Entering into medical school at 16 years old is the beginning for me of finding my “own tribe”, of where I belong in the context of what I wanted to become in the future. I became active in the student movement starting in those early years and joined a progressive health organisation that aligned itself with the organised groups of the working class - collective of farmers in the rural areas, unions of factory workers in the cities, associations of nurses and allied medical professionals in the hospital I trained in. With the internet still relatively new at that time and mobile phones still uncommon, we communicated mainly through notes and passing on of messages through our networks of comrades. Within our own student organisation, we would use a logbook stationed in a location that was allocated to us by the university as our “tambayan” or hangout. This was our social media during those times. When passing through our hangout after a 24-hour duty at the hospital, we would check the pages of the logbook to appraise ourselves of what went on with our friends and colleagues since we last visited. We would post ideas, jokes, poems, etc. This was also our means to announce a meeting, a celebration, or an educational discussion. These events were usually always not just us students but with invited guests from our allies. Representatives from the collective of farmers would speak to us about what was going in terms of issues of land grabbing by wealthy families, or the challenges poised by new governments policies on produce and livestock. Members of the factory workers union would share with us union-busting strategies implored by companies to stymie their efforts and campaigns for fairer minimum wages and more human working conditions. Our nurse and allied medical professional colleagues would speak to us about challenges in the hospital that we don’t see as students - policies that impact nursing staff and as such threaten patient safety and welfare.

These logbooks were also our means to receive and send out calls for help and support. I remember a call for help from our union allies after most of those in their union were summarily dismissed without cause by the factory they worked for. To them, this was clearly a way to bust their union but outwardly this was reported as them being unprofessional, always late for work, and cause a lot of problems that affect the productivity of the factory. Through the help of allied lawyers, the union filed a lawsuit against the factory and won their case in the lower courts. The factory brought the case to the court of appeals which sided with the factory owners immediately triggering a review of the case by the Supreme Court. In fear that the money and power that the factory owners have and seeming influence they had on the higher courts, our union colleagues went on hunger strike over the 3 month period that the Supreme Court was hearing this case. The calls of help we received via those logbooks was to monitor the health of our colleagues who were on hunger strike as a form of solidarity. Over a three month period, we organised ourselves to make sure that at least one of us was with them at their picket lines to check in on them and see that they were OK. The Supreme Court sided with our colleagues and the factory owners were ordered by court to give them back pay plus damages as a result of their summary dismissal without cause.

These events shaped me and my consciousness and will direct me to doing more community health and public health work, working alongside communities, supporting their causes whilst they supported ours. Such work is not always safe. Those with power fear such community organisation and those involved get identified and threatened. In my last role back home in the Philippines as a community organiser and community doctor in a war-torn municipality in the southern Philippines, I faced such threats that eventually forced me to leave the country. Since then, I’ve never felt the same sense of community as I have. Not until I met and got to work with, and be involved in the work that our invited guest today does.

To say that it is an honour to be able to introduce to you Dr Noam Ross is an understatement.

Noam Ross is Executive Director of rOpenSci, a non-profit organization that helps researchers develop and use R packages to better produce and share scientific data, methods, and results. Noam was a founding member of the organization’s software peer-review program, and also oversees rOpenSci’s work in training, mentorship, scientific infrastructure and multilingualism. Noam is a scientist whose work focuses on studying and forecasting the circulation of infectious diseases and their emergence in wildlife populations. He is also a co-founder of Grant Witness, an organization that tracks changes in federal science funding policy to inform journalism, litigation, and activism. He holds a Ph.D. in Ecology from UC Davis.

They say not to meet your heroes. I say they haven’t met a person like Noam.

Ladies and Gentlemen, Dr Noam Ross.

We believe that lasting impact in nutrition starts with strong communities, evidence-based practice, and leaders who understand both. That’s why we’re excited to announce that Emmanuel Mandalazi, an experienced nutrition and public health specialist, is stepping into the role of Director of Community at nutriverse.

With over 15 years of experience spanning emergency nutrition, public health, and research, Emmanuel brings a rare blend of technical depth and community-centred thinking to our growing community.

The nutriverse community of practice brings together nutrition data analysts, researchers, and practitioners who are committed to learning from one another and improving how nutrition data is analysed and applied. It provides a shared space to exchange methods, tools, and real-world experiences across research, policy, and practice.

Through peer learning, collaborative problem-solving, and open discussion, the community supports rigorous, reproducible analytics while staying grounded in practical impact. The goal is to strengthen individual practice, build collective capacity, and advance better nutrition decisions through better use of data.

We are committed to cultivating a welcoming, globally diverse community of software users and developers, data analysts, researchers, and practitioners. Whether you’re a long-time contributor or just getting started, we are dedicated to ensuring this is a safe and supportive space for you. We are committed to fostering a safe, inclusive, welcoming, and harassment-free experience for all.

We strive to foster a community grounded in shared values, a community where people feel comfortable exploring ideas and asking questions. We encourage everyone to assume good intent and recognise the competence of those they engage with.

To this end, we have a Code of Conduct by which we expect members of the community to abide by.

When we started nutriverse in 2018, we firmly believed that health and nutrition data can and should work better. Not through one-off, limited, and fragmented data and analysis tools but through transparent, coordinated data systems, robust and performant data tools, and a community of practice that is empowered to participate, utilise, and govern the use of this data.

This is why we are incredibly proud to share that Abdoul-Aziz Goza, one of the three stalwarts of nutriverse when we started in 2018, is taking on the role of Executive Director.

With over 15 years of experience in large-scale child nutrition data systems, Aziz is an expert in survey design, health and nutrition data quality assurance, and advanced statistical analysis. He has supported large-scale child nutrition systems across Africa and Asia, strengthening national platforms, and designing complex surveys.

As a former UNICEF Emergency Health and Nutrition Specialist in Niger, he brings experience and expertise in health and nutrition assessments such as KAP, SMART, and SQUEAC/SLEAC surveys in humanitarian contexts.

Aziz’s field experience stretches across the Sahel and beyond in countries such as Senegal, Mauritania, Mali, Burkina Faso, Cameroon, Kenya, Ethiopia, and Niger where he’s led surveys, training, and data system strengthening efforts. It’s this blend of technical expertise and on-the-ground experience that makes his insights so valuable.

Aziz’s strategic leadership marks an important step forward for nutriverse as we continue building not just the tools and systems around health and nutrition data but most importantly an empowered community of practice that actively participates in, leverages, and stewards the use of this data.

Join us in celebrating this important milestone for nutriverse and in supporting Aziz’s tenure as Executive Director!

On the last day of January 2018, I made my first git commit to the {zscorer} package in the GitHub organisation account that I named nutriverse. A little over eight years later, the nutriverse now has four R packages published on the Comprehensive R Archive Network (CRAN) and another four in various stages of active development. What started out as my desire to learn how to write R packages that I can use and re-use for every nutrition research and nutrition analytics project that I worked on turned into the beginnings of an ecosystem of tools that I and a handful of other nutrition researchers, data analysts, and practitioners use routinely. Now, in 2026, we are transitioning nutriverse from its small “hobbyist phase” and grow it towards institutional sustainability.

We are happy to share that nutriverse is now fiscally-hosted by the Open Source Collective (OSC), a non-profit fiscal host that provides essential financial and legal infrastructure for thousands of open source projects.

What this means is that as nutriverse’s fiscal host, OSC will be supporting nutriverse in holding the project’s funds, handling taxes, signing legal contracts, and managing accounting. This structure allows us to focus on code and community rather than bureaucracy.

Our software development team is pleased to announce that {sleacr}, an R package for Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC), has been accepted to the Comprehensive R Archive Network (CRAN) today!

get_sample_n(N = 600, dLower = 0.5, dUpper = 0.8)

$n
[1] 19

$d
[1] 12

$alpha
[1] 0.06446194

$beta
[1] 0.08014249

get_sample_d(N = 600, n = 16, dLower = 0.5, dUpper = 0.8)

$n
[1] 16

$d
[1] 10

$alpha
[1] 0.07890285

$beta
[1] 0.1019738

village_list

# A tibble: 1,001 × 4
      id chiefdom section village  
   <dbl> <chr>    <chr>   <chr>    
 1     1 Badjia   Damia   Ngelehun 
 2     2 Badjia   Damia   Gondama  
 3     3 Badjia   Damia   Penjama  
 4     4 Badjia   Damia   Jawe     
 5     5 Badjia   Damia   Dambala  
 6     6 Badjia   Fallay  Bumpewo  
 7     7 Badjia   Fallay  Pelewahun
 8     8 Badjia   Fallay  Pendembu 
 9     9 Badjia   Kpallay Jokibu   
10    10 Badjia   Kpallay Kpaku    
# ℹ 991 more rows

get_sampling_list(village_list, 40)

survey_data

# A tibble: 14 × 7
   country      province     district      cases_in cases_out rec_in cases_total
   <chr>        <chr>        <chr>            <int>     <int>  <int>       <int>
 1 Sierra Leone Northern     Bombali              4        26      6          30
 2 Sierra Leone Northern     Koinadugu            0        32      6          32
 3 Sierra Leone Northern     Kambia               0        28      0          28
 4 Sierra Leone Northern     Port Loko            2        28      0          30
 5 Sierra Leone Northern     Tonkolili            1        27      5          28
 6 Sierra Leone Eastern      Kono                 2        14      3          16
 7 Sierra Leone Eastern      Kailahun             4        30      3          34
 8 Sierra Leone Eastern      Kenema               8        26      4          34
 9 Sierra Leone Southern     Pujehun              6        21      1          27
10 Sierra Leone Southern     Bo                   6        16      8          22
11 Sierra Leone Southern     Bonthe               7        34      2          41
12 Sierra Leone Southern     Moyamba              6        34      0          40
13 Sierra Leone Western Area Western Area…        6        40      5          46
14 Sierra Leone Western Area Western Area…        2        18      0          20

with(
  survey_data, 
  lqas_classify(
    cases_in = cases_in, cases_out = cases_out, rec_in = rec_in
  )
)

   cf tc
1   0  1
2   0  0
3   0  0
4   0  0
5   0  0
6   0  1
7   0  0
8   1  1
9   1  1
10  1  1
11  0  0
12  0  0
13  0  0
14  0  0

## Simulate population ----
lqas_sim_pop <- lqas_simulate_test(
  pop = 600, n = 40, dLower = 0.6, dUpper = 0.9
)

## Get classification probabilities ----
lqas_get_class_prob(lqas_sim_pop)

                    Low : 0.9548
               Moderate : 0.834
                   High : 0.8405
                Overall : 0.9071
Gross misclassification : 0

plot(lqas_sim_pop)

pop_data

# A tibble: 14 × 2
   district               pop
   <chr>                <dbl>
 1 Kailahun            526379
 2 Kenema              609891
 3 Kono                506100
 4 Bombali             606544
 5 Kambia              345474
 6 Koinadugu           409372
 7 Port Loko           615376
 8 Tonkolili           531435
 9 Bo                  575478
10 Bonthe              200781
11 Moyamba             318588
12 Pujehun             346461
13 Western Area Rural  444270
14 Western Area Urban 1055964

pop_df <- pop_data |>
  setNames(nm = c("strata", "pop"))

estimate_coverage_overall(
  survey_data, pop_data, strata = "district", u5 = 0.177, p = 0.01
)

$cf
$cf$estimate
[1] 0.1257481

$cf$ci
[1] 0.09247579 0.15902045


$tc
$tc$estimate
[1] 0.1706466

$tc$ci
[1] 0.1371647 0.2041284

check_coverage_homogeneity(survey_data)

ℹ Case-finding effectiveness across 14 surveys is not patchy.

! Treatment coverage across 14 surveys is patchy.

$cf
$cf$statistic
[1] 20.1292

$cf$df
[1] 13

$cf$p
[1] 0.09203514


$tc
$tc$statistic
[1] 33.10622

$tc$df
[1] 13

$tc$p
[1] 0.001642536

To cite sleacr in publications use:

  Ernest Guevarra, Mark Myatt (2026). _sleacr: Simplified Lot Quality
  Assurance Sampling Evaluation of Access and Coverage (SLEAC) Tools_.
  doi:10.5281/zenodo.7510931 <https://doi.org/10.5281/zenodo.7510931>,
  R package version 0.1.3, <https://nutriverse.io/sleacr/>.

To cite the SQUEAC and SLEAC Technical Reference in publications use:

  Mark Myatt, Ernest Guevarra, Lionella Fieschi, Allison Norris, Saul
  Guerrero, Lilly Schofield, Daniel Jones, Ephrem Emru, Kate Sadler
  (2012). _Semi-Quantitative Evaluation of Access and Coverage
  (SQUEAC)/Simplified Lot Quality Assurance Sampling Evaluation of
  Access and Coverage (SLEAC) Technical Reference_. FHI 360/FANTA,
  Washington, DC.

To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.

We’ve just released a patched version of {nipnTK} (v0.2.2), our nutrition data quality toolkit R package, on CRAN. This was a general upkeep and maintenance update focused on tidying up documentation including the package website which now sports the new nutriverse colour and branding scheme. Existing users of the {nipnTK} package will be able to continue using the package as before after updating. If you work with nutrition data specifically anthropometric measurements data, consider looking into {nipnTK} and give it a try in your nutrition data processing workflows.

We’ve also spruced up the development versions of the {micronutr} and the {anthrocheckr} packages. Again, these updates are all general upkeep and maintenance focusing on package documentation and website. We plan to get a minor version release on CRAN for {micronutr} by end of March 2026 that will include new functions for adjusting serum micronutrient biomarkers for inflammation using the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) Method. For {anthrocheckr}, we plan to get a first CRAN release by the end of February 2026.

We have also been working on our Community-based Management of Acute Malnutrition (CMAM) coverage assessment set of packages - {squeacr} and {sleacr}. We have been developing these packages for more than 3 years now and we are happy to report that {sleacr} is ready for submission to CRAN. We will let you know once it has been accepted. {squeacr} on the other hand is on its final steps of development leading to a first release. We are aiming for it to be ready for submission latest by the end of February 2026.

These are all the updates for now!