Here you will find a detailed and updated manual for Kampal. If you want to check out a quick guide, you can do here. If you prefer to see which information Kampal stores about myself and my environment click here.


Application description

Kampal Research (KR) is a tool developed and commercialized by Kampal Data Solutions for the analysis of R+D centers and institutions. For this purpose, all the available data from corporative and third party databases is downloaded. Then, their structure is analyzed and visualized using statistics and complex systems techniques, plotting the results in terms of numbers and graphs. This way we obtain a reliable representation of the institutions and groups that form it, and relevant information for decision making in short, medium and large term to making it better.

Data is the keystone of this tool, and they are related to the analyzed institution. Essentially:

  • Data about the institution personnel.
  • Structure
  • Data related to the institution activity: publications, projects, thesis, patents, etc.

With them, we perform a deep analysis of the institution, providing among others:

  • Statistical activity analysis
  • Relational map construction
  • Community identification
  • Strengths and weaknesses of the structure
  • Highlighted or peripheral researchers
  • Evolution over time of the activity
  • Identification of new opportunities or recessionary activities
  • Reports for decision making
  • Etc.

{% trans 'Diferentes instituciones tienen categorías y clasificaciones diferentes. Una Institución puede tener registradas las Patentes, o las estancias Erasmus de sus estudiantes, y otra no. Por eso lo reflejado en este manual puede no corresponder exactamente con una Institución concreta.'

In the following figure we can see a global schema of the data and information flow at Kampal Research.

Application access

The access can be through, where there is a link to There you can access public projects. If you have an account you can access all public and private projects associated with that account.


In Kampal Research (KR) each basic unit of analysis, the project generally corresponding to an institution, can be configured as public or private. If public will be accessible by any visitor to the site, if it is private, only with username and password.

Projects configuration

Projects can be configured by the owner (superuser, there can be several). For this, once logged in, he can access to the administration project menu clicking in the project icon. The available functionalities are:

  • Pick an image. It will be shown for accessing the project.
  • Set public/private. It it is declared public, anyone will be able to visualize and brose through the project (except in configuration). If it is private, it will be only accesible if he has an account
  • Create a user. The project superuser can create here, in case the project is public, all the users he wants, assigning them a default password. Only in private projects. This users can't access the configuration menu.
  • Description. A brief descriptive text of the project that will be shown as Info.

Access to the projects

Through public projects or my projects (if you are logged in) you can access to the corresponding projects.

We can see a project list with a brief summary of each one.

To access a single project, you have to click over its image; if it is public you will access directly; if it is private, it is asked for a user-password if you are not logged in with an authorised user. For following this manual, it is recommended to use the application. If you don't have an authorised account, the Demo institution, ficticious and public, allows you to see it.


When we acces a concrete project, the system loads the initial visualization after a few seconds. It is the full system (all the researchers of the institution with some parameters set to default values. The view is similar to the following one

The full analysis consists of a wide set of results, accessibles through the selection of several options and parameters. Everything can be seen and modified with this interface.

  • Viewer. It is the section in which you can see the results of the analysis, in graphic or numeric mode. In this case we shop a typical graph.
  • Analysis. Here we set the set of researchers, what data are we going to analyse, and how the collaborations between them are defined. That is to say, we specify people (nodes) and the relations to study (links).
    • Selection
    • Network
    • Modifiers
  • Data to show in the viewer
    • Production
    • Communities
    • Evolution
    • Network properties
    • Selection evaluation
    • Listing
  • Summarized description
  • Other options
    • Links visibility
    • Search
    • Generate PDF
    • Animation


In the middle is where all the results are visualized.

The results are shown grouped in two big categories that correspond to the map or statistics tabs.

Inside the viewer we can choose numerous parameters and options, as the posibility of downloading a PDF, switch to full screen, or see an animation with the activity evolution.

Here you can see the collaborative map, being able to see the groups and communities, interactions, the role and relevance of each subset or researcher. It gives us information about the strength and the intensity of the collaborations and the role played by communities and people.

We see all nodes (researchers) as circles of different sizes and colours and links (relations) as lines of different colours and thickness

Below on the left we can see a description of the parameters that correspond mainly to the analysis being performed, for example

Artículos por impacto, tamaño por producción, coloreado automático, hasta 2015

For different selections, even with the same researchers, if we change the link weight or the data type used, we obtain totally different maps, each one of them having important information for each concrete activity.

Navigation in the viewfinder

Using the visor we can zoom in and out using the wheel mouse, and we can browse around the map ckicking and dragging over it (the links are removed at that moment to improve performance). We can perform searches of nodes in the map thanks to the bar placed in the top right corner.

Node information

Clicking in one node we can see basic information about it.

  • Name and ascriptions to different structures such as department, faculty intitute, etc.
  • A picture of the researcher.
  • A link to the address you want the user (for example your website or Facebook page...)
  • A link to the a PDF file of the CV
  • Full report about the researcher activity, both in absolute and compative data with the institution. Given that it contains a huge amount of information and some ideas have not been introduced yet, we will dedicate a specific section later (Seeindividual report)
  • Collaborative path between researchers: in the dropdown of each researcher we can seeGo fromandGo to. If we click in a node and press the "Go from" button, we select this node, and then we can click in other node and press the button "Go to". This way, the system will calculate the shortest path between both researchers, choosing always existing links, that is to say, collaborations. We are able to know how close and how far two researchers are in terms of collaboration, or how to go from one to another through its collaborators The path is the shortest path if we assume that the distance of each link is proportional to the inverse of its weight, so it hasn't to be the path with less links. It will be the one in which the sum of the effective distances of all links is the smallest one.

Additional actions

In the lower part we can perform three extra actions:

  • Visualize links (on/off)
  • Request the generation of a PDF file with the map you are visualizing at this moment. The file contains informationa and logos properly placed.
  • Visualize the activity evolution animation.

The information of this dropdown (pictures, ascriptions, files...) are only accesible when the data is in the server and users are authorized.

In the upper right part whe have a search bar for the current map.


From here, we can select the set of researchers to analyze. It is possible to use a wide variety of options for the selection.

  • Select by name
  • Select each organizative structure
  • All or a subset
  • A blend of everything above
Modifier for collaborators

Given a set of people, we can decide to do the analysis of just these researchers or to add their collaborators.

Period to account merits

This option is only active when we select an ascription, but only one (for example a department). It shows us if all the merits of a researcher have to be accounted or just those that have been generated while the researcher has had an ascription to the selected institutions.

The user indicates what he desires and the system automatically completes searching among all the names and surnames of the researchers. It also takes into account the names of all organizative institutios (departments, institutes, etc.).

Several searches

Once the search has been finished, it remains in the selector. We can add as much selections as we want, and they will be joined adding all the researchers of all of them.

Remove selection

It allows to remove all the selections clicking in the cross of each field.


Here you can visualize all the organizations that form the institution.

First, the different types of structures are deployed (departments, faculties,...), and inside each type, a full listing of the specific structure. You can select one by one or all at the same time.

Add collaborators

  • No. Strictly all the selected researchers. By default
  • Level 1. Let's consider level 1 collaborators those researchers who have collaborated with at least one researcher of the selected subset. That is to say, given a selected subset, level 1 is composed by every researcher that belongs to this subset and those who have a link with them.
  • Level 2. Let's consider the selected researchers and the level 1 researchers. Then, we add all the collaborators of all of them, that is the original set, those who have a link with them, and those who have a link with the previous ones.
  • Level 3. Let's consider the selected researchers, the level 1 researchers, the level 2 researchers, and then we add all the collaborators of them.

It is convenient to remark that the number of people grows proportionally with the level

Period to account merits

It is only active when selecting a type and only one of secondment, for example if we select an institute, or department.

Let's suppose we have selected a department. In this case, in general, a researcher will have belonged to that department for a certain period, not during his entire professional life in the institution.

For example, if we select department A, the investigator X may have belonged to that department between 2001 and 2008, and from 2008 to present to the department B. Well, if you select "Merits of each researcher in the period of membership to the selection" it will count only the merits of the research between 2001 and 2008. In this case also, researchers who do not belong to the department at the present time will appear with transparency in the graph. When the selection is made on more than one structure (eg two departments, or a department and a person), this option is disabled, and all merits are valued regardless of periods of secondment.


Once the study has been genrated, in this line it will appear the visualized parameters. These parameters correspond to the last generated view, not necessarily to the default one. By clicking on Reload in this line, we go back to the default vision and all parameters are reset.


Clicking here all calculations and real-time analysis is performed. After the calculation, the results are loaded and shown according to the parameters selected at that time that appear in the viewfinder. Note that if we changed the set of researchers, and before pressingGeneratewe change the view options, this change is applied to the previous selection, not the current one, which is not active until you click Generate.


Here we determine the type of data to be analyzed, the kind of relationships we want to study and the weight we give to such relationships to build collaborative networks. For example if we want to study the activity in scientific publications, we choosePublications, and within it, if we want to weight the relations according to the impact of the journal, we select Indexed.

Each choice is used to analyze different aspects of the system, so we have different views. Of course it is possible to do studies where different activities are integrated, if we want to have a more global view of the system.

Kampal permite el análisis de todos los artículos registrados en la Institución. Cuando los artículos han sido publicados en revistas indexadas, pueden realizarse análisis específicos con criterios derivados del impacto o posición relativa (cuartiles, deciles…). Para analizar estas publicaciones debemos seleccionar Indexados en Tipo de Artículo. Si queremos hacer un análisis de todos los artículos registrados en la Institución, estén o no indexados, debemos seleccionar Todos en Tipo de artículo. En este caso no existen criterios adicionales de calidad, pesándose los artículos todos igual.

Each choice is used to analyze different aspects of the system, so we have different views. Of course it is possible to do studies where different activities are integrated, if we want to have a more global view of the system.'


Let's discuss the different options


Fixed the set of people in the selector, the study will be based on the same publications that appear in the project database in which we can find at least one of the selected authors. All studies will be conducted based on the chosen aspect (Indexed, Excellence...).

Weight of Publications

If we choose the criterion All, we consider the totality of the articles registered in the Institution. In this case we do not consider options to weigh the quality of the articles, and all are considered equal, basing the analyzes on the total number.

If we choose the Indexed criterion, then articles published in indexed journals are analyzed.

To quantify the analysis we can set several criteria

  • Indexed. Each publication is weighted according to its Impact Factor. It is the simplest way to assign a weight to publications to measure the quality of them.
  • Quartile. The quartile of the publication is estimated in the corresponding year and we assing it 4 points if it is in the first quartile (Q1), 3 if it is in the second (Q2), 2 in the third (Q3), and 1 if in the fourth (Q4). Quartiles (or percentiles in general) are often used worldwide by organizations and rating agencies worldwide.
  • Excellence. For the study we only consider those publications that are in the first decile, all with the same weight of 1. The rest are not considered. It allows us to analyze the excellence.
  • Number. All publications weight the same. We select 1. It allows us analyze the production by number.

For each publication we use the impact factor of the journal of publication according to the Journal of Citations Report (Indexados). If there is no data available for a year for a journal, it is assigned the impact of closest year available.

Impact factors

While it is well known, we recall briefly how impact factors, quartiles, deciles, etc. are fixed.

A publication in a journal indexed has several fields, namely:

  • Journal of publication
  • Date
  • Authors and affiliation
  • Body
  • References

The Journal of Citation Reports (JCR) contains an updated list of relevant journals (indexed) that establishes the impact factor for each year as follows (in simplified form)

Let's consider the publications published in a journal A indexed in 2010. Now we calculate how many citations they have received all these publications during the following year. We account only citations from indexed journals. We divide by the total number of publications from the journal A in 2010, and that is our impact factor for the journal A in 2010. That is, in summary, the average number of citations that an article published in that journal generated after two years.

This definition has nuances and some questionable aspects, but it is certainly the most internationally accepted criteria for assessing publications. Note that the valuation is therefore based on the journal that published the article and the particular year, and not to specific aspects of the article.

Among the many ways to parameterize the quality of publications, the most basic is the previous one. In general, the use of the impact factor has some problems.

The impact factor applies to all articles in the journal, but it is clear that not everyone has the same impact on the scientific community.

On the other hand there is significant heterogeneity between areas since the number of citations depends on factors unrelated to quality, such as the number of people in the area, types of studies, etc., and that are spread to other criteria such as the number of citations from an article or factors h of researchers.

This comes from the different number of publications and different number of signatures in each scientific area, so that the use of impact factors favors the busiest areas in number of publications, researchers and tendency to group of participants in the articles.

One way to avoid the problem of different impacts between areas, is the use of quartiles, deciles, or other percentiles. Quartiles, for example, are calculated by dividing into four journals of a given scientific area.

Thus, we weight the relative quality of the journal in your area, eliminating the heterogeneity between areas. That is, if the first journal of an area has an impact factor of 20, and the first from another area has 4 (as for example between medicine and mathematics) if we use impact factors researchers working on the first area will be ahead of the ones of the second area. If we use quartiles or percentiles in general, both journals are in the first percentile, and the weight would be the same.

So in general the use of percentiles generates more homogeneous and free of bias comparisons by area of ​​activity.

It does not eliminate the problem that within the same journal articles of very different quality are published.

Another important factor is the number of authors of an article. There are areas where it is common to divide the merits by the number of authors and other areas where this is not done. The problem is again that comparing areas where many publications are signed by hundreds of people to areas where it is usual to find just one or two signatories, where it generates huge heterogeneities.

All this is a constant source of study, research, debate, even controversy, and Kampal presents a number of options to cover all the accepted criteria so that in each research area the study can be tailored to its usual criteria.

When we study publications, the collaborative network is set considering that two people are related (they have a link between them) if there are any publications which appear both among the signatories.

We establish this way connections between people. This is why it is reasonable that a connection produced by a high quality publication attracts more signatories to a collaboration of poor quality.

The intensity of each of the links of our system must also be set. We must also consider that two researchers with 10 publications in common must be "more" related (attracted more) than two researchers with only 1.

Let's consider an article signed by four authors, and that we have decided to use the criterion of impact factor. We assume that there is no hierarchy in the signing of the publications in terms of order, position... Let's take as an example a impact factor of 8 for the journal in which the article was published. We create a link between all authors so that all weight the same, including a link with themselves. We do this so that the sum of each node links results in an impact factor of 8 corresponding to the publication in selected. According to the graph shown, we assign a weight of 2 to each link, so that each author receives a weight of 8 adding his links.

If we consider several publications, and we proceed with all of them the same wat, we get a set of relationships with links of different weights, such that the sum of all the links on a node gives us the sum of the impact factors of all articles of the person.

We end up this way with links between nodes, each one with a different numerical value, which corresponds exactly to the impact of common publications of these two nodes.

If instead of choosing Indexed we chose quartiles, we proceed in the same way, but the initial weight of the article is (4,3,2,1) depending on the quartile of the journal in which the publication falls.

Loop Nodes

These are the links leaving a node and return on the same node. In the case of publications, a loop indicates that the researcher has no collaboration with other people in the graph, but he does have published articles, which can have been published either with their unique signature or with people that are not listed in the graph.

Allocation of merits and number of signatories

From the above it can be seen that given the article of theexample, with an IF of 8 and 4 authors, each one receives a weight of 8 if we use the Indexed option. In some disciplines or in some assessment processes, it takes into account the number of authors, favoring publications with a low number of signatories, so we could choose to distribute the factor 8 between 4 authors, matching 2 to each author. This is especially significant when we have articles with around 1,000 signatories. In that case, if the impact factor is 5 for example, 1,000 people receive this merit, which in some disciplines is accepted but not others, since it is not considered the same to work to write an article between 1000 people than between 2. In fact, the article with an IF of 5 signed by 1,000 people distributes merits (valid for all CV) for a value of 5,000 units, while another article with an IF of 5 signed by 2 persons, distributes only 10 units. We can find again a great controversy: who argues that an article with 1000 people is complex and all have worked, and that the total work is gigantic, and who argues that the signing of 1000 people corresponds to technological or political criteria and not to relevant scientific contributions. In any case, Kampal supports both.

The The full merit of each publication option assigns to each signatory the full merit of the article, and this is the default option.

To choose the option of sharing the impact among the signatories (which applies to Indexed as quartiles, excellence or number) simply select The merit of the article divided by the number of signatories. Thus, in the example, the impact is distributed evenly among eight authors, two for each.

Impact factors

Given some data, such and article, and a specific weight, for example Indexed, we have well defined the relations relations between the nodes and their intensity. Relationships are represented by links, and their width is proportional to the strength of the relationship.

Then we assign positions to each node, so that those who are more attracted to each other than with the rest, occupy next positions. This is not generally simple because the relationships are complex, they are not are not watertight, and you have to decide where to place each one, knowing that it is impossible to be close to all collaborators. There will be some who will be away, because they have other intense collaborations with other groups.

We can make the analogy of a galaxy, where stars are attracted to each other, but the clusters are formed in regions of higher density, and these clusters differ from those around.

This ends up in a situation in which more compact groups are formed, where internal collaboration is more intense, and it is attached to the remainder by certain connections but weaker than internal.

Thus we can see at first glance the structure of collaborations of the selected researchers.

The above map gives us an idea of ​​what people work more assiduously with each other, but nonetheless this is not enough accurate because there are many people "sit on the fence". We may ask ourselves which are exactly the most similar groups, defining clear criteria for the decision. There are different algorithms that give similar results. Using these algorithms we can detect precisely which groups of people make up a community, understood as a group of people where the internal collaboration is more intense than with the rest.

Both the geometry and the communities depend on the type of data we are analyzing; for example if you are considering Articles -> Indexed the results are generally very different than the situation in which we consider Articles -> Excellence; and they will be yet more different if we consider Project -> Funds.

Geometry becomes evident to the naked eye. To quickly identify communities, we can assign a color to each one, and color all its members with the same color. This makes clear the structure of communities (in the graph we have surrounded some with of them with a red circle to make it more obvious). In the application, coloring nodes according to the community to which you belong, corresponds to the Color option -> Automatic.


Now consider projects that can represent basic research, applied research, national research, European research, research transfer, etc.

In projects we consider the following:

  • Principal Investigator (PI). In general a single one
  • Co-Investicators (CI)
  • Start and end date
  • Funds
  • Granting (or managing) agency

Now we build the collaborative network. First of all, connections (or links) are now not symmetrical in the sense that there exist PIs and CIs with very different roles in projects. We define the PI links that go to each CI or to the same PI as leaving ones.

The intensity of the links can be fixes in two ways:

  • Number. The weight of each project is 1 divided by the number of signatories. This way the weight of a project depends only on the number of signatories and it is more importantly the less they signatories it has.
  • Funds.The weight of each link is equal to the project funds divided by the number of signatories, so that each link weights the amount of money (distributed evenly) that travels through it.

Here we can see a graph for a simple project, with PI and 2 CI.

The crown in a node indicates that it represents the IP of at least one project. In the number option, each link would weight 1/3. In the funds option, if the project funds are 30,000 euros, each link would weight 10,000

Now we show 5 researchers that have 3 projects, each with a different weight, which manifests graphically in the different thickness of the links.

Projects type

Each institution assigns a type to projects, depending on the management office or entity that grants them. Projects can be filtered by this type, whose quantity and characteristics depend on each institution.

Loops on nodes

These are the links leaving a node and return on the same node. In the projects case, a loop indicates that the researcher is PI of a project.


Apart from analyzing separately the activity in articles and projects, an important sign of how collaboration works inside an institution comes from the sum of both. This gives us a new, more integrated, and collaborative vision of the network. There are other important activities included here, which may depend on each institution. So we can analyze activity in patents, creation of spin-offs, dissertations ...


This is common to academic and research institutions, as we consider a more global aspect, creating a link for each article or each common project, weighting all the same, regardless the IF of an article or the project funds. Each collaboration (publication or project in common) weights 1.


We analyze patent activity. The network is formed by connecting coauthors of each patent.


The map shows the collaborative structure according to the data (articles, projects, collaborations) selected and the specific parameters related to the weight of relations (quartiles articles, projects, funds, etc...).

Nodes are displayed in the map with a certain size and color, and they are placed in a certain position. None of this is random, everything is determined according to well-defined criteria and with a particular meaning.

After we select people and the type of network we want to see, we have a group of people (nodes) and a set of relations between them, relations that mean connections between people with a certain force (links).

Once nodes and links are defined, the first task is to build a map to assign each node a position on the map that gives us a quick visual idea of ​​how are the relationships, showing the collaborative structure, communities, isolated an united people, etc.

This calculation requires some processing time in the central servers of the application. Once we have completed this task, the geometry of the network is availavle, with node positions and the lines that connect them.

But apart from geometry, we can choose many other parameters to display our map, for example, we will need to define how colors and sizes are assigned to each node or link.

We can also choose if we want to see all researchers, or hide some for clarity. We can even choose a group of them according to some criterion.


The node size and color can be chosen according to various criteria


Here you choose what means the size of each node (the circle that represents each person). In general, quantitative data is associated with node area: that is, if a node has twice more data than other one, its area will be twice as big (and therefore its radius will be a root of 2 factor greater). There are several options:

  • Production. It corresponds to the internal production of the researcher within the community represented.
  • Total production. It corresponds to the total production of each author, the sum of all selected merits, whether generated inside or outside the community represented.
  • Centrality. The size is chosen according to the importance of the node to communicate and collaborate with its environment, especially as a person joining different communities, that is, the bridge role between individuals and communities.
  • PageRank.The relevance of the person is defined as the quantity and quality of the relationships with important people in the graph. It is the criteria used by Google to order searches.


We select the criteria for coloring each researcher:

  • Auto.The system identifies communities as sets of nodes that collaborate more closely, and all its members are colored alike. The links are painted with the color of the bigger node.
  • By ascription. We can choose that the color represents one of the organizations of the ascriptions of each node: for example Departments, Faculties, etc. Within each organization, given a faculty, we can choose the All option, in which each faculty is colored using a visible code or a specific faculty. In this case the faculty is colored using the corresponding color, and the rest in gray. This allows us to see the role played by each organization within other, identifying gaps or high densities.
  • Gender.The color is set according to the researcher sex. In this case we can discern the global role of men and women within the selected group.


It allows us to reduce the number of displayed nodes:

Year (from-to)

The maps are built using dates from which data is available for the institution and till the selected date here. If you select the last year, all available data are discussed.

Top N

Displays and analyzes the N researchers with greater production according to the selected parameters or also the N percent.


Men and/or women. It selects only people of one gender. If we use any previous top or gender option, first it is selected by gender, and on that selection, the corresponding top is made. (Not the other way around, which would give a very different result in general).


This is an advanced option, which allows integrating results.

It allows grouping multiple nodes into one, according to certain characteristics. By default there is no grouping, and nothing is said in the descriptor. The group becomes a single circle replacing all the balls that share the grouping property. Each ball has links with others through collaborations (articles or projects) between people of different groups.

  • Auto. Given the natural communities of the whole, all members of a community are joined to form a single entity represented with a ball whose size corresponds to the sum of the activity of its members. Using this automatic grouping, the name of each node corresponds to the bigger node among those who compose that community. Note: Using this set of nodes (which actually represents clusters) the detection algorithm communities is executed, and the color we see now is automatic, that is to say, if we see several clusters with the same color it means that all these clusters work together more closely.
  • By ascription. Suppose we choose for example Group by Department. In this case we see that all researchers in the same department are joined and become a single node with the department name, the size of the total production of the Department, and the links indicate collaborations between researchers from different departments. Note: In this case the name of each node (Community) is assigned as the name of the organization that corresponds to the grouped one: for example, if it is grouped by department, the name of each node will be the name of the corresponding Department. If we group by the institution, the color is always automatic.
  • By gender. In this case we will see two balls. All men are integrated in one of them, and all women in the other one. Each ball is sized according to the selected criteria, and all together they show the intensity of the collaborations.
Note on colors and groupings

You need to pay attention when we use the option of grouping with color options.

When we group, nodes with the same criteria are collapsed. For example, if we choose Group -> Automatic, all members of each natural community are collapsed to a node. If we would like to color for example by department, in general, we will find members of different departments inside the ball, so the color selection is ambiguous. To avoid problems, when we group nodes by any criteria, the color can not be chosen and the system does always Color -> Automatic.


Aquí se muestra en tablas y gráficas los resultados asociados al grupo seleccionado según los datos (artículos, proyectos o colaboraciones) y los parámetros seleccionados (impacto, excelencia, fondos, etc...). Existen datos estadísticos individuales y datos referidos a la red y su estructura. Para comprender bien algunos datos mostrados, conviene primero aclarar el concepto de producción interna vs producción total.

A table with the following columns is shown:

Name Internal production Total production Centrality Relevance

The columns can be sorted directly or inversely using each field. By default, alphabetical order (by name) is chosen. For long lists only the first 100 are shown.

Internal production refers to production within the current selection, and total production refers to all production.

Centrality gives measures the importance to connect groups, and relevance is a measure of how many important people the user is connected to.

The list can be downloaded as a file.

Note on domestic and total production

Let's suppose that we are analyzing a set of publication and all its signatories. Note that this is not usual in Kampal: the usual situation is to establish a set of people, and then select the articles in which at least one of the selected researchers appears. If we list all signatories of the selected articles, generally they will contain other signatories, and we will not have all their publications (the ones that have been signed with other people outside the selection). But now, for example, let's suppose we have a closed system: all persons and all publications.

In this case, all collaborative links are within the graph and if we add a node all the values ​​of the links, that number matches the total output of the node. For example, if a researcher has signed 3 publications with impact = 2.2, 1.3, 0.3, the sum of the links is 3.8 and we will say that the full impact of that person is 3.8. (We do not use the standard flag).

Let's suppose now that we have a general graph of N people. It is quite likely that when we consider his articles, there are more researchers than the originals, researchers that have signed some article with the original selection but others with different researchers. If we make the graph with the original authors and we draw their heavy links with the total number of authors, some links will not be present in the graph. If we add the links of a node, when losing one some of them, their sum will be less than the total productivity. This sum now represents the activity of the researcher in the drawn graph, indicating how much of their activity is inside and how much outside, and what is the internal and external collaboration. (A similar discussion applies to the case of projects presented below.)

When you select all and deploy Production, domestic production and external is identical, because nobody is outside.

Note: strictly for the total institution, a university, for example, there are people "outside ", but the starting point is to assume that for the total institution, the links are drawn total). So if we deploy Production with the selection of all, the domestic production and total production columns are equal.

When we look at a research institute, the previous columns are no longer the same. Total production corresponds to the sum of all the merits of each researcher, and coincides with that shown when we used all. However internal production column shows now lower values, corresponding to the activity carried out within the selection, according to what we explained above: the links with people outside the selection are cut and therefore the person who should go loses on its merits the value of the link ( Impact / number of signatures, for example).

A big difference between total production and domestic production indicates that most of the contributions are made with people outside the selection. This may be an indication that the researcher makes few collaborations in the selected group. On the other hand, if both quantities are very similar, it is an indication of poor collaboration with different people in the selected group.


For each map the system identifies the groups that collaborate with each other more actively. This group is called "Community ". Each community is identified with the name of the member with higher productivity. The productivity of each community is calculated as the sum of the productivity of its members. In the list on the left all communities appear, they are identified by the member with higher degree and its total productivity. If we click on one of them, it appears on the right of us the list of its members, with the degree of each of them. In the list of communities, each row is colored according to how the member of highest grade is colored highest in the graph.

We see a column with Production in the community and the other with Production in the selection.

These numbers tell us how much activity is performed in each community within the current selection. So a community where Production in the community is much smaller than the Production in the selection tells us that most of the contributions are made with people outside their community. Recall that community refers to all the researchers within the team that work more assiduously. It is a subset of the selection. Within this community we have also the internal production, and collaborations with people from other communities. The sum of all community members production appears in columns. If we weight only internal links, we get the Internal Production column. If we add all the links of the whole selection, we get the Selection production.

Note that the latter is different from the Total production, and it coincides with the sum of the production of all members in the community. If we drop down a community, all members appear, and we can see the selection production.

Production in the community and in the selection

Let's see the differences between the two concepts

Production in the selection

It corresponds to the sum of the internal productions of the selection considering all members of the community. This production is not the sum of the total output of its members, because we have eliminated the merits contracted with members outside the selection.

Production in the community

For each member of the community, we calculate the production made with members of the same community, eliminating the merits with members of other communities, this is what we call broken links (and of course eliminating the merits starting with nodes outside the selection). We add the production of all members of the community and we get the Community Production.

Note on communities and groups

When we do not group, the community listing is calculated as we explained above, where the color of each row in the table corresponds to the color of the graph community. For example, if we have selected automatic color, the color of each row is selected by the system; if have selected to color by department, the color is chosen by the Department of the first person in each community. Remember that we speak now of colors in communities, not the colors in the graph.

When a community is listed, it is colored using the color of his representative, which is actually the color of both members in the graph is displayed.

Let's suppose that now we select to group. First of all we have to remember that now the color option is set to automatically by the system.

Let's suppose we Group -> Automatic. In this case, each natural community collapse in s single node, an its name is the name of the more productive person. So initially we have a lower number of nodes. We can see in the graph that there are multiple nodes with the same colors. This means that the system has executed a community detection algorithm using the communities formed earlier as the basis: that is to say, it has formed Super Communities, and each of the original communities that form it are colored the same way, using a color chosen by the system. If we now go to communities, we see a list where each row corresponds to a Super Community, identified by the name of the larger community it contains, identified by its higher productivity member. If we click on one of these Super Communities, on the right we can see the list of the original communities that form the new one, each one identified by the most productive member.

When we perform other type of grouping, the behavior is similar. Let's suppose that we group by department. Each node contains all researchers of the graph that belong to the same department, and each node is identified with the name of the Department. Next, we perform a detection of Super Communities. It detects which departments work closer, and each group of departments identified is colored with the same color. Then, we will have a community with 4 apartments, one with 3, etc. If we access communities, each row in the list is a Super Community identified by the name of the department with greater productivity, and the statistics correspond to the production of eachSuper Community. By clicking on a Super Community we see the list of communities that form it, in this example, a list of departments with their productivity.


We see the historical productivity of the selection. You can see up to 3 curves:

  • Internal production. Activity developed within the selection or graph.
    • Here, the share of the merits acquired with members outside the selection is not counted.
    • In the event that we have chosen several ascriptions in the selection, the above calculation is not changed.
    • In the event that we have chosen a unique ascription in the selection, if we have selected "All the merits of the researcher" nor the above calculation is changed.
    • If you have chosen "Merits of each researcher in the period of membership in the selection ", then the merits assessed are only those obtained when the researcher was a member of the selection.
  • Total production. Total activity of the community, calculated as the sum of the total activity of all its members.
  • Reference production. Internal production of the reference institution

In general we are interested not only in the comparison of total production but also in per capita production, and for this the "Normalize" option is enabled. This option allows dividing the total productivity by the number of people that belongs to each set: the selected or total.

By default the (ever-increasing) cumulative production is shown and the annual production can also be displayed.

Network properties

They are numerical indicators of results related to the structure of the network. It tells us how is the collaboration between people, if there are well-defined subclusters, if people work with researchers that have a relevance similar to them or not, how woven it is, etc.

For each property we indicate numerically or graphically the comparison between the whole Institution (unless we are precisely total institution.)

You can access an online explanation for each property.

List of properties

Here we can find the global network properties listed, both in terms of statistics and collaborative structure. For each property the result is graphically displayed, and an explanation of what it represents can be accessed.

  • Number of members. Number of selected nodes, also indicating the total nodes
  • [Data] number. Data about publications, projects or collaborations are displayed as selected.
  • Clustering. It tells us how many of those associated with me are interrelated. In a star network, this number is very low. In a network of all with all, it is high. In other words, it indicates whether the nodes are closely related to their environment. Higher values ​​indicate a very cohesive network. [0,1].
  • Assortativity. It indicates if all researchers collaborate with the same number of people. In a clearly hierarchical network, heads do collaborate with no head researchers, but never with other heads. No head researchers only collaborate with bosses. Roles are not mixed. Thus, this is how nodes prefer to join. If positive, there is a tendency to collaborate between similar nodes. A negative value indicates tendency to collaborate between different nodes. [-1,1].
  • Modularity. It indicates whether the coherence of the network is very sensitive to small changes, for example, it indicates whether the disappearance of a single node divides the network into two or more disconnected parts. It measures the presence of well-defined subclusters in the network. subcommunities networks with intense collaborations but with few collaborations between subcommunities have high modularity. [0,1].
  • Central nucleus. Giant Cluster network, or giant component, defined as the largest set of nodes in which all are connected through collaborations. That is, the greater subnetwork where you can go from one to another through links.
  • Internal Activity Index. It indicates that part of the activity is developed in collaboration with members of the selected network. It is the ratio of internal production to total production, and it is in the range [0,1]. The value of 1 indicates that all production is internal.
  • Average distance. For each pair of nodes the the distance between them can be defined as the number of links that separate them along the shortest path. This quantity is the average of these distances.
  • Maximum diameter. If we want to communicate two nodes, the number of steps is less than or equal to this number.

Individualized report

While this functionality is accessible from the popup that appears clicking each node as described, after entering the needed ideas, and because it contains similarities with the functionality Report Selection that is accessible from the viewer.

The report graphics and images can be downloaded.


A summary of the researcher activity.

Some data about the activity and quality of publications and projects is provided.

To frame the activity of a researcher a study of the role that is playing within the current selection is made. This way we can know the relative position within a department, institute, or even within the entire institution, as selected at the time of requesting the report.

Position by activity

We define the position as the average output for production on publications and for funds generation on projects as IP. Then we calculate the percentile in which the researcher is located and that is what we show.

Position by relevance

We define it as the average of the Betweenness and PageRank . It indicates the role of the researcher ashub of traffic information and collaborations with relevant people on the network.

Important note about the distribution of the activity

The production distribution is heavily chopped in low outputs. Specifically, the probability of finding a researcher with production x obeys a law of the type p(x) = axx > 0

This means that many researchers with low outputs, and few with high outputs. A calculation of percentiles for this makes all researchers fall into the lowest deciles, giving little information. To avoid this problem, we show percentiles on logarithmic scale, so the researchers distribution by percentile is more homogeneous and gives more information.

Heat map production in publications and projects

Here we show what is the relative position of the researcher analyzed within the current selection for production in articles and projects.

For the selection, we know the production of all its members, so we know the mean and the standard deviation. To indicate the relative position of the researcher in the selection, we calculate the distance of its production to the average, in standard deviation units, that is to sayd = (x-m)/σ Thus, if the distance is very low, it will be close to the average. if d is positive and high, it will be a prominent researcher in the set. If it is negative, it means that their productivity is below average.

This calculation is also made for project funds.

Now we make a map of the entire selection with the aim of using it as a reference: for that, we calculate the amount d previous to articles and research funds for each. Then we build a two-dimensional histogram where we make boxes for each distance in units of standard deviation for articles and projects. Then we count how many researchers fall in each of these boxes. Since to draw this histogram we would need a three-dimensional plot, to visualize it better, we make a color map, where the color indicates the height, that is the number of researchers that fall into each dimensional interval.

Thus, a red color indicates that there are many researchers at these distances from the average. A blue color indicates that there are no researchers with those numbers.

Once we have the heat map drawn, we place a yellow dot corresponding to the position of the selected researcher. A middle position (indicated by two straight lines) indicates that the researcher is globally positioned in the average (Articles more projects). If placed in the upper right corner, it is excellent in both activities. If placed in the lower right corner, its publications output is good, but weak in projects, which generally is an indication that despite good activity in publications, has failed to generate projects independently as IP and it should be a warning to try to improve in this area.

A researcher at the upper right corner indicates that it performs a task relevant as IP in projects with significant funds, although this pre-eminent position is not reflected proportionally in the production of scientific publications.

Researchers at the bottom left corner indicate low activity both in articles and in projects that may correspond with persons not engaged in R&D or with people whose scientific production in general should be improved.


Information on especially high or low activities is shown both in absolute terms and in relative terms to the current selection.

El análisis de las publicaciones se hace en dos apartados. Por un lado se analizan el conjunto total de artículos registrados en unizar (Artículos Totales) y por otro los artículos indexados. En ambos casos el estudio es similar con alguna diferencia que remarcaremos oportunamente.


Specific analysis of publications. First we see some statistics.

Position by activity and relevance

Percentile analysis on the selection of scientific production, calculated from the total number or Impact of publications, again on logarithmic scale.

Positioning in selection

Here the histogram output of the selection is shown. We make boxes for production, and we count how many researchers fall into each box. The box where the researcher analyzed is placed is colored in red. For clarity, you can choose to view the output axis (the X axis) on a linear scale or a logarithmic scale.

Positions on the right indicate high productivities, on the left, low ones.

Heat map of scientific production and centrality

Now we are studying the researcher position in output-centrality within the selection. The construction is similar to the output heat map in publications-projects, but now using publications-centrality output data in the publications network.

A position in the upper right corner indicates a researcher with high production and high relevance in the network. A researcher at the lower right corner indicates a high scientific output researcher with low relevance: this may be because their collaboration is peripheral or that there is nonexistence of collaborations within the selected network; in any case it is a cause for alert.

Those at the top left are researchers with low production but high relevance. In general there are not many authors with a few publications but that work with different groups, making bridge between them. They are optimal people to foster collaboration and interdisciplinarity, and we should give special attention to them: for example if measures are taken to increase their activity this will effectively end in the cohesion of the network.


All researcher projects are analyzed. Some statistical data and additional information related to articles are provided.

It should be recalled that the collaborative project network is different from the articles network in the sense that there is a a Principal Investigator (PI) different to the remaining co-investigators (CI). In this network it is natural take into account funds for the PI, not for the CI, so that the activity goes to the first. The shown data are similar to publications data.


The observations indicate special or striking situations, both in terms of high or low activity, in each of the different analyzes. They are comparative data that indicate that the researcher analyzed is located away from the central region.

General remarks

  • You have no indexed publications.
  • The researcher does not have projects as PI.
  • The researcher has no publications in the first decile or projects in excellence as IP exceeding 100,000€.
  • The number of collaborators in publications or projects is very low.
  • Distinguished researcher. It has articles and projects and is well above average in both activities.
  • Maladjustment in the position of the decile in impact publications and projects funds as PI. (The difference in the occupied decile by publication impact and project funds is greater than 6).

In previous observations "low" means that it is placed in deciles 1 or 2 within the selection. And "very high", that it is located in deciles 9 or 10.

Observations for joint activity in publications and projects

We consider the activity in impact publications and funds as PI, and we calculate the position of the researcher in the heat map production - centrality, and it is classified according to the following chart. The zones are defined as follows

  • C1: below average in articles and projects
  • C2: above average in articles and projects
  • C3: above average in publications and below in projects
  • C4: below average in publications and above in projects
  • Z0: Absence of activity in articles and in projects as IP
  • Z1: Decile 1, 2 in publications and projects
  • Z2: Decile 9, 10 articles and projects
  • Z3: Decile 9, 10 in publications, 1, 2 in projects
  • Z4: Decile 1, 2 for publications, 9, 10 for projects

The generated observations are as follows:

  • Z0: Researcher without significant activity
  • Z1: Researcher with low overall profile
  • Z2: Excellent researcher
  • Z3: The researcher must/can ask for projects
  • Z4: The researcher must/can ask for more publications
  • C1: medium-low activity, could be improved on publications and projects
  • C2: medium-high Activity
  • C3: On average but improvable in projects
  • C4: On average but improvable in publications

Comments on articles or projects

Now we consider a particular activity, such as impact articles. We build a similar graph for each network above, but now the activity is located in the X axis, and the centrality of the researcher in that particular network on the Y axis.

The alerts are as follows:

"Network" in this case this mean the network type of the analysis and it can be:

  • Artículos indexados por impacto
  • Number of publications
  • Projects funds as PI
  • Number of projects as PI

We study each one separatelly.

  • Z0: Researcher without significant activity in the network
  • Z1: Researcher with generally low profile in the network
  • Z2: Researcher with great production and influence in the network network
  • Z3: Great production but low interaction in the network network
  • Z4: Great presence and influence in the network of network but low production. The researcher must/can increase their number of publications/projects
  • C1: medium-low position in the network
  • C2: medium-high position in the network
  • C3 and C4: Average position in thenetwork


The role played by the researcher in impact publications and project funds within its collaborative network is analyzed and observations are generated in the following cases:

  • Positive/negative alert if the researcher has a high/low position against members of their community. If it is placed in decile 1 negative warning. If it is in decile 10, positive alert.
  • Negative alert if the researcher is isolated (single member in their community).
  • Negative alert if the researcher is community leader and should increase its activity: if the researcher output is less than twice the average production of other researchers in their community.

Selection report

Through this option we access a report about the current selection, both globally and in detail for its members.

It contains numerous sections that are described below. The aim is to give an overview of the selection and to place it within the institution (eg to know the activity of a department in absolute and relative terms).

This functionality allows us to compare the different entities that make up the institution. Thus, when selecting a research institute, for example, we will be able to see all data related to the institute, a comparison of the main indicators of all institutes of the university.

The report graphics and images can be downloaded.


Selection overview. An evolution graphof scientific output and fund raising is shown by the members of the selection, both in annual and cumulative production.

Total production and internal production of the selection can be seen.

In projects it is shown the amount of PIs funds that are in the selection and the sum of these funds, minus the proportional part corresponding to project members not present in the selection.

A graph with the proportion of men and women in the selection is shown (considering all the members of the selection, ie all selected people who have either a indexed publication or participate in a project).

Membership evolution

Temporal evolution of:

  • Members belonging to the selection over time.
  • Members of the selection that are PI of at least one project.

Publication number evolution

Temporal evolution of:

  • Total number of articles of the members of the selection over time.
  • Internal output, defined as the share of the number of publications produced with researchers from the selection and during the period of membership of the signatory.

Project funds evolution

Temporal evolution of:

  • Total production: Total funds obtained by PIs of the selection over time, no matter if at the moment of the concession they belonged or not to the selection.
  • Internal production, where only the matching funds remaining within the selection are recorded, and during the period of membership of PIs to the selection.


Percentage of Women-Men in the selection.

Top 10

For each selection researcher we calculate its average position in production of items for impact and project funds, showing here the top 10.

Top 10 in the institution

We show here the members of the selection that are located in the Top10 of the institution, calculated as the average impact position articles and project funds.

Institution positioning

We draw as background the color activity graph about publications-projects for the whole institution. Then for each member of the selection, we calculate its position in this graph and draw a yellow dot.

This gives an idea of ​​the distribution of the selection activity compared to the institution: a similar point distribution indicates similar activity in the institution activity. If there is an excess of points in the upper right corner it indicates that there are more excellent researchers than in the institution (high number of articles and projects).

A similar discussion applies to other point distributions.


Relevant statistics:

First, statistical data on cumulative production in the selection is shown. The total number of publications, the publications of excellence (first decile) and the overall impact are shown.

Taking the signatures of the publications, the average number of signatories and the percentage of repeat signatories is given (in extreme cases 0 indicates that all publications are signed by one person, 1 indicates that all publications are signed by all people).

There are scientific areas where the number of signatories is lower than others (eg mathematics against astrophysics). And there are areas where it is more common sharing signatures (legal areas against experimental areas, for example). The above numbers are indicators of how the publications are signed in the selection.

Signatories repetition

Index that quantifies the repetition of signatures in the selection. At the end of 0% it indicates that all publications are signed by a single person; in the case of 100% all publications are signed by the same people. Indices greater than 1% indicate a high level of repetition. Since 100% means that all signatories have signed all publications, which is an extreme situation, it indicates lower rates and a high frequency of repetition. Amounts above 5% for this value are already indicative of a high level of repetition of signatures.

Internal-external activity

Proportion of the activity of the members of the selection that is done with other members of the selection. If we select all, it is the 100% ; low numbers indicate that members of the selection collaborate more with outsiders. Very high numbers indicate that members of the selection make almost all their activity internally without collaborations outside it.


The publication production evolution is shown by years, using different indicators, both cumulative and annual.


Percentage of men and women with indexed articles published

Top 10

Los 10 primeros de la tabla de producción científica por impacto

Top 10 in the institution

Los investigadores de la selección que están entre los 10 primeros de la institución por impacto de sus publicaciones.

Institution positioning

As a basis we have the color map of the institution researchers using scientific production and centrality. On this map we place each and every member of the team, to see if the distribution is similar or is biased.


Relevant statistics:

Firstly, statistical data on cumulative production for the selection is shown. The total number of projects and total funds understood as the sum of the funds whose PI is in the selection at the time of collection is shown. the number of relevant projects (those of more than 100 thousand euros amount) is also indicated. The average of project members is indicated.

Finally, an index of internal/external activity is calculated as follows. On the one hand, we have the total production as the sum of project funds whose IP is in the selection. On the other hand, we can subtract the previous corresponding amount (in proportion) to project members who are not in the selection, what we call internal production.

For example, let's take a project of 40 thousand euros, whose PI is in the selection and with 10 signatories in total, 4 of them outside the selection. It provides 40,000 euros to the total production and 24 thousand euros to internal production.

Taking the signatures of the publications, the average number of signatories and the percentage of repeat signatories is given (in extreme cases 0 indicates that all publications are signed by one person, 1 indicates that all publications are signed by all people).

There are scientific areas where the number of signatories is lower than others (eg Mathematics versus astrophysics). And there are areas where it is more common than in other sharing signatures (legal areas against experimental areas, for example). The above numbers are indicators of how publications are signed in the selection.


The publication production evolution is shown by years, using different indicators, both cumulative and annual.


Percentage of men and women with indexed articles published

Top 10

The first 10 of the table of scientific production by impact

Top 10 in the institution

Los investigadores de la selección que están entre los 10 primeros de la institución por impacto de sus publicaciones.

Institution positioning

As a basis we have the color map of the researchers of the institution using scientific production and centrality. On this map we place each and every member of the team, to see if the distribution is similar or is biased.


This option allows you to compare similar groups activity, particularly when one of the following types of ascription is selected:

  • Macroarea
  • Center
  • Research institutes
  • Department

When we chose a selection of these ascriptions (and only one) a comparison between all ascriptions of the same type is performed.

For example, if we select only one an institute, a comparison of all institutes is performed.

In the case of selecting a department, the comparison is not performed with everyone else, but only with the same macroarea departments to standardize and simplify the comparison.

Comparison publications and projects

Absolute production (impact and equity, respectively) of each assignment is shown in a bar graph. You can see the standard chosen by the membership, ie production per capita data.

Evolution of articles, projects and membership and IPs

the evolution of commodity production and project funds shown. In both cases the chart type can be chosen to be displayed.

En el caso de artículos puede verse la producción en términos de impacto, en número o solamente los artículos del primer decil (excelencia). En el caso de proyectos pueden verse los de investigación, transferencia. En ambos casos puede verse la producción acumulada o anual, y normalizarse por el número de miembros de la adscripción.

Average number of signatories

It is shown for both publications and projects.

Internal versus external activity

Relations between these activities for publications and projects.

Percentage of men/women

In the case of articles it refers to the percentage of signatories; in the case of projects, PIs are compared.


The particularly significant individual or collective situations are indicated, either for being well above or well below average, or other special circumstances relating to the structure of the network.

General recommendations

An alert is launched in the following cases

  • Relative position low on the selection: it warns of a particularly low situation if we compare it with the rest of ascriptions. Lower means that it is in the percentile 20 or below. It is calculated for publications (total impact, number of publications, publications of excellence) and projects (funds and number).
  • High relative position of the selection: similar to the above, but when the activity is equal to or above the percentile 80.
  • Descending activity: it warns about a consecutive drop in activity during the last full years. Warnings are given for number of publications, impact, excellence articles, number of projects, and project funds.
  • Ascending activity: It warns about a consecutive rise in activity during the last two years, for the activities of the previous section.
  • Mismatch of internal and total output: It is notified when the percentage of internal activity to the total activity is less than 10% , or greater than 90% . It applies to the number of publications and number of projects.
  • Absence of representatives in Top10: Absence in the top 10 of the institution of members of the selection.

People close to the top 10 of the institution

Selection researchers located between top 10 and top 40 of the institution are shown. The position is calculated using the average impact of publications and projects funds as IP.

Researchers liable for improvement

Researchers that are below average activity from the institution in number of publications or fund raising.

Community leaders liable for improvement

It indicates whether the activity of the head of the community is less than twice the average of the activity of other members of their community.

It is calculated for the impact of articles and for project funds.

Communities liable to improve its internal activity

It indicates whether the community makes a low internal activity compared to other communities in the selection. Low means in the 20th percentile or below.


It allows you to view all the merits of the members of the selection: it will be a list of all publications, projects, patents... according to the type of analysis in progress, with a selection of information for each entry.

The list can be downloaded to a file. The file can be opened with Excel or other applications, but it must be emphasized that the decimal part is separated with a period (.).