Data collection and analysis tools for food security and nutrition

particular concern is that 
this is true also for the 
scientific community
,
where traditional mental 
frameworks and research tools (experimenting, 
quantification, surveying, interviewing, conducting 
participant observation, ethnography, etc.) are being 
challenged by emerging new tools (data mining, 
web scraping, text mining, sentiment analysis, 
etc.),
11
which have not yet sufficiently permeated 
academic curricula. This brings to the fore 
the 
need to invest in capacity development at all 
levels, starting even in primary school and 
continuing through specialized training of 
professionals working in public and private 
data-driven institutions
.
INSUFFICIENT RESOURCES 
FOR DATA COLLECTION AND 
ANALYSIS
Insufficient resources refer to both financial 
and human resources. These are discussed 
separately in the sections that follow.
11 Consider, for example, developments in the theory of 
measurement that address the problem of quantification in the 
human behavioural and social sciences (Bond, Yan and Heene, 2020; 
Mari 
et al., 2017), or the epistemological implications of big data for 
research (Kitchin, 2014b).

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DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION
FINANCIAL CONSTRAINTS
Insufficient allocation of financial resources to 
agricultural development and FSN programmes is 
a long-standing concern for many countries The 
inability to allocate needed financial resources 
from the public budget to FSN initiatives has been 
noted and linked to the limited tax base in many 
low- and middle-income countries (Tinsley, 2010), 
with repercussions on the production of official 
statistics. Further exacerbating the lack of data 
and information for effective FSN policies, national 
research funding programmes in these countries 
are also less likely to invest in research to promote 
food security, nutrition and health, as these are 
considered less marketable than research in 
other fields (Neema and Chandrashekar, 2021). 
Lack of public funding has clear consequences 
for FSN data. Kalibata and Mohamedou (2021) 
estimate that 90 percent of the national statistics 
offices (NSOs) in low- or lower-middle-income 
countries lack agricultural data due to funding 
limitations. Calls for external support have 
been less effective than hoped. The review of 
financing data for low- and middle-income 
countries, conducted by the Secretariat of the