These data consist of five simulated datasets and a syntax file written in R. All files were created for use in the recorded COORDINATE Workshop 2 (https://www.youtube.com/watch?v=DeyBKxa894E). In this workshop, Scott Milligan, from the GESIS Leibniz Institute for the Social Sciences, leads participants through a complete data harmonisation exercise. The exercise examines the correlation between experiences with bullying and children’s happiness. Participants may run through the process parallel to the recorded workshop. More information on the project and the Harmonisation Toolbox developed in the project are available on the project’s webpage https://www.coordinate-network.eu/harmonisation or in COORDINATE Harmonisation Workshop 1 (https://www.youtube.com/watch?v=DeyBKxa894E).

Public data used for data harmonization. This dataset is associated with the following publication: Uhran, B., L. Windham-Myers, N. Bliss, A. Nahlik, E. Sundquist, and C. Stagg. Improved Wetland Soil Organic Carbon Stocks of the Conterminous U.S. Through Data Harmonization. Frontiers in Soil Science. Frontiers, Lausanne, SWITZERLAND, 1: 706701, (2021).

Harmony is a data harmonisation project that uses Natural Language Processing to help researchers make better use of existing data from different studies by supporting them with the harmonisation of various measures and items used in different studies. Harmony is a collaboration project between the University of Ulster, University College London, the Universidade Federal de Santa Maria in Brazil, and Fast Data Science Ltd.

You can read more at https://harmonydata.org.

There is a live demo at: https://app.harmonydata.org/

These are the datasets used to validate Harmony. The Excel file is McElroy et al's data, and the zip file contains the English and Portuguese GAD-7s.

ACF Agency Wide resource Metadata-only record linking to the original dataset. Open original dataset below.

ACF Agency Wide resource

Metadata-only record linking to the original dataset. Open original dataset below.

This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB.

Multi-Omics Clinical Data Harmonization Market Outlook

According to our latest research, the global Multi-Omics Clinical Data Harmonization market size reached USD 1.65 billion in 2024, reflecting robust adoption across healthcare and life sciences. With a strong compound annual growth rate (CAGR) of 14.2% projected from 2025 to 2033, the market is anticipated to reach USD 4.65 billion by 2033. This growth is primarily driven by the escalating integration of multi-omics approaches in clinical research, the increasing demand for personalized medicine, and the urgent need to standardize complex biological data for actionable insights. As per our latest research, the market's expansion is underpinned by technological advancements and the broadening scope of omics-based applications in diagnostics and therapeutics.

The rapid growth of the Multi-Omics Clinical Data Harmonization market can be attributed to several key factors. One of the most significant drivers is the exponential increase in biological data generated from next-generation sequencing and other high-throughput omics platforms. As researchers and clinicians seek to unravel the complexities of human health and disease, the need to integrate and harmonize disparate data types—such as genomics, proteomics, metabolomics, and transcriptomics—has become paramount. This harmonization enables a more comprehensive understanding of disease mechanisms, facilitating the identification of novel biomarkers and therapeutic targets. Moreover, regulatory bodies and funding agencies are increasingly emphasizing data standardization and interoperability, further fueling demand for robust harmonization solutions.

Another major growth factor is the accelerating adoption of precision medicine initiatives worldwide. The shift from one-size-fits-all therapies to tailored treatment regimens necessitates the integration of multi-omics data with clinical and phenotypic information. Harmonized data platforms empower clinicians and researchers to draw meaningful correlations between omics signatures and patient outcomes, thereby enhancing diagnostic accuracy and enabling the development of personalized therapeutic strategies. Pharmaceutical and biotechnology companies, in particular, are leveraging multi-omics harmonization to streamline drug discovery pipelines, improve patient stratification, and optimize clinical trial designs, contributing to significant market growth.

Technological innovation plays a central role in propelling the Multi-Omics Clinical Data Harmonization market forward. Advances in artificial intelligence, machine learning, and cloud computing have revolutionized the way multi-omics data is processed, integrated, and analyzed. Sophisticated software platforms now offer automated data curation, normalization, and annotation, reducing manual errors and accelerating research timelines. Additionally, collaborative efforts between academic institutions, healthcare providers, and industry stakeholders have led to the establishment of large-scale multi-omics databases and consortia, further driving market expansion. The growing focus on data privacy, security, and regulatory compliance also shapes market dynamics, prompting continuous innovation in harmonization technologies.

Regionally, North America remains the dominant force in the Multi-Omics Clinical Data Harmonization market, accounting for the largest share in 2024. The region's leadership is attributed to its advanced healthcare infrastructure, significant investments in omics research, and a strong presence of key market players. Europe follows closely, leveraging robust public-private partnerships and supportive regulatory frameworks. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by increasing government initiatives, expanding healthcare access, and rising awareness of precision medicine. Latin America and the Middle East & Africa, though currently smaller markets, are expected to demonstrate steady growth as they enhance their research capabilities and digital health ecosystems.

Solution Analysis

The Solution segment of the Multi-Omics Clinical Data Harmonization market is bifurcated into software and services, each playing a pivotal role in enabling seamless integration and analysis of diverse omics datasets. Software solutions encompass a wide range of platforms and tools designed to automate data normalization, annotation, and integ

Multi-Omics Clinical Data Harmonization Market Outlook

According to the latest research conducted in 2025, the global Multi-Omics Clinical Data Harmonization market size stands at USD 1.47 billion in 2024. The market is experiencing robust momentum, driven by technological advancements and the growing adoption of precision medicine. With a recorded CAGR of 13.6%, the market is projected to reach USD 4.22 billion by 2033. This substantial growth is primarily fueled by the increasing integration of multi-omics datasets in clinical research and diagnostics, which is enabling more comprehensive and actionable insights into complex diseases and therapeutic responses.

The primary growth factor propelling the Multi-Omics Clinical Data Harmonization market is the escalating demand for personalized and precision medicine. As healthcare systems globally shift towards individualized treatment regimens, the necessity to harmonize and integrate diverse omics datasets—such as genomics, proteomics, metabolomics, and transcriptomics—has become paramount. These integrated data solutions facilitate a holistic understanding of disease mechanisms, improve diagnostic accuracy, and enable the development of targeted therapies. The proliferation of next-generation sequencing technologies, coupled with the decreasing cost of omics profiling, has further democratized access to multi-omics data, thereby accelerating its utilization across clinical and research settings.

Another significant driver is the rapid digitization of healthcare and the growing emphasis on interoperability and data standardization. The harmonization of multi-omics clinical data addresses critical challenges related to data silos, heterogeneity, and lack of standardized formats. Advanced data harmonization platforms are leveraging artificial intelligence and machine learning to automate the integration and curation of large-scale omics datasets, ensuring data quality, consistency, and compliance with regulatory standards. This technological evolution is not only enhancing the efficiency of clinical workflows but also fostering collaborations among pharmaceutical companies, research institutions, and healthcare providers.

Furthermore, the rising investments from both public and private sectors in biomedical research are playing a pivotal role in market expansion. Governments and funding agencies worldwide are supporting large-scale multi-omics projects aimed at deciphering the molecular underpinnings of complex diseases such as cancer, neurodegenerative disorders, and rare genetic conditions. These initiatives are generating vast amounts of clinical omics data that require robust harmonization solutions for effective utilization. Additionally, the growing prevalence of chronic diseases and the increasing adoption of electronic health records (EHRs) are amplifying the demand for integrated data management platforms that can seamlessly harmonize clinical and omics datasets for improved patient outcomes.

Regionally, North America continues to dominate the Multi-Omics Clinical Data Harmonization market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading biotechnology firms, advanced healthcare infrastructure, and strong government support for precision medicine initiatives have positioned North America at the forefront of innovation. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by expanding research capabilities, rising healthcare expenditures, and increasing adoption of multi-omics technologies in countries like China, Japan, and India. Europe also maintains a significant market presence, supported by collaborative research networks and robust regulatory frameworks for data standardization and interoperability.

Omics Type Analysis

The Omics Type segment of the Multi-Omics Clinical Data Harmonization market encompasses genomics, proteomics, transcriptomics, metabolomics, epigenomics, and other emerging omics disciplines. Among these, genomics

EO Data Harmonization Pipelines Market Outlook

According to our latest research, the EO Data Harmonization Pipelines market size globally reached USD 1.94 billion in 2024, and is projected to grow at a robust CAGR of 13.2% from 2025 to 2033, culminating in a forecasted market value of USD 5.62 billion by 2033. This dynamic growth is primarily attributed to the surging demand for integrated Earth Observation (EO) data across diverse industries, driven by the need for accurate, real-time, and interoperable geospatial insights for decision-making. The market is experiencing significant advancements in data processing technologies and AI-driven harmonization tools, which are further propelling adoption rates on a global scale. As per our comprehensive analysis, the increasing complexity of EO data sources and the critical need for standardized, high-quality data pipelines remain pivotal growth factors shaping the future of this market.

One of the primary growth drivers for the EO Data Harmonization Pipelines market is the exponential increase in the volume and variety of EO data generated by satellites, drones, and ground-based sensors. As governments, research institutions, and commercial enterprises deploy more sophisticated EO platforms, the diversity in data formats, resolutions, and temporal frequencies has created a pressing need for harmonization solutions. These pipelines enable seamless integration, cleansing, and transformation of disparate datasets, ensuring consistency and reliability in downstream analytics. The proliferation of AI and machine learning algorithms within these pipelines has further enhanced their ability to automate data normalization, anomaly detection, and metadata enrichment, resulting in more actionable and timely insights for end-users across sectors.

Another significant factor contributing to market growth is the increasing adoption of EO data for environmental monitoring, agriculture, disaster management, and urban planning. Governments and private organizations are leveraging harmonized EO data to monitor deforestation, predict crop yields, assess disaster risks, and optimize urban infrastructure planning. The ability to harmonize multi-source data streams enables stakeholders to generate comprehensive, cross-temporal analyses that support sustainable development goals and climate resilience strategies. The integration of cloud-based platforms has democratized access to harmonized EO data, allowing even small and medium enterprises to leverage advanced geospatial analytics without substantial upfront investments in hardware or specialized personnel.

Furthermore, the rising emphasis on interoperability and data sharing among international agencies, research institutions, and commercial providers is fueling the demand for robust EO data harmonization pipelines. Initiatives such as the Global Earth Observation System of Systems (GEOSS) and the European Copernicus program underscore the importance of standardized data frameworks for global collaboration. These trends are driving investments in open-source harmonization tools, API-driven architectures, and scalable cloud infrastructures that can support multi-stakeholder data exchange. As regulatory requirements for data quality and provenance intensify, organizations are increasingly prioritizing investments in harmonization technologies to ensure compliance and maintain competitive advantage in the rapidly evolving EO ecosystem.

From a regional perspective, North America currently dominates the EO Data Harmonization Pipelines market, accounting for over 38% of the global market share in 2024, followed by Europe and Asia Pacific. The United States, in particular, benefits from a mature EO ecosystem, substantial government funding, and a vibrant commercial space sector. Europe’s growth is propelled by strong policy frameworks and cross-border collaborations, while Asia Pacific is rapidly emerging as a high-growth region, driven by increasing investments in satellite infrastructure and smart city initiatives. Latin America and the Middle East & Africa are also witnessing steady adoption, supported by international development programs and growing awareness of EO’s value in addressing regional challenges such as agriculture productivity and climate adaptation.

EO Data Harmonization Pipelines Market Outlook

According to our latest research, the global EO Data Harmonization Pipelines market size reached USD 2.17 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.2% projected through the forecast period. By 2033, the market is expected to attain a value of USD 6.19 billion. This growth is primarily driven by the surging demand for integrated, high-quality Earth Observation (EO) data across various sectors, including environmental monitoring, agriculture, and urban planning, as organizations increasingly seek actionable insights from multi-source geospatial datasets.

The exponential increase in the volume and diversity of EO data sources has emerged as a primary growth factor for the EO Data Harmonization Pipelines market. Organizations now rely on satellite imagery, aerial photographs, UAV data, and ground-based sensors to monitor and analyze dynamic terrestrial and atmospheric phenomena. However, the heterogeneity and varying formats of these datasets have posed significant challenges for seamless integration and analysis. The development and adoption of sophisticated EO data harmonization pipelines have become essential, enabling the conversion, standardization, and fusion of disparate data streams into coherent, analysis-ready datasets. This capability not only enhances the accuracy and reliability of downstream analytics but also accelerates decision-making processes in critical domains such as disaster management, climate change assessment, and precision agriculture.

Another pivotal driver is the rapid technological advancement in cloud computing, artificial intelligence, and machine learning, which has revolutionized the EO data harmonization landscape. Cloud-based platforms now offer scalable, on-demand processing power, allowing for real-time harmonization of massive EO datasets. AI-powered algorithms automate data cleansing, normalization, and feature extraction, significantly reducing manual intervention and operational costs. These innovations have democratized access to EO data harmonization solutions, making them accessible to a broader spectrum of end-users, from government agencies and research institutes to commercial enterprises. The integration of these advanced technologies not only improves the efficiency of EO data pipelines but also opens new avenues for developing predictive models and geospatial intelligence solutions.

The increasing focus on sustainability and environmental stewardship has further amplified the demand for EO data harmonization pipelines. Governments and international organizations are investing heavily in monitoring land use, water resources, and atmospheric conditions to meet regulatory requirements and inform policy decisions. Harmonized EO data enables comprehensive, cross-border analyses that are vital for addressing global challenges such as deforestation, urban sprawl, and natural disasters. As regulatory frameworks around data quality and interoperability become more stringent, organizations are compelled to invest in robust harmonization solutions to ensure compliance and maintain data integrity. This regulatory push, combined with growing public and private sector awareness of the value of harmonized EO data, is expected to sustain market growth over the coming decade.

Regionally, North America and Europe continue to dominate the EO Data Harmonization Pipelines market, accounting for a combined market share of over 60% in 2024. The United States, in particular, benefits from a mature geospatial technology ecosystem and significant investments in satellite infrastructure. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by expanding EO satellite programs in China, India, and Japan, coupled with increasing adoption of cloud-based geospatial solutions. Latin America and the Middle East & Africa are gradually emerging as promising markets, propelled by investments in environmental monitoring and disaster management initiatives. As these regions enhance their EO capabilities, the global market is poised for sustained expansion.

Component Analysis

The EO Data Harmonization Pipelines market by component is segmented into software, hardware, and services. Software solutions remain the largest segment, accounting for over 45% of the market share in 2024. These platforms are integral for the automated ingestion, normalization, and fusio

ST_LUCAS is a harmonized dataset derived from the LUCAS (Land Use and Coverage Area frame Survey) dataset. LUCAS is an Eurostat activity that has performed repeated in situ surveys over Europe every three years since 2006. Original LUCAS data (https://ec.europa.eu/eurostat/web/lucas/data) starting with the 2006 survey were harmonized into common nomenclature based on the 2018 survey. ST_LUCAS dataset is provided in two versions:

• lucas_points: each LUCAS survey is represented by single record

• lucas_st_points: each LUCAS point is represented by a single location calculated from multiple surveys and by a set of harmonized attributes for each survey year

Harmonization and space-aggregation of LUCAS data were performed by ST_LUCAS system available from https://geoforall.fsv.cvut.cz/st_lucas. The methodology is described in Landa, M.; Brodský, L.; Halounová, L.; Bouček, T.; Pešek, O. Open Geospatial System for LUCAS In Situ Data Harmonization and Distribution. ISPRS Int. J. Geo-Inf. 2022, 11, 361. https://doi.org/10.3390/ijgi11070361.

List of harmonized LUCAS attributes: https://geoforall.fsv.cvut.cz/st_lucas/tables/list_of_attributes.html

ST_LUCAS dataset is provided under the same conditions (“free of charge”) as the original LUCAS data (https://ec.europa.eu/eurostat/web/lucas/data).

Supplementary material 1

A detailed overview of the results of the literature search, including the data extraction matrix can be found in the Additional file 1.

Yearly citation counts for the publication titled "Promoting data harmonization to evaluate vaccine hesitancy in LMICs: approach and applications".

SDR 2.0 Cotton File: Cumulative List of Variables in the Surveys of the SDR Database is a comprehensive data dictionary, in Microsoft Excel format. Its main purpose is to facilitate the overview of 88118 variables (i.e. variable names, values, and labels) available in the original (source) data files that we retrieved automatically for harmonization purposes in the SDR Project. Information in the Cotton File comes from 215 source data files that comprise ca. 3500 national surveys administered between 1966 and 2017 in 169 countries or territories, as part of 23 international survey projects. The COTTON FILE SDR2 is a product of the project Survey Data Recycling: New Analytic Framework, Integrated Database, and Tools for Cross-national Social, Behavioral and Economic Research, financed by the US National Science Foundation (PTE Federal award 1738502). We thank the Ohio State University and the Institute of Philosophy and Sociology, Polish Academy of Sciences, for organizational support.

README

This repository contains the ON-Harmony data resource associated with the manuscript:

Warrington et al. (2025), "A multi-site, multi-modal travelling-heads resource for brain MRI harmonisation", under-review, Scientific Data

Overview

• Project name: ON-Harmony: A resource for development and comparison of multi-modal brain 3T MRI harmonisation approaches
• Years that the project ran: 2018-2021 (Phase A), 2023-2024 (Phase B)
• Phase A was originally published in Warrington et al. (2023)
• Description of the contents of the dataset: 20 participants, each scanned in 6 3T scanners across 5 sites and 3 major vendors, 5 modalities. 9 participants with 6 within-scanner repeats as well. Data collected in 2 phases, with 10 participants in each phase.

Methods

Subjects

Phase A

N = 10 healthy participants (mean age at recruitment 34 ± 9.4 years; 8 male, 2 female)

Subject: Sessions

• 03286: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001
• 03997: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001
• 10975: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001
• 12813: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001
• 13192: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF1PRI002 OXF1PRI003 OXF1PRI004 OXF1PRI005 OXF1PRI006 OXF2PRI001 OXF3TRI001
• 13305: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001
• 14221: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001
• 14229: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF2PRI002 OXF2PRI003 OXF2PRI004 OXF2PRI005 OXF2PRI006 OXF3TRI001
• 14230: NOT1ACH001 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001 OXF3TRI002 OXF3TRI003 OXF3TRI004 OXF3TRI005 OXF3TRI006
• 14482: NOT1ACH001 NOT1ACH002 NOT1ACH003 NOT1ACH004 NOT1ACH005 NOT1ACH006 NOT2ING001 NOT3GEM001 OXF1PRI001 OXF2PRI001 OXF3TRI001

Phase B

N = 10 healthy participants (mean age at recruitment 29.8 ± 11.4 years; 5 male, 5 female)

Subject: Sessions

• 16981: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF4GEP001
• 16841: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF4GEP001
• 16842: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF4GEP001
• 16974: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF1PRI002 OXF1PRI003 OXF1PRI004 OXF1PRI005 OXF1PRI006 OXF2PRI001 OXF4GEP001
• 15320: NOT1ACH001 NOT2ING001 NOT2ING002 NOT2ING003 NOT2ING004 NOT2ING005 NOT2ING006 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF4GEP001
• 16745: NOT1ACH001 NOT2ING001 NOT4GEP001 NOT4GEP002 NOT4GEP003 NOT4GEP004 NOT4GEP005 NOT4GEP006 OXF1PRI001 OXF2PRI001 OXF4GEP001
• 16975: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF4GEP001 OXF4GEP002 OXF4GEP003 OXF4GEP004 OXF4GEP005 OXF4GEP006
• 16793: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF4GEP001
• 16794: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF4GEP001
• 16766: NOT1ACH001 NOT2ING001 NOT4GEP001 OXF1PRI001 OXF2PRI001 OXF2PRI002 OXF2PRI003 OXF2PRI004 OXF2PRI005 OXF2PRI006 OXF4GEP001

Apparatus

Scanners:

1. Philips Achieva (NOT1ACH),
2. Philips Ingenia (NOT2ING),
3. GE MR750 (NOT3GEM),
4. Siemens Prisma (32ch) (OXF1PRI),
5. Siemens Prisma (64ch) (OXF2PRI),
6. Siemens Trio (OXF3TRI),
7. GE Premier (48ch) (NOT4GEP),
8. GE Premier (21ch) (OXF4GEP)

Phase A data were acquired using Scanners 1-6. Phase B data were acquired using used Scanner 1,2,4,5,7,8. Scanners 1,2,4,5 are overlapping between the two phases.

Modalities:

1. T1w,
2. T2w FLAIR,
3. diffusion MRI (dMRI),
4. resting-state functional MRI (rsfMRI),
5. susceptibility-weighted imaging (SWI)

Experimental location

Scanners are located across five sites in the United Kingdom.

1. 3T Philips Achieva: SPMIC-UP, Nottingham
2. 3T Philips Ingenia: SPMIC-UP, Nottingham
3. 3T GE MR750 (phase A only): SPMIC-QMC, Nottingham
4. 3T Siemens Prisma (32ch): WIN-FMRIB, John Radcliffe Hospital, Oxford
5. 3T Siemens Prisma (64ch): WIN-OHBA, Warneford Hospital, Oxford
6. 3T Siemens Trio (phase A only): OCMR, John Radcliffe Hospital, Oxford
7. 3T GE Premier (48ch) (phase B only): SPMIC-QMC, Nottingham
8. 3T GE Premier (21ch) (phase B only): OCMR, John Radcliffe Hospital, Oxford

• SPMIC-UP - Sir Peter Mansfield Imaging Centre, University Park, University of Nottingham
• SPMIC-QMC - Sir Peter Mansfield Imaging Centre, Queen's Medical Centre Medical School, University of Nottingham
• FMRIB - Oxford Centre for Functional MRI of the Brain, University of Oxford
• OHBA - Oxford Centre for Human Brain Activity, University of Oxford
• OCMR - Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford

Notes

Anatomical data have been defaced following the UKBiobank defacing procedure. Defacing masks are available in each session directory as sub-

At time of release, SWI data were not yet incorporated in to the BIDS standard. The SWI extension proposal (https://bids-specification.readthedocs.io/en/v1.2.1/06-extensions.html: accessed Autumn 2022) was used to define SWI data structure.

Minor deviations of protocols for individual scans The majority of subjects were acquired using the same protocols. However, for some subjects there were minor deviations in some protocol parameters that we describe here for completeness.

Phase A - GE MR 750 rfMRI: For one subject (03286), we acquired two versions of the rfMRI data with 1) an isotropic spatial resolution of 2.4mm and 2) a spatial resolution of 2.2x2.2x3.4mm. Neither matched the 3.3mm isotropic data acquired for other subjects. For 6 subjects (03286, 03997, 10975, 12813, 14482, 14221) there was a mismatch in PE direction between dMRI and fMRI that we accounted for in the processing pipeline. - Philips Achieva dMRI: In most cases, the dMRI protocol included 6 b=0 s/mm2 volumes. Four subjects (13305, 13192, 14229, 14230) were acquired with 2 b=0 s/mm2 volumes. - Philips Ingenia dMRI: In most cases, dMRI data were acquired using an in-plane acceleration factor of 1.5 (TE=98ms, TR=4.4s). For 4 subjects (13305, 13192, 14229, 14230), the dMRI data were acquired using an in-plane acceleration factor of 2 (TE=92ms, TR=3.9s).

Phase B - Philips Achieva fMRI: 16975 (16975_NOT1ACH001) has a single volume missing, due to data corruption during reconstruction. - Philips Ingenia fMRI: 15320 (15320_NOT2ING006) has a single volume missing, due to data corruption during reconstruction. - GE Premier 21 (Oxford) dMRI: For session 16793_OXF4GEP001, the dMRI reversed-phase-encode b=0 was incorrectly acquired with a left-right (LR) phase-encode direction instead of posterior-anterior (PA). We accounted for this by running a bespoke fieldmap estimation with FSL's topup (Andersson et al. NeuroImage 2003), which was fed into the UKBB pipeline for further processing. For session 16745_OXF4GEP001, dMRI were acquired with a slice thickness of 2.4 mm, instead of 2.0 mm. - GE Premier 21 (Oxford) anatomical: For subjects 15320, 16793, 16794, 16974, 16766, 16745, the T2w FLAIR was acquired with a 1.3x1x1 mm resolution instead of 1x1x1 mm. For sessions 16793 and 16794, the T1w MPRAGE was acquired with a 212x256x256 matrix size, instead with 256x256x212. - GE Premier 42 (Nottingham) dMRI: For session 16794_NOT4GEP001, no dMRI reversed-phase-encode b=0 image was acquired. In this case, we generated a synthetic distortion-free b=0 image using Synb0-DisCo (v3.0) (Schilling et al. Magn Reson Imaging. 2019, Schilling et al. PLoS ONE 2020) with the same phase-encoding (anterior-posterior, AP) direction as the main data. A fieldmap was then estimated from the synthetic distortion-free b=0 and acquired b=0 data using FSL's topup, which was then used for further processing.

Incidental Findings - A cortical hypointensity visible on the right hemisphere (for fMRI, dMRI and SWI) was observed for Subject 16766, phase B. The subject is healthy and Incidental finding inspection deemed this as a non-pathological feature. Overall, the z-scored IDPs and QC measures for that subject scans were not considerably different from the mean of all subjects, so we kept these scans.

Supplementary Material 2.

Description and harmonization strategy for the predictor variables.

The program PanTool was developed as a tool box like a Swiss Army Knife for data conversion and recalculation, written to harmonize individual data collections to standard import format used by PANGAEA. The format of input files the program PanTool needs is a tabular saved in plain ASCII. The user can create this files with a spread sheet program like MS-Excel or with the system text editor. PanTool is distributed as freeware for the operating systems Microsoft Windows, Apple OS X and Linux.

The datasets included with this archive supplement the journal article:

Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. Remote Sens. 2019, 11, 1002. doi:10.3390/rs11091002.

The archive includes a README file with further explanations.