# How the EDCOM Dashboard Data Was Cleaned

How the EDCOM II ECCD dashboard data were extracted and cleaned: 1,486 logged changes, 11 checks passed and 99.997% of schools linked to a barangay.

This is the Markdown copy of [How the EDCOM Dashboard Data Was Cleaned](https://nash.com.ph/blog/edcom-eccd-dashboard-data-cleaned), an article on the NASH Reviewer Blog by Mark Anthony Llego, published Oct 27, 2026, updated Nov 4, 2026. Topics: Open data, DepEd data. NASH Reviewer is an independent practice platform not affiliated with DepEd, BHROD, or NEAP.

The EDCOM dashboard data come from the early-childhood care and development (ECCD) dashboard of the Second Congressional Commission on Education (EDCOM II), a Tableau workbook that puts DepEd's end-of-year reading results beside data on child nutrition, child development centers and programs. NASH Reviewer extracted the six tables inside the workbook, 135,406 rows in all, cleaned them, and recorded every change in a cleaning log of 1,486 entries. The cleaned data passed all 11 validation checks, and the national totals did not move: 5,127,670 learners assessed and 511,753 struggling readers in 38,679 public schools.

The problems quietly shifted regional figures. Sulu was still labeled BARMM in three tables after the 2024 Supreme Court ruling that it is not part of the region, garbled characters broke barangay names, and some schools could not be placed in any barangay, so the dashboard's regional figures fell short of its national totals. After cleaning, 38,678 schools (99.997%) are linked to a barangay and the regional figures add up. Every file is free to download. NASH Reviewer is not affiliated with EDCOM II or DepEd.

## Key findings

- The dashboard's workbook holds 6 tables with 135,406 rows, behind two dashboards and 27 worksheets; all of them were extracted without filters and matched the workbook's own row counts.
- The cleaned data passed all 11 validation checks, and the national totals are unchanged at 5,127,670 learners and 511,753 struggling readers in 38,679 schools.
- The cleaning log lists 1,486 changes; the largest rule moved 547 Sulu records to Region IX, and the next repaired 226 values of garbled text.
- 38,678 of 38,679 schools (99.997%) are now linked to a barangay: 299 links were added beyond the dashboard's exact name match, and 98 schools were located through a predecessor school, 397 additions in all.
- One school remains unlinked because its barangay was created too recently to have a PSGC code in the reference; it is still counted in its region and division.
- Coordinates found by research for 114 schools are less precise, a median 5.2 km from the barangay center against 0.73 km for DepEd's own coordinates flagged valid; they are flagged, not dropped.

## What the EDCOM II ECCD dashboard holds

The [EDCOM II ECCD dashboard](https://edcom2.gov.ph/eccddashboard/) is a public Tableau workbook published by [EDCOM II](https://edcom2.gov.ph/). It has two dashboards, Overview and Literacy Map, built from 27 worksheets. Six tables stored inside the workbook feed every worksheet, chart and map on the dashboard.

The tables come from four sources, each dated on the dashboard. Reading results are [DepEd](https://www.deped.gov.ph/)'s Comprehensive Rapid Literacy Assessment (CRLA) at the end of SY 2025-26, as of 10 May 2026, for public schools. Stunting comes from Operation Timbang Plus 2025 of the Department of Health and the [National Nutrition Council](https://nnc.gov.ph/) (DOH/NNC), as of 2 March 2026. The list of barangays without a child development center (CDC) comes from the [DILG](https://www.dilg.gov.ph/) Community-Based Monitoring System, as of 17 September 2025. A short list of program records covers literacy, nutrition and childcare interventions.

Two further tables tie the sources together. A barangay reference built on the Philippine Standard Geographic Code (PSGC), the government's official list of places and their codes, gives each barangay its code, municipality, province and region. A school location table gives each school's coordinates and geography. The table below lists the six tables as extracted.

| Table                   |   Rows | Source                                           |
|:------------------------|-------:|:-------------------------------------------------|
| Barangay matching       | 42,025 | PSGC barangay reference with match keys          |
| Barangays without a CDC |  4,604 | DILG CBMS, as of 17 Sep 2025                     |
| Programs                |    100 | Intervention program records                     |
| Stunting 2025           |  1,743 | DOH/NNC OPT Plus 2025, as of 2 Mar 2026          |
| School locations        | 48,255 | School coordinates and geography                 |
| Reading assessments     | 38,679 | DepEd CRLA, end of SY 2025-26, as of 10 May 2026 |

The CRLA is DepEd's teacher-administered reading screen for Grades 1–3. The dashboard counts each learner once, as Grade 1 Mother Tongue plus Grade 2 Filipino plus Grade 3 English, and defines struggling readers as Low Emerging plus High Emerging. Both definitions are kept unchanged.

### How the tables were extracted

The tables were read from the workbook file behind the dashboard and its Tableau Public mirror, not copied from the screen. No filters were applied, so every region, division and school is included, not only the view the dashboard opens on. Each table's row count matches the count the workbook stores for it, and each exported file has a checksum, so any later change would show.

The raw extract in the download is a byte-for-byte copy of that export, in which the text `\N` marks an empty field.

## The checks the cleaned data passed

The cleaned tables were accepted only after passing 11 automatic checks. All of them passed:

- Every reading assessment row in the raw extract is still present.
- Each school ID appears only once.
- The national number of learners assessed is unchanged.
- The national number of struggling readers is unchanged.
- Row counts reconcile: cleaned rows plus dropped rows equal extracted rows in every table.
- Every record that can be placed has a region.
- Every school location record still without a region has no division, province or linked barangay from which one could be found.
- Every barangay's region code agrees with its region label.
- Every stunting row whose region was relabeled corresponds to a known boundary change.
- The regional overview adds up to the national totals.
- Every school not linked to a barangay is on the list of links to review.

The cleaned data hold 5,127,670 learners assessed and 511,753 struggling readers in 38,679 schools, exactly as in the raw extract. The cleaning changed where some schools sit on the map and in which region they are counted, but no learner count. The dashboard counts a school in a region only through its barangay link, so a school without a link counted nationally but in no region. The cleaned regional overview counts each school in the region of its own reading record, so the regional figures add up whether or not a school is linked.

## How the EDCOM dashboard data was cleaned, rule by rule

The cleaning never estimates data: no missing count, rate or name is filled in from a model or an average. Each rule does one narrow thing: it adds a column or flag, drops a row, repairs garbled text, or fills a region or link from other evidence in the data or from documented research. Every change is a row in the cleaning log, with the table, record, rule, field, old and new values and the reason. The table below counts the 1,486 logged changes by rule, including the links and coordinates explained in the next section.

| Change                                             |   Records |
|:---------------------------------------------------|----------:|
| Sulu moved to Region IX                            |       547 |
| Garbled text repaired                              |       226 |
| Barangay link confirmed by research                |       149 |
| Coordinates from research                          |       114 |
| Barangay link by validated PSGC code               |       110 |
| Location from a predecessor school                 |        98 |
| Region relabeled from the PSGC code (NIR and Sulu) |        86 |
| Region filled from the province                    |        68 |
| Region filled from the municipality                |        30 |
| Barangay link by normalized name                   |        26 |
| Duplicate barangay code dropped                    |        15 |
| Two-way name match resolved by code                |        14 |
| Header row dropped                                 |         2 |
| Region filled from the barangay                    |         1 |

![Horizontal bar chart of the 1,486 logged cleaning changes by rule, on a log scale: Sulu moved to Region IX 547; Garbled text repaired 226; Barangay link confirmed by research 149; Coordinates from research 114; the smallest are header row dropped (2), region filled from the barangay (1).](https://nash.com.ph/blog/_astro/edcom-dashboard-cleaning-changes.-HnCnX4-_1Gagw9.webp)

*Figure 1. Logged cleaning changes by rule, on a log scale, from the move of Sulu to Region IX down to a region filled from a single barangay link. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/edcom-dashboard-cleaning-changes.png).*

### Region names standardized to the 18 regions

The sources spell regions in different ways. Every cleaned table gets a `region_clean` column with one name for each of the 18 regions of the PSGC, and a crosswalk lists each source label and its region. Because this adds a column rather than overwriting the source label, it is not counted in the log.

### Negros Island Region relabeled from PSGC codes

The Negros Island Region (NIR), created by law in 2024, groups Negros Occidental, Negros Oriental and Siquijor. The stunting table still labeled these areas Region VI or Region VII, although their PSGC codes already place them in NIR, and it labeled Sulu's municipalities as BARMM. The cleaning relabeled 86 stunting rows, both the NIR and the Sulu rows, to the region the PSGC gives them, since the PSGC is the authority on where an area belongs.

### Sulu moved to Region IX

In 2024 the Supreme Court ruled that Sulu is not part of BARMM, and the PSGC now codes the province to Region IX (Zamboanga Peninsula). The barangay reference and DepEd's reading data already follow this. Besides the stunting rows above, the list of barangays without a CDC and the school location table still labeled Sulu as BARMM, and the cleaning moved 547 of their Sulu records to Region IX, the largest rule in the log. Without it, Sulu would fall in different regions depending on the table.

### Garbled text repaired

Garbled text, often called mojibake, appears when text saved in one character encoding is read as another. A letter such as Ñ turns into a pair of odd characters, so a barangay named "BAÑAS" appears as "BAÑAS". The cleaning repaired 226 such values, almost all of them barangay names in the school location table. Because the dashboard links schools to barangays by exact name, a garbled name matches nothing, and some schools could be linked only after the repair.

### Header rows and duplicate barangay codes removed

The stunting and school location tables each held a row that repeated the column names (2 in all), and both were dropped. The barangay reference listed 15 codes twice. Most were exact copies; the rest differed only in an alternate spelling, which is kept because other tables use it for matching. The barangay reference now has 42,010 rows (from 42,025), the stunting table 1,742 (from 1,743) and the school location table 48,254 (from 48,255). The reading table is unchanged at 38,679 schools.

### Missing regions filled from the province or municipality

Some school location records had no region label. A region was filled only when the evidence pointed to one region and no other, in this order: from the linked barangay (1), then from the province when every other school in it is in the same region (68 records), and last from the municipality when its name exists in only one region in the PSGC (30). Records with no geography at all were left without a region.

## Linking schools to their barangays

The barangay link places a school next to the stunting and CDC data for its area and decides its region on the dashboard. The cleaning keeps the dashboard's linking rule and adds narrower rules for the schools it could not place. The table below shows how every school is now linked.

| How the school was linked to its barangay   |   Schools | Share   |
|:--------------------------------------------|----------:|:--------|
| Dashboard's exact name match                |    38,379 | 99.22%  |
| Confirmed by research                       |       149 | 0.39%   |
| Validated PSGC code                         |       110 | 0.28%   |
| Normalized name                             |        26 | 0.07%   |
| Name match resolved by code                 |        14 | 0.04%   |
| Not linked                                  |         1 | 0.00%   |

The first rule is the dashboard's: a school's barangay, municipality and province names must match exactly one barangay in the reference. It links 38,379 schools, some only after garbled names were repaired. When the exact names matched more than one barangay, the school's own PSGC code chose between them, which settled 14 schools.

The next rule uses the school's own barangay code, but only where the source marked it as validated, and it linked 110 schools. Unvalidated codes proved unreliable: when one disagreed with the name match, the school's own region sided with the name match nearly every time. In 30 schools the name points to one barangay and a validated code to another; both codes are kept, and the name match is used.

The third rule normalizes names before matching. It ignores alternate names in brackets, so "BALINDONG (WATU)" can match "BALINDONG", and requires barangay, municipality and province to agree on exactly one barangay. It linked 26 schools. Another 149 links were confirmed by NASH Reviewer's own research in school records, maps and directories. Each is in the cleaning log with its source and agrees with the PSGC reference.

![Bar chart of how 299 schools beyond the dashboard's 38,379 exact name matches were linked to their barangay: confirmed by research 149; validated psgc code 110; normalized name 26; name match resolved by code 14; not linked 1.](https://nash.com.ph/blog/_astro/edcom-school-barangay-links.raHrquQM_y7y1S.webp)

*Figure 2. How schools beyond the dashboard's exact name match were linked to their barangay, by method, with the single school that remains unlinked. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/edcom-school-barangay-links.png).*

Together these rules added 299 links beyond the name match. With the 98 schools located through a predecessor school, the cleaning made 397 additions in all, and 38,678 schools (99.997%) are now linked. The one school left unlinked sits in a barangay too new to have a code in the PSGC reference; it still counts in its region and division.

### Schools located through a predecessor school

When an elementary school is converted into an integrated school, it often gets a new school ID with no location record under it. Its name then matches an older record in the same division that no assessed school uses. A school ID is meant to be permanent, so a name match alone is not enough: the older record became the school's location only where research documented the conversion.

Research confirmed 98 such conversions. For 2 schools it rejected every candidate match as wrong, and those schools were linked to the barangays the research found instead. It left 1 case on hold, because the school named as its predecessor still reports under its own ID.

### Researched coordinates and coordinate quality

For 114 schools there is neither a location record nor a documented predecessor, so their coordinates come from map and directory listings found by research. They are less precise: a median 5.2 km from the center of the school's barangay, against 0.73 km for DepEd's own coordinates that passed the quality checks, and 26 lie more than 10 km away. They are marked as researched, so a map can leave them out, and no barangay link depends on them.

Every assessed school carries a coordinate quality flag: 37,218 have valid coordinates, 769 have coordinates the source itself flags as doubtful, 368 sit more than 10 km from the center of their linked barangay (flagged, not moved), and 324 have none.

## What is in the EDCOM dashboard data download

The download has five parts:

- **The raw extract**, unchanged: the six tables as recovered from the workbook, with the field dictionary and the workbook's metadata.
- **The cleaned tables**, as compressed CSV: one row per school with CRLA results by grade and language, barangay link, coordinates and flags; all school location records with coordinate quality; the barangay reference with match keys and centers; the barangays without a CDC; the 2025 stunting figures for ages 0–2 by region, province and municipality; the program records; the cleaned regional overview; the region crosswalk; and a comparison of each regional figure before and after cleaning.
- **The cleaning log** (`cleaning_log.csv`): every change, with table, record, rule, field, old value, new value and reason.
- **The validation summary** (`validation.json`): row counts before and after cleaning, the result of each check and a summary of how schools were linked.
- **The analysis tables**: the school table with CRLA blocks, barangay and municipality codes and school inputs (Parquet); the school estimates with intervals; the division estimates for all 232 divisions; and the municipal table with reading, stunting and CDC figures.

A README, the terms of use and a checksum file come with the release. The links are in the [Download the data](#download-the-data) section below.

## What this means for school heads and division data teams

Start from the cleaned or analysis school table. The raw extract is for checking the cleaning, not for analysis, since it keeps the old Sulu labels, header rows and garbled names. Regional work should use the `region_clean` column, which places Sulu in Region IX and treats NIR as its own region.

A school head can check the school's own row in minutes. The link method shows how the school was placed in its barangay, and the coordinate flag shows how far to trust its map position. A school linked by research, located through a predecessor, or flagged as suspect, far or missing is worth checking, and the log gives the reason for every change so a wrong link can be traced and reported. Division data teams can compare division totals with their own CRLA records as of 10 May 2026 and review the flagged schools in their division, since local staff know these places best.

For reading results, use the school estimates rather than raw shares, which swing widely in small schools and should not be used to rank them. [How to Read a School's CRLA Results Fairly](https://nash.com.ph/blog/how-to-read-school-crla-results) explains the estimates, [CRLA End-of-Year Results SY 2025-26](https://nash.com.ph/blog/crla-end-of-year-results-sy-2025-2026) gives the national and regional picture, and [Stunting, Day Care Centers and Early Reading](https://nash.com.ph/blog/stunting-day-care-and-reading) compares reading with stunting and CDCs.

## How these numbers were made

The cleaning applies the rules table by table in a fixed order, and each rule writes its changes to the log as it runs, so the log and the cleaned tables come from the same run. Among the automatic linking rules, a school linked by an earlier rule is not touched by a later one; a link confirmed by research takes precedence over all of them. No research link is accepted whose code is missing from the PSGC reference or disagrees with the barangay and municipality the research names.

Distances run in a straight line from a school's coordinates to the center of its barangay; the median is the middle value, with half the schools closer and half farther.

## Limits

Research-based links and coordinates are less precise than DepEd's records, and the researched points should be left out of fine-scale maps. The cleaning can fix only errors that show: a school matched by name to the wrong barangay, with nothing else pointing elsewhere, would not be caught.

The dashboard is a snapshot. It reflects each source as of its stated date, and later corrections by DepEd, DOH/NNC or DILG are not in it. The school inputs in the analysis table come from DepEd's SY 2023-24 files, two school years before the reading results. Stunting among children aged 0–2 and reading in Grades 1–3 describe different children, so comparisons between them are about places, not individual children.

## Frequently asked questions

### What is the EDCOM II ECCD dashboard?

It is a public Tableau dashboard from EDCOM II on early-childhood care and development. It combines DepEd's CRLA results at the end of SY 2025-26 with DOH/NNC stunting data, DILG's list of barangays without a child development center, and program records.

### Did cleaning change the CRLA national totals?

No. The cleaned data hold the same 5,127,670 learners assessed and 511,753 struggling readers in 38,679 schools as the raw extract. The cleaning changed region labels, names and links, and made the regional figures add up to the national ones.

### Why is Sulu in Region IX instead of BARMM in this data?

The Supreme Court ruled in 2024 that Sulu is not part of BARMM, and the PSGC now codes the province to Region IX. DepEd's reading data already follow this, but three of the dashboard's tables still labeled Sulu as BARMM, so the cleaning moved those records.

### How accurate are the school locations in the EDCOM dashboard data?

Most come from DepEd, and those flagged valid lie a median 0.73 km from the center of their barangay. The 114 schools with researched coordinates are less precise, a median 5.2 km away, and are marked so they can be excluded.

### Where can I download the cleaned EDCOM dashboard data?

All files are free to download at data.nash.com.ph, from the raw extract to the analysis tables, with a README. The cleaned tables, the log and the analysis tables are © 2026 Mark Anthony Llego, NASH Reviewer, all rights reserved: email [support@nash.com.ph](mailto:support@nash.com.ph) for written permission before reusing them. The underlying data remain subject to the source agencies' terms. The links are in the download section below.

## Download the data

The cleaned EDCOM II dashboard data behind this series are free to download for personal study. The cleaned tables and documentation are © 2026 Mark Anthony Llego, NASH Reviewer, all rights reserved: to reuse, republish or build on them, email [support@nash.com.ph](mailto:support@nash.com.ph) for written permission first. The underlying data were published by EDCOM II and its source agencies and remain subject to their terms. [How the EDCOM Dashboard Data Was Cleaned](https://nash.com.ph/blog/edcom-eccd-dashboard-data-cleaned) explains every table and cleaning rule. The files are at data.nash.com.ph: start with the [README](https://data.nash.com.ph/edcom-data/v1/README.md), then the [school table with CRLA results and school inputs](https://data.nash.com.ph/edcom-data/v1/edcom_crla_2026.parquet) (Parquet), the [school estimates](https://data.nash.com.ph/edcom-data/v1/school_estimates.csv.gz) (CSV), the [division estimates](https://data.nash.com.ph/edcom-data/v1/division_estimates.csv) (CSV) and the [municipal table](https://data.nash.com.ph/edcom-data/v1/municipalities.csv) (CSV). The DepEd school files used for school inputs are described in [Download Cleaned DepEd School Data](https://nash.com.ph/blog/download-cleaned-deped-school-data).

Every file, with its rows, columns and checksum, is listed on the [EDCOM II CRLA data page](https://nash.com.ph/edcom-data). To cite the data: Llego, M. A. (2026). *EDCOM II early-childhood dashboard data, cleaned, version 1*. NASH Reviewer. https://nash.com.ph/edcom-data



## More in this series

- [CRLA End-of-Year Results SY 2025-26: All 18 Regions](https://nash.com.ph/blog/crla-end-of-year-results-sy-2025-2026)
- [CRLA Start vs End of SY 2025-26 in the Same Schools](https://nash.com.ph/blog/crla-start-vs-end-of-year-sy-2025-2026)
- [CRLA by Language: Mother Tongue, Filipino and English](https://nash.com.ph/blog/crla-mother-tongue-filipino-english)
- [How to Read a School's CRLA Results Fairly](https://nash.com.ph/blog/how-to-read-school-crla-results)
- [CRLA Variation: School, Division or Region?](https://nash.com.ph/blog/crla-school-division-region-variation)
- [Where Struggling Readers Are Concentrated](https://nash.com.ph/blog/where-struggling-readers-are-concentrated)
- [School Resources and CRLA Reading Results](https://nash.com.ph/blog/school-resources-and-crla-reading)
- [CRLA Results in Schools With and Without a Principal](https://nash.com.ph/blog/schools-with-and-without-principal-crla)
- [Action Research and Thesis Topics on Reading (CRLA)](https://nash.com.ph/blog/action-research-topics-reading-crla)
- [CRLA Reading Hotspots: A Map of Struggling Readers](https://nash.com.ph/blog/crla-reading-hotspots-map)
- [Stunting, Day Care Centers and Early Reading](https://nash.com.ph/blog/stunting-day-care-and-reading)
- [Literacy and Nutrition Programs Compared With Need](https://nash.com.ph/blog/literacy-nutrition-programs-coverage)
- [CRLA Schools With No Struggling Readers: A Data Check](https://nash.com.ph/blog/crla-schools-with-no-struggling-readers)
- [How the EDCOM Dashboard Data Was Cleaned](https://nash.com.ph/blog/edcom-eccd-dashboard-data-cleaned)
- Regional reports: [NCR](https://nash.com.ph/blog/crla-results-sy-2025-2026-ncr), [CAR](https://nash.com.ph/blog/crla-results-sy-2025-2026-car), [Region I](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-1-ilocos), [Region II](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-2-cagayan-valley), [Region III](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-3-central-luzon), [Region IV-A](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-4a-calabarzon), [MIMAROPA](https://nash.com.ph/blog/crla-results-sy-2025-2026-mimaropa), [Region V](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-5-bicol), [Region VI](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-6-western-visayas), [NIR](https://nash.com.ph/blog/crla-results-sy-2025-2026-negros-island-region), [Region VII](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-7-central-visayas), [Region VIII](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-8-eastern-visayas), [Region IX](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-9-zamboanga-peninsula), [Region X](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-10-northern-mindanao), [Region XI](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-11-davao), [Region XII](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-12-soccsksargen), [Caraga](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-13-caraga), [BARMM](https://nash.com.ph/blog/crla-results-sy-2025-2026-barmm)
- Earlier reports: [CRLA Results SY 2025-2026 (DepEd's selected-school file)](https://nash.com.ph/blog/crla-results-sy-2025-2026), [Do the Same DepEd Schools Lag on the ELLNA, NAT and CRLA?](https://nash.com.ph/blog/deped-schools-lag-ellna-nat-crla), [DepEd School Inputs vs Test Results](https://nash.com.ph/blog/deped-school-inputs-vs-test-results), [EDCOM II Final Report: Guide for Aspiring School Heads](https://nash.com.ph/blog/edcom-ii-final-report), [DepEd Data for Aspiring School Heads: Where to Start](https://nash.com.ph/blog/deped-data-aspiring-school-heads)

*Analysis and charts by Mark Anthony Llego for NASH Reviewer. Data: EDCOM II early-childhood dashboard and Department of Education public datasets. NASH Reviewer is not affiliated with EDCOM II or DepEd. Charts may be reused only with written permission from support@nash.com.ph.*

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© 2026 Mark Anthony Llego, NASH Reviewer. All rights reserved. You may quote up to 100 words from this article with the author's name, its title and a link. In research, cite it as: Llego, M. A. (2026, October 27). *How the EDCOM Dashboard Data Was Cleaned*. NASH Reviewer. https://nash.com.ph/blog/edcom-eccd-dashboard-data-cleaned. Any other reuse of the article, its charts or its data needs written permission: email support@nash.com.ph. Terms of Service: https://nash.com.ph/terms#website-content-and-data