ABCD tools & resources

Last reviewed: October 2026. Know of something we’re missing? Open an issue or use our submission form.

ABCD’s data structure changed completely with Release 6.0 (new BIDS-style IDs, session names, and variable names; distribution moved from NDA to NBDC). Code and tutorials written for earlier releases often won’t run as-is on current data. This page collects official and community resources and sorts them by which release they work with, so you can tell at a glance what’s ready to use. For step-by-step guides (access, downloads, analysis, publishing), see Working with ABCD data.

Official hubs at a glance

Resource	Use it for
ABCD documentation	Variables, releases, imaging. This is the source of truth
NBDC Data Hub · Data Access Platform · platform guides	Access (DUCs), downloads, Sandbox
DEAP (docs)	Data dictionary, dataset builder, in-browser analyses
Official software packages	NBDCtools, NBDCtoolsData, ABCDscores (R), nbdctools (Python). See Working with the data
Release notes	What’s in (and wrong with) each release
ABCD publications	Official list of ABCD-related papers. Also see our News page
ABCD Study GitHub · NBDC GitHub	Official code (ABCD-STUDY is mostly legacy, and current work lives in nbdc-datahub)

How releases are tagged

Badge	Meaning
7.0	Built or updated for the current release (7.0)
any release	Methods, concepts, or tools that don’t depend on ABCD’s data structure
6.x	Built for the NBDC era (6.0/6.1). Usually works on 7.0, but check variable names
≤ 5.1	NDA era. Access steps and variable names are outdated, but the ideas may still help

Tip

Porting old code? NBDCtools::convert_names_data() and convert_names_file() translate pre-6.0 variable names to the new scheme, and the data dictionary lists each variable’s old name. See Accessing the data for the new ID and session conventions.

The official packages are all maintained for 7.0, and ABCDscores’ version number matches the data release (7.x ↔ release 7).

7.0 Ready for the current release

Resource	Type	What it does	Maintainer
abcd_ksads	Python	Cleans and harmonizes KSADS-COMP psychiatric diagnoses: missingness codes, administration calendar, 230 diagnosis variables mapped to DSM categories, instrument-version tracking. Tested, with CI. Companion preprint: Giampetruzzi et al. (2026), Harmonized psychiatric diagnosis data from the ABCD Study (preprint)	E. Giampetruzzi / R. Poldrack
ABCD-ReproSchema	R + Python	The ABCD data dictionary converted to ReproSchema JSON-LD, tagged per release (6.0, 6.1, 7.0). Useful for machine-readable instrument metadata	ReproNim
ABCD Data Dictionary Semantic Search	Web app	Plain-language variable search over the 6.0 and 7.0 dictionaries	B. Das
7.0 release notes and docs changelog	Official docs	What changed in 7.0 and in point releases (7.0.x). This is the closest thing to a known-issues list	DAIRC
Genetics (WGS) domain docs	Official docs	Whole-genome sequencing, new in 7.0 (~8,700 participants)	DAIRC
Linked External Data (LE) domain docs	Official docs	Every geocoded source in one place (ADI, COI 2.0, SVI, air pollution, NLCD, Opportunity Atlas, state policies), plus 7.0 fixes	DAIRC
Novel Technologies (Fitbit) domain docs	Official docs	How Fitbit heart-rate, sleep, and activity measures are derived, with documented accuracy caveats	DAIRC
Substudies docs	Official docs	Lesser-known substudy datasets (Baby Teeth metals, COVID-19, Endocannabinoid, MR Spectroscopy, RECOVER, Social Development, …)	DAIRC
CloudPipe	Python (AWS)	New (Sept 2026). Reference cloud pipeline from minimally processed sMRI/fMRI to MNI-space BOLD, confounds, and FastSurfer outputs. Meant to be adapted, not run as-is	J. Russell
abcd_extract_eprime	MATLAB	Official scripts that extract task timing and behavior (MID, SST, n-back, REC) from E-Prime files. Minimal documentation	ABCD-STUDY (DAIC)

7.0 known issues worth knowing about

Summarized from the 7.0 release notes and the docs changelog (through docs 7.0.4, October 2026). Always check the official pages for updates.

• Coverage: 11,860 participants across 16 events. Data are complete through the 5-year follow-up and nearly complete for year 6. The 7-year and 7.5-year (ses-07M) data are partial.

• Siemens XA30 sessions (889): T1w, T2w, and fieldmaps are missing from BIDS sourcedata/rawdata. Derivatives and tabulated imaging data are not affected.

• Concatenated voxelwise imaging: an outdated registration target for atlas channel 11 left out cerebellar grey matter.

• RECOVER COVID-symptom tables (recover_p_ps, recover_p_pscg): 346 variables are misnamed. The release notes give a rename mapping, and a fix is planned for 7.1.

• fc_y_ev__edu_002__v01 has a mislabeled response level (school type).

• Documentation-only notes added in 7.0.3–7.0.4: hair-style bias in hair toxicology, a Pubertal Development Scale scoring correction, a body-size caveat for dMRI, mh_y_resil and Emotional Stroop randomization issues.

Warning

Still on 6.0/6.1? These were fixed in 7.0: hair toxicology was coded 1/2 instead of 0/1, the Fitbit qc_300min sleep flag used > 299 instead of ≥ 300, and three ozone variables in le_l_lo3 were mis-mapped. Re-check any results that use these variables.

Found a data problem? Report it through the ABCD issue-reporting page (NBDC Help Center ticket). There’s no public known-issues tracker.

Note

As of October 2026 we couldn’t find any 7.0-specific training (webinar, walkthrough, or office hours). The NBDC 7.0 pages point to the written docs, and the platform and DEAP are unchanged from 6.x, so the 6.x tutorials below still apply. Questions go to nbdc@mail.nih.gov or our Discussion Board.

any release Release-agnostic

Training & talks

Resource	Type	What it covers
2025 ABCD Insights & Innovations Meeting, Collaborative Learning Workshops (Day 1 · Day 2 · timestamped agenda)	NIH VideoCast	Imaging, biostatistics, linked external data, and Responsible Use of Data workshops, plus Q&A with ABCD investigators. Day 1 at 52:20 is an NBDC/DEAP demo 6.x, and Day 2 at 45:00 is NBDC/DEAP Q&A
ABCD-ReproNim (materials · videos · exercises · Neurostars forum)	Course	Reproducible, responsible ABCD analysis: lectures, data exercises, and Q&A. Companion paper: Laird et al. (2026), DCN. Older modules use NDA-era data. Next: ABCD-ReproNim Hackathon, April 5–9, 2027
ABCD Workshops: 2021 Modeling Developmental Change (slides, talks, code) · 2019 Brain Development and Mental Health (slides, talks, code)	Recorded talks, slides, code tutorials	Reproducible science, longitudinal modeling, puberty, and adolescent mental health. The methods teaching is excellent, but code and data-access steps are NDA-era ≤ 5.1. Attendees of the 2021 workshop wrote the Saragosa-Harris et al. (2022) practical guide (PDF). Supported by NIH R25MH125545
ABCD public science webinars	Recorded talks	Findings on obesity, substance use, COVID-19, sleep, and screen use, from ABCD investigators. Good background
“Mind the Kids: Lessons from the ABCD data revolution”	Podcast (ACAMH, 2026; free account)	Adriene Beltz on measuring puberty in ABCD
r-stats-workshop	Quarto course	Four-module R and statistics course using synthetic ABCD-shaped longitudinal data

Code & tools

Resource	Type	What it does
metasnf	R (CRAN)	Subtyping with meta-clustering and similarity network fusion, designed around ABCD-style multimodal data
ggsegAtlasTrack	R	The AtlasTrack white-matter atlas (used for ABCD DTI) for ggseg plots
ggAtlasTrack	R	Plots AtlasTrack tract statistics, with helpers for ABCD 5.0 and 6.0+ variable names 6.x
BrainSwipes	Web app	Crowdsourced visual MRI QC, used for ABCC
abcd-bids-tfmri-pipeline	Python	Task-fMRI level-1/level-2 GLMs on CIFTI dense time series (abcd-hcp-pipeline outputs)

Methods papers for specific domains

These complement the Best-practice guides. Many describe measures from earlier waves, but the measurement issues they raise still apply.

Paper	Why read it
Fan, C. C., et al. (2021). ABCD Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data. DCN. doi:10.1016/j.dcn.2021.101030	Founding methods paper for the geocoded data
Abad, S., et al. (2024). Lifetime residential history collection and processing for environmental data linkages in the ABCD study. Health & Place. doi:10.1016/j.healthplace.2024.103238	How multi-address exposure variables are built
Barch, D. M., et al. (2021). Demographic and mental health assessments in the ABCD study: Updates and age-related trajectories. DCN. doi:10.1016/j.dcn.2021.101031	Measure-by-measure mental health protocol
Gonzalez, R., et al. (2021). An update on the assessment of culture and environment in the ABCD Study. DCN. doi:10.1016/j.dcn.2021.101021	Culture and environment measures
Sullivan, R. M., et al. (2026). Substance use patterns from late childhood to mid-adolescence: Updates on the ABCD Study. Drug & Alcohol Dependence Reports. doi:10.1016/j.dadr.2026.100463	Most recent substance-use descriptive update
Wade, N. E., et al. (2024). Cannabis use and neurocognitive performance at 13–14 years-old: Optimizing assessment with hair toxicology in the ABCD Study. Addictive Behaviors. doi:10.1016/j.addbeh.2023.107930	Self-report vs. hair toxicology
Anokhin, A. P., et al. (2022). Age-related changes and longitudinal stability of individual differences in ABCD Neurocognition measures. DCN. doi:10.1016/j.dcn.2022.101078	Reliability/stability of cognitive measures
Bagot, K. S., et al. (2022). Youth screen use in the ABCD study. DCN. doi:10.1016/j.dcn.2022.101150	Screen-use measures and how they changed across waves
Zhao, et al. (2025). Examining measurement discrepancies in adolescent screen media activity with insights from the ABCD study. npj Mental Health Research. doi:10.1038/s44184-025-00131-z	Youth vs. parent screen-time reports
Feldman, et al. (2026). Who’s counting? Discrepancies between caregiver-reported, youth-reported, and Fitbit sleep in the ABCD study. doi:10.1007/s44470-026-00042-6	Choosing among sleep measures
Harris, et al. (2025). The Childhood Opportunity Index 2.0: Factor structure in 9–10 year olds in the ABCD Study. IJERPH. doi:10.3390/ijerph22020228	Check before using COI as one composite
Fan, C. C., et al. (2023). Genotype data and derived genetic instruments of ABCD Study for better understanding of human brain development. Behavior Genetics. doi:10.1007/s10519-023-10143-0	Genotyping array QC and ancestry (pre-WGS)
Elyounssi, S., et al. (2025). Addressing artifactual bias in large, automated MRI analyses of brain development. Nature Neuroscience. doi:10.1038/s41593-025-01990-7	How MRI quality biases automated structural results, and fixes
Cetin-Karayumak, S., et al. (2024). Harmonized diffusion MRI data and white matter measures from the ABCD Study. Scientific Data. doi:10.1038/s41597-024-03058-w	Scanner-harmonized dMRI derivatives (originally distributed via NDA)
Van den Akker, O. R., et al. (2021). Preregistration of secondary data analysis: A template and tutorial. Meta-Psychology. doi:10.15626/MP.2020.2625	The tutorial behind the OSF secondary-data preregistration template

6.x NBDC era (6.0 / 6.1)

These were built on the current platform and data structure. Most work on 7.0, but verify variable names.

Resource	Type	What it does
DEAP tutorial (DAIRC, 2025)	Video (~55 min, chapters)	Full DEAP walkthrough: data dictionary, building/saving datasets, name mapping, downloads. This is the video the official DEAP docs now embed
DEAP quick guide (PDF)	Handout	Step-by-step dataset creation and download
“Tutorial on the new DEAP”	Video	Shorter DEAP overview from the 6.0 launch
lasso-project-template	Shell/R template	Minimal reproducible R project for the NBDC Sandbox
abcc_automated_QC	Python	Automated structural/functional QC for ABCC derivatives (outlier detection, connectivity pass flags)
abcd-abcc_motion_reg_generator	Python BIDS app	Extracts and repairs motion regressors from ABCC derivatives
abcd-abcc_concat_filtered_dtseries_generator	Python BIDS app	Builds concatenated, filtered task (n-back) dense time series from ABCC
abcd-rsfmri-training-code	R/Python tutorial	Loading ABCD 6.0 resting-state derivatives for analysis
ABCDWrangler	Python	Variable lookup, long↔wide reshaping, MRI QC filters. README says it’s still being adapted to 6.0
“ABCD Study omits gender-identity data from latest release”	News (The Transmitter, 2025)	Why gender-identity variables are missing from 6.0+, a common newcomer question

≤ 5.1: NDA-era resources (click to expand)

These were written for releases distributed through the NIMH Data Archive. Access instructions and variable names are outdated. They’re listed because the methods or concepts can still be useful.

Resource	Type	Why it’s still useful
pelham-weighting-resources	R workshop materials (release 4.0)	Concepts for population and attrition weighting
abcd_acs_raked_propensity	R	How the ACS-raked propensity weights were computed
abcc_datapre	Python	E-Prime → BIDS events with scanner-timing QC; logic still applies
Cleaning-ABCD-hormone-data	R	Salivary hormone cleaning following the Herting (2021) decision tree
LED social/environmental context code (OSF)	Code + data	Source code and US-wide source data behind neighborhood measures
NeuroHackademy 2021: ABCD-BIDS Community Collection	Video	ABCC concepts and BIDS layout (access steps are outdated)
NIDA: Identifying Research Gaps & Opportunities with ABCD Study Data (2021)	Video + transcript	Prevention-science topics: trauma, self-regulation, substance-use initiation
analysis-nda	R	Historical. Old NDA merge scripts, superseded by NBDCtools
fitbit-pipeline	Python	Superseded. Fitbit scoring now lives in ABCDscores

Archived NDA access and download tools. These don’t work for 6.0+, which is only on NBDC.

• NDA cloud access training

• nda-tools

• nda-abcd-s3-downloader

• NDA Collection 3165 docs (ABCC 1.0–2.0). ABCC is now part of the official release; see the ABCC start page

See also

• Best-practice guides: peer-reviewed practical guides, including responsible use (Simmons et al., 2021) and the official responsible-use guidance

• Methods & open-science toolkit: preregistration, reproducible workflows, covariates, nesting, longitudinal models, effect sizes, and machine learning