Collaborations (between institutions) that resulted from grants for biomedical research

What you see

The data visualization shows the nature of collaborations between research institutions that resulted from grants for health research from major funders in 2022. Note: (i) Not all direct grants by these funders resulted in collaborations and (ii) one direct grant may have resulted in one or more collaborations.

The first two charts show how many collaborations resulted from direct grants by funder (chart A) and how these grants that resulted in collaborations are divided according to health category (chart B). Charts C-G compare direct grants to the collaborations linked to them with respect to country income group, WHO region, country, grant type and institution.

Grants for the WHO list of  'neglected tropical diseases' or 'R&D Blueprint pathogens' can be filtered by clicking on the respective circular button at the top left of the data visualization.

Points to note:

• 40% (33,736) of collaborations resulted from direct grants funded awarded by the Canadian Institutes of Health Research, followed by 24% (19,929) of collaborations from grants awarded by the United States of America’s National Institutes of Health (NIH) which funded the greater number of collaborations in 2020; the NIH also gave the highest average grant duration (approx. 7.5 years) (chart A).
• 59% (42,698) of collaborations were for research on noncommunicable diseases, followed by communicable, maternal, perinatal and nutritional conditions (31%; 22,522) and injuries (4%; 3,031) (chart B1).
• Of all grants on noncommunicable diseases (select this disease in chart B1), the highest number of collaborations were for research on neuropsychiatric conditions (14,299) (chart B2).
• Of all grants for neglected tropical diseases (select this category at the top left of the visualization), the top three disease/conditions on which research collaborations occurred were noma (150), dengue (134), and chikungunya virus disease (78) (see chart B3).
• Of all grants for R&D Blueprint pathogens (select this category at the top left of the visualization), the top three disease/conditions on which collaborations occurred were research on COVID-19 (4,517), followed by Ebola virus disease (116), and severe acute respiratory syndrome (SARS) (41) (see chart B3).
• 97.9% (20,691) of direct grants that resulted in collaborations had been awarded to recipients in high income countries (chart C, left). Of these, 87.2% (72,694) collaborations were with others in high-income countries (select ‘high income’ in chart C, left and then see chart C, right). In fact, for each income group, collaborations were most likely to be with others in the same income group. Thus, for example, grant recipients in high income countries were most likely to collaborate with others in high income countries.
• Overall, Canada is top of the list of countries with which grant recipients collaborated (chart E, right).

Exploring the data further

• Select a funder, a WHO region, a country or any other category (by clicking on a bar in a chart or a cell in a table) to filter data for the desired selection in the other charts.
  • For example, selecting South Africa, through its 119 direct grants (chart E, left), its collaborations were mostly with institutions in South Africa (222) followed by the United Kingdom (48) and the Netherlands (13). (chart E, right). 
• Hover the cursor on a data element (a bar or a cell) to see more information in a popup window (for example, percentages and other relevant categorizations). The popup information is updated automatically for any of the selections made by filtering the data in the previous step.
• Hold the ‘Ctrl’ key to select more than one option, for example two regions.
• Undo a selection by clicking the ‘undo’ or ‘reset ‘symbol near the bottom of the page or by clicking the same element again.

Scope, analysis and limitations of the data

Scope

• The World RePORT is hosted by the United States of America’s National Institutes of Health and managed through a steering committee of the agencies providing data.
• Not all funders who provide data to World RePORT report on a yearly basis. For example, at the time of this analysis, only 12 funders had reported data for grants awarded in 2022
• This 2022 data analysis included grants reported by the Global Alliance for Chronic Disease (GACD) to the World RePORT. They include grants that have not been directly reported to the World RePORT or grants from funding agencies which do not currently contribute data to World RePORT.
• The World RePORT data include direct (primary) grants provided to recipient institutions as well as collaborations with other institutions resulting from these grants (indirect grants administered by recipient institutions). This data visualization focuses on collaborations with the primary recipient institutions.
• Not all funders that reported data for 2022 grants are included in this analysis, either because they did not report collaboration grants or available information on direct grants that resulted in collaborations with other countries or institutions does not allow linking the collaborators with the recipients of the direct grants.

Analysis

• Automated data mining was used to generate information on the type of grant and health category using text-based data fields for each grant. 
  • To determine the type of grant, synonyms for the type of grant categories listed in the data visualization were extracted from the title or abstract (if available) of the grant record.
  • To assign a health category to each grant, the Observatory’s compiled list of disease synonyms was used as described below. The list was compiled using as a base the Unified Medical Language System (UMLS) and the International Classification of Diseases. This was complemented by synonyms drawn from the data, mostly to account for errors in data entry such as spelling errors or use of abbreviations.
  • An automated algorithm was applied to two data fields, the grant’s title and abstract, using the list of disease synonyms to generate the disease classification field used in this analysis. The algorithm stops if a match is identified using the title field, if not a match using the abstract field is pursued. The first match closer to the beginning of the text field was selected. This was considered the primary disease focus of the grant. It is possible that a grant has more than one disease focus; this is not captured in this analysis.
  • The algorithm was refined through various iterations and will continue to be refined in future updates but as with any automated algorithm, it is likely that some grants were not correctly matched. . A full description of the methods and approach is available in this paper: Resource allocation for biomedical research: analysis of investments by major funders.

Limitations of the data

• For some funders, especially those with relatively new or fixed data collection platforms, the data do not accurately reflect the scale of research investments or collaborations between recipients of grants. Most funders do not include their intramural investments which can be substantial, notably the NIH and the Institut Pasteur, the latter is estimated by the data source to represent only around 8% of their entire grants
• This analysis will be updated when new data becomes available; however, time lags with the scheduled updates by the data source are inevitable. Accuracy and completeness of the information is the responsibility of the data source, see  terms and conditions of use.
  

Data sources

• World RePORT
• Country income group classification: The World Bank

Previous Versions

February 2023 | January 2022 |  January 2021 |  December 2020 |  May 2019