric systems the results may be indexed with some delay after their publication, it is advisable to periodically check for indexing these results (Fig. 2, pos. 2.5).

For results of this type, such as a publication, the search engines of these systems allow you to generate queries in the form of various sets of output data: title of the publication; names of authors; page numbers; publication year; DOI; name of the journal; ISSN of the journal; issue number etc. . Since there may be errors in the information or differences in the spelling of special characters (publications in mathematics or chemistry), it seems appropriate to search not only in the full set of available publication data, but also in subsets of this set. The accuracy of the search decreases as the subset decreases, so the queries are ordered by the degree of "reliability" of such a subset. Subsets from which the publication cannot be uniquely identified, such as year and page numbers, are not used. If the search engine managed to find an article in an external system, then it is "linked": the identifier of this article is stored in the system where it was found. It should be noted that such a search principle is universal and can be applied to other types of results if appropriate search mechanisms are available and accessed.

For those publications that managed to create "bindings", the number of citations is periodically updated according to those indicators that are calculated by bibliometric systems. If an article is indexed in both Web of Science and Scopus, then it has two indicators: the number of citations according to Web of Science data; the number of citations according to Scopus.

Depending on the functionality of the API of external systems and the restrictions on the subscription of organizations to these systems, it is possible to implement additional verification mechanisms. An example of such a mechanism is the loading of authors' affiliations with organizations. For the correct accounting of articles published by the organization, it is important that each article correctly indicates the authors' affiliations with this organization, since these publications are made as part of the work of the author in this organization. This information is provided by the authors themselves during the preparation of the publication, and then transferred to the publisher.

Model and software verification mechanisms responsible.

An important part of the verification of performance results is the mechanism for their verification of the System at different administrative and organizational levels. Such verification is multi-level and assumes that the results are first checked by those responsible for the lower level units (departments, laboratories; see Fig. 2, pos. 2.6), and then are checked by those responsible for the upper level units (faculties, institutes, centers; see Fig. 2, item 2.7) or for the organization as a whole. Responsible at the lower level have an idea of what activities are carried out in their departments and can confirm the data on the result, send the result to the authors for correction of information, or reject the result with a note that it does not relate to the activities of the department.

Top-level owners receive data on the results of workers from all lower-level departments, so they must be able to check whether the result is approved by the owner or rejected.

To calculate the rating scores, various options for accounting for confirmed results can be used. Depending on the specific tasks of the evaluation, the results that were not rejected by the responsible (“black list”), or only those results that were confirmed by the responsible (“white list”) may be included in the calculation.

If the authors of the result work in several structural subdivisions, then such a check is performed for all these subdivisions.

On FIG. 1 shows a flowchart that implements business processes for entering and verifying information about the results of scientific and technical, experimental design, innovation and technological and related forms of activity, is presented in the form of the following flowcharts using the example of a scientific and technical publication, as a result activities.

The user is offered a choice of options: to receive (import) data on the result of activities through the upload file or through the program interface of an external information system (1.1). Further, automatic loading of information on the results of scientific and technical, experimental design and innovation and technological activities is carried out through the interface of an external source (1.2). The form for entering information is automatically filled in using the received data by parsing (parsing) (1.3). The process of filling in the input form by the user (1.4) is carried out. Next, an automatic search for duplicates of the results of scientific and technical, experimental design and innovation and technological activities entered into the interactive information-analytical system (1.5) is carried out. Next, it is checked whether there are duplicates of the results of scientific and technical, experimental design and innovation and technological activities entered into the interactive information-analytical system (1.6). After that, the authors' comparisons (1.7) are automatically determined. Then there is an automatic establishment of links (affiliations) with other information objects in the system (1.8). At the next stage, the results of scientific and technical, experimental design and innovation and technological activities are added to the information and analytical system (1.9). After that, the process of notifying the co-authors of the entered data and those responsible for maintaining information in the information and analytical system at the place of work of all authors is launched that new results of scientific, technical, experimental design and innovation and technological activities have been added (1.10).

On FIG. 2 shows a block diagram representing a model of multi-level verification of data on the results of scientific and technical, experimental design, innovation and technological and training activities for specialists.

At the first stage, an automatic search for comparisons of the authors of the results of scientific and technical, experimental design and innovation and technological activities is carried out. Next, the correctness of the results of comparisons (2.1) is checked. After that, an automatic search for duplicates of the input results of scientific and technical, experimental design and innovation and technological activities in the information and analytical system (2.2) is carried out. Connections (affiliations) of the input results of scientific and technical, experimental design and innovation and technological activities with other information objects in the information and analytical system (2.3) are established. Next, the co-authors and those responsible for maintaining information in the information and analytical system are notified that new results of scientific, technical, experimental design and innovation and technological activities have been added (2.4). At the next stage, an automatic search for the results of scientific and technical, experimental design and innovation and technological activities in external information systems and databases (2.5) is carried out. After that, the results of scientific and technical, experimental design and innovation and technological activities are confirmed by those responsible for maintaining information in the information and analytical system, starting from the level of structural units, ending with the level of organization (2.6, 2.7).

On FIG. 3 will now present a general diagram of a computing device (300) that provides the data processing necessary to implement the claimed solution.

In general, the device (300) contains such components as: one or more processors (301), at least one memory (302), data storage medium (303), input/output interfaces (304), I/O means ( 305), networking tools (306).

The processor (301) of the device performs the basic computing operations necessary for the operation of the device (300) or the functionality of one or more of its components. The processor (301) executes the necessary machine-readable instructions contained in the main memory (302).

The memory (302) is typically in the form of RAM and contains the necessary software logic to provide the required functionality.

The data storage means (303) can be in the form of HDD, SSD disks, raid array, network storage, flash memory, optical storage media (CD, DVD, MD, Blu-Ray disks), etc. The means (303) allows long-term storage of various types of information, for example, the above-mentioned files with user data sets, a database containing records of time intervals measured for each user, user identifiers, etc.

Interfaces (304) are standard means for connecting and working with the server part, for example, USB, RS232, RJ45, LPT, COM, HDMI, PS/2, Lightning, FireWire, etc.

The choice of interfaces (304) depends on the specific implementation of the device (300), which can be a personal computer, mainframe, server cluster, thin client, smartphone, laptop, and the like.

As means of I/O data (305) in any embodiment of the system that implements the described method, the keyboard must be used. The keyboard hardware can be any known: it can be either a built-in keyboard used on a laptop or netbook, or a separate device connected to a desktop computer, server, or other computer device. In this case, the connection can be either wired, in which the keyboard connection cable is connected to the PS / 2 or USB port located on the system unit of the desktop computer, or wireless, in which the keyboard exchanges data via a wireless communication channel, for example, a radio channel, with base station, which, in turn, is directly connected to the system unit, for example, to one of the USB ports. In addition to the keyboard, I/O devices can also use: joystick, display (touchscreen), projector, touchpad, mouse, trackball, light pen, speakers, microphone, etc.

Means of networking (306) are selected from a device that provides network data reception and transmission, for example, an Ethernet card, WLAN/Wi-Fi module, Bluetooth module, BLE module, NFC module, IrDa, RFID module, GSM modem, etc. With the help of tools (305) the organization of data exchange over a wired or wireless data transmission channel, for example, WAN, PAN, LAN (LAN), Intranet, Internet, WLAN, WMAN or GSM, is provided.

The components of the device (300) are coupled via a common data bus (310).

In these application materials, a preferred disclosure of the implementation of the claimed technical solution was presented, which should not be used as limiting other, private embodiments of its implementation, which do not go beyond the scope of the requested legal protection and are obvious to specialists in the relevant field of technology.

Claims (9)

1. A computer-implemented method for input and multi-level verification of large scientometric data into an interactive information and analytical system, containing the steps at which: unload primary data on the results of scientific and technical, experimental design and innovation and technological activities into an interactive information and analytical system through an upload file with automatic detection of the format of the downloaded data and/or through the interface of an external information system; carry out an automatic fuzzy search for duplicates of the results of scientific and technical, experimental design and innovation and technological activities entered into the interactive information and analytical system, using predefined primary data and data on the results in the interactive information and analytical system, while, in case of finding duplicates , the specified results are not added to the interactive information and analytical system; carry out an automated comparison of the authors of the input results of scientific and technical, experimental design and innovation and technological activities with existing authors in an interactive information and analytical system based on the results of the analysis of existing data on joint publications of the authors of the input result; carry out affiliation and preservation of primary data on the results of scientific and technical, experimental design and innovation and technological activities entered into the interactive information and analytical system with existing objects in the interactive information and analytical system, while objects in the interactive information and analytical system are understood as data on journals, collections of articles or conferences; save the primary data and create a new object in the interactive information and analytical system; information on the result of scientific and technical, experimental design and innovation and technological activities is automatically verified by automated search in external information resources with the preservation of identifiers of the results found and further periodic updating of data on the result, including related bibliometric indicators. 2. The method according to claim 1, in which bibliometric and other reference information is loaded in an automated mode via an upload file and/or via an interface of an external information system. 3. The method according to claim 1, in which notifications are automatically sent about the addition of a new result of scientific, technical, experimental design and innovation and technological activities of users of an interactive information and analytical system who are the authors of this result.

Images