Beyond Boundaries: Data Sharing in Motor Development Research—Best Practices, Challenges, and Opportunities

Abstract

Data sharing is increasingly recognized as a critical practice in scientific research, facilitating transparency, reproducibility, and collaboration. In motor development research, the exchange of data holds particular potential for advancing knowledge and improving methodologies, especially because data collection in motor development research is resource intensive and costly, often requiring specialized equipment, standardized protocols, and longitudinal tracking. However, high data protection requirements, due to the personal nature of the data, pose significant challenges to data sharing. Many motor performance assessments involve children and adolescents, making privacy concerns even more pronounced due to ethical considerations surrounding vulnerable populations. The fragmentation of data sets, the variety of assessment tools, and inconsistencies in data collection methods further complicate large-scale data sharing in motor development research. Addressing these challenges requires the implementation of structured data-sharing frameworks, the development of technical infrastructures, and a shift toward standardized protocols. This research note outlines best practices for data sharing, highlights two prominent projects—MO|RE data (MOtor REsearch data repository) and the COMBINE Project (The Consortium for Motor Behavior in Neurodivergence)—and discusses key challenges and opportunities. The note concludes with implications for researchers aiming to implement data-sharing practices effectively.

Motor development is a crucial aspect of childhood and adolescent health, as motor competence is strongly linked to lifelong physical activity levels, fitness, and overall well-being (Ortega et al., 2008; Stodden et al., 2008). There are two notable challenges currently limiting the rate of progress in our understanding of motor development: (a) small, homogenous samples and (b) high monetary costs and effort associated with primary data collection. Many studies in the field are conducted with small sample sizes, generally powered for detecting the largest between-group effects (Lohse et al., 2016). These small samples sizes impede our understanding of the complexity of motor development and may limit the generalizability of our findings. As institutions invest less in research and rely on researchers to acquire extramural funding, it becomes increasingly difficult to support large-scale primary data collection. There is also high person effort required from both study team members and participants in motor development research. Data sharing offers a means of addressing these two challenges, maximizing our speed of discovery.

To fully understand developmental trajectories and identify factors that contribute to motor competence, motor development research requires robust data sets. These data sets enable the necessary large-scale analyses and longitudinal tracking across diverse cultures, geographies, and neurotypes. Data sharing provides the tools to move beyond small, single-population studies to large, more generalizable studies.

It is nearly impossible for a single research group to amass the resources, expertise, and funding to aggregate enough data to build these robust data sets alone. Costs associated with personnel, space, and equipment required for primary data collection continue to rise, whereas intramural support declines. These costs create barriers especially for students and early career researchers (Lohse et al., 2016). Motor development researchers also rely heavily on enthusiastic community members willing to volunteer for our research studies. These community members devote time and energy to allow us to conduct our research with little to no immediate benefit for themselves. In-person research, especially with children and clinical populations, is taxing on the community members that already have incredible demands on their time. Data sharing provides the opportunity for us to maximize the yield from the time and energy that our participants give to our research.

In a recent thought-provoking article in this journal, Lohse (2025) described the benefits and concerns regarding data sharing for the fields of motor control, learning, and development. Here, we continue this critical conversation for the field of motor development by providing actionable solutions based on two independent data-sharing initiatives, MOtor REsearch data repository (MO|RE) data and The Consortium for Motor Behavior in Neurodivergence (COMBINE). These initiatives aim to facilitate data harmonization, ensure compliance with ethical standards, and promote interdisciplinary collaboration in the field of motor development. With this research note, we aim to (a) present implementation challenges for sharing motor development data, (b) discuss opportunities and best practices for data sharing, and (c) illustrate these opportunities for our field with two leading projects—MO|RE data and COMBINE.

Implementation Challenges for Sharing Motor Development Data

Although other areas of child development and health sciences research have embraced data sharing, the field of motor development lags behind. Despite the myriad benefits of data sharing, many researchers cite ethical or regulatory concerns and logistics as limiting factors. Solutions that maintain high ethical and technical standards exist for many of these perceived barriers if researchers are willing to learn from other fields, partner with participants, and share infrastructure.

Data Privacy and Ethical Concerns

Participant confidentiality and safety must remain a top priority in any open science initiative. This issue poses particular challenges in the field of motor development, where many participants are members of one or more vulnerable populations (e.g., minors, pregnant people, and people with disabilities). Motor development research studies often also include a longitudinal component, necessitating the collection and maintenance of identified data. Although these data may pose risks to participants if mishandled, they also add tremendous value to studies that seek to answer questions about the complex interplay between demographic factors (e.g., age, sex assigned at birth, gender, and racial and ethnic identity), motor performance, and health indicators. Given the ethical considerations surrounding the use of data collected from vulnerable populations, researchers must engage in rigorous anonymization and compliance with applicable data protection regulations (Ross et al., 2018; Vlahou et al., 2021). The European Union and United Kingdom adopted the General Data Protection Regulation (GDPR) in an effort to address privacy and safety concerns, and despite common misconceptions, international data sharing is permissible under this legislation (Vlahou et al., 2021). And yet, some academic and healthcare institutions vary in their interpretation of GDPR policies or are not sufficiently resourced to support regulated data sharing, which can deter researchers from attempting international collaboration.

Anonymization offers a solution to many of the concerns and regulatory restrictions associated with identified data sharing, though implementation can be challenging and drawbacks remain for management of data from longitudinal studies (Vlahou et al., 2021). To clarify the distinction between anonymization and deidentification, it is important to note that regulatory frameworks apply different definitions and thresholds for what constitutes sufficient protection of personal data. In this context, de-identification can refer to different approaches: under Health Insurance Portability and Accountability Act in the United States, removal of 18 specific identifiers (“Safe Harbor”) is considered sufficient, whereas under the GDPR in Europe, pseudonymized data remain classified as personal data because reidentification is still possible, and only full anonymization falls outside the scope of the regulation. According to the GDPR, data are considered anonymized only if reidentification of individuals is no longer possible; pseudonymized data, by contrast, remain classified as personal data and thus subject to the GDPR. Many motor development studies are based on small sample sizes, often focusing on specific age groups or clinical populations (e.g., children and neurodivergent people). Small sample sizes increase the risk of reidentification, making it difficult to ensure complete anonymity. Furthermore, most research in this field relies on longitudinal data as motor development must be studied over time to capture growth trajectories and developmental milestones. The value of longitudinal tracking complicates data protection efforts, necessitating advanced security measures and ethical frameworks to guide responsible data sharing.

Finally, many motor development data sets consist of children’s information collected with parental or guardian consent. Anonymized data sharing precludes researchers from recontacting participants when they reach the legal age of consent, relying on parents’ consent decisions at the time of initial data collection. Manhas et al. (2018) provide a thorough overview of the landscape of legal and ethical considerations associated with sharing longitudinal data obtained from children. Notably, from a sample of 346 parents, they determined that less-engaging consent processes were preferred when sharing de-identified, nonbiological research data. In fact, 44% felt that consent for sharing de-identified data was unnecessary, and half of the remaining parents preferred an opt-out model of consent for data sharing. In a similar study, Ali et al. (2024) surveyed 195 parents. Parents overwhelmingly supported sharing their children’s data within universities and nonprofit health-related research centers (96%) but were resistant to sharing with for-profit companies. Over 54% of parents were happy for their child’s data to be shared globally. Finally, families with a child with a neurodevelopmental condition affirmed greater support for data sharing than those with only typically developing children. Taken together, these studies suggest that careful attention to anonymization processes and data access controls mitigate the concerns that parents may have about sharing their children’s data for secondary analysis. Concerns about data sharing, particularly among parents, often center on the risk of misuse by third parties, including commercial actors. Addressing these concerns requires robust safeguards. As automated data extraction (“scraping”) tools become more sophisticated, secure repository environments such as MO|RE data and COMBINE allow the implementation of controlled access systems, user authentication, and contractual data use agreements to prevent unauthorized bulk harvesting of data by third parties, including for-profit entities such as technology companies. In addition, both MO|RE data and COMBINE require active login-based access, and MO|RE data further applies license agreements that explicitly restrict data use to scientific purposes only.

Standardization Issues

Motor development research relies on a wide variety of assessment tools, for example, Movement Assessment Battery for Children, Bruininks–Oseretsky Test of Motor Proficiency, Peabody Developmental Motor Scales, Tests of Gross Motor Development, German Motor Test, and physical literacy assessments. The diversity of these tools presents major challenges for direct comparison and data harmonization. Differences in test administration, scoring systems, and population norms create inconsistencies that hinder cross-study comparisons and large-scale meta-analyses. In addition, some tests focus on specific aspects of motor development (e.g., balance, coordination, strength), making it difficult to aggregate data sets without losing specificity. However, standardization efforts are underway. Projects like the Youth International Fitness Test (Ortega et al., 2025) aim to establish internationally recognized assessment frameworks. Specifically, Youth International Fitness Test requires detailed metadata to ensure compatibility across studies (Lang et al., 2023), which ensures reliable standardization. Similarly, repositories such as MO|RE data and COMBINE have developed mapping and harmonization protocols to align domains of motor competence across data sets collected with different methodologies (Eberhardt et al., 2020). Nonetheless, achieving full standardization remains an ongoing challenge.

Even when using the same assessment, individual research labs record and store data using a variety of methods (e.g., REDCap, Excel, Access, and Structured Query Language) and with different levels of granularity (e.g., raw scores, standard scores, and percentiles). The lack of uniform data formats significantly complicates integration across studies. One approach for maximizing uniformity is to provide data entry templates for ensuring that data are provided in the same format and data dictionaries for ensuring that values reported for individual variables are consistent across entries, as in the case of COMBINE.

In addition to this a priori approach, data validation needs to be conducted to determine that data do meet the format and data dictionary requirements. Both MO|RE data and COMBINE use similar approaches for ensuring data uniformity and validation, beginning with an automatic data validation check on all data submitted for entry. These validation checks detect implausible values (e.g., negative age and percentiles greater than 100) and, for COMBINE, harmonize common data entry differences (e.g., converting entries for the handedness variable from “R,” “Right,” or “right” to “r”). Following automatic validation, the project leads examine the validation report and submitted data to ensure that the data meets the required criteria for submission into the data repository. Automation of data validation and standardization processes minimizes the labor required by individual research teams and maximizes the consistency and completeness of the shared data set.

Infrastructure and Funding

Maintaining large-scale data repositories and ensuring their long-term sustainability require significant financial and technical resources. Hardware and software resource needs can include the following: secure cloud storage solutions for sensitive motor development data, database software solutions such as REDCap or MySQL, and supercomputing resources for data-driven modeling of large multidimensional data sets. Technical support needs can include database development and management, creation and maintenance of scripts for automated quality control and harmonization processes, creation and maintenance of documentation and training resources for researchers and data contributors, and personnel with expertise in statistical analysis of large and/or longitudinal data sets. Regulatory support needs can include legal professionals with expertise in national or international data-sharing agreements, institutional ethics boards well-versed in current best practices for data sharing, and support from funding agencies.

These needs can be addressed through data-sharing initiatives that seek to shift the burden from individual researchers to a consortium, creating a more sustainable, accessible model for motor development research. For example, COMBINE members benefit from access to centralized hardware, software, technical, and regulatory support in a no-cost consortium model. In this model, participating researchers contribute data and/or expertise in exchange for access to shared infrastructure, without paying membership fees. Operational costs are covered by institutional and grant-based funding, allowing the model to remain independent of direct member contributions while maintaining sustainability. Most motor development data-sharing initiatives rely on institutional or grant-based funding to build and maintain these resources. As consortia like MO|RE data and COMBINE grow, so does the network of infrastructure available to sustain data-sharing efforts and to demonstrate value to institutions and funding agencies that provide crucial long-term financial support.

Fostering Trust and Collaboration

Despite growing recognition of the benefits of open science, many researchers remain hesitant to share their data (Keller et al., 2025; Tenopir et al., 2020). Apart from the ethical and regulatory considerations discussed previously, researchers commonly express concerns including loss of competitive advantage in publication; fear of improper use of context-dependent data leading to misinterpretation of results; and unclear data ownership, particularly in collaborative relationships.

To address researchers’ concerns and demonstrate safety and value, data-sharing frameworks need to incorporate clear, detailed access and use policies. These should include guidance for proposing data analyses to ensure that collaboration is prioritized over competition, as well as standards for authorship and resource citation. Consortia like COMBINE and MO|RE data encourage researchers to participate by offering coauthorship opportunities, secure access models, and standardized metadata requirements to ensure proper attribution and ethical use of shared data. Such consortia also encourage networking at scientific meetings to facilitate exchange of important contextual information about data, generate novel research questions, spark new collaborations, and improve interpretation of results, in turn building community and extending opportunities within and beyond our field.

By overcoming these challenges, motor development research can harness the full potential of data sharing, leading to more comprehensive analyses, international collaborations, and evidence-based interventions that support motor development across the lifespan.

Current Practices and Opportunities for Data Sharing

The evolving digital landscape presents several opportunities to enhance data sharing in motor development research. One of the most significant scientific benefits is the potential to merge multiple small data sets, many of which have been collected through labor-intensive field-testing processes. This would yield one or more generalizable, comprehensive data sets from which to generate norms, as others have done in similar large-scale projects, for example: Databrary (Gilmore et al., 2020), Adolescent Brain Cognitive Development study (Volkow et al., 2018), fitness landscape (Ortega et al., 2023), European normative values for physical fitness (Tomkinson et al., 2018), and FitBack platform (Sorić et al., 2025). Given the complexity and variability of motor development across different populations and age groups, pooling data sets from diverse studies can establish more robust benchmarks for motor development assessments. This approach enables researchers to track long-term developmental trends, detect population-level changes, and refine assessment tools such as the Movement Assessment Battery for Children, the Bruininks–Oseretsky Test of Motor Proficiency, and the German Motor Test.

In addition to creating more comprehensive normative values, data sharing facilitates the integration of contextual factors that influence motor development. By linking motor development data with variables such as individual demographic factors, physical activity levels, diagnoses, environmental exposures, and health indicators, researchers can develop a more holistic understanding of motor skill acquisition and its influencing factors (Stodden et al., 2008). Large-scale, multidimensional data sets allow for the use of advanced modeling techniques, for example, cluster analyses and deep learning algorithms, that are optimal for discovering new associations between motor competence and external determinants.

To maximize the usability of shared data, the current best practices follow the FAIR guiding principles, four key characteristics that facilitate data sharing (Wilkinson et al., 2016). These principles state that data must be findable, accessible, interoperable, and reusable. Findable data are those which are globally registered and indexed within the data set and are described with rich metadata. Accessible data are retrievable with a universally implementable protocol for authorized users. Access to the database is possible after registration with an email address. For data set uploads, different license levels can be selected: Creative Commons licenses for public data and scientific usefile licenses for scientific purposes only. This framework ensures both transparency and legal clarity with regard to data reuse. Interoperable data are described with a vocabulary that is formal, accessible, shared, and broadly applicable. Specifically, in the case of MO|RE data, the minimum requirement is that at least one motor test task must be collected according to the prescribed motor test protocol. These data sets are then pooled directly on the MO|RE data platform. It is therefore essential that motor performance data are obtained using internationally recognized protocols for motor performance assessment. These requirements are aligned with established standards for motor field testing and ensure a certain degree of consistency and comparability of the data. In addition, MO|RE data is currently developing an ontology that will allow the database to become partially machine-readable in the future. This not only facilitates interoperability but also enhances the potential for reuse by other researchers (Ondraszek et al., 2025).

Reusable data are richly described with accurate and relevant attributes, maintained at domain-relevant community standards, and associated with detailed provenance. Ensuring adherence to FAIR principles facilitates data integration across multiple studies and disciplines. Table 1 illustrates how the FAIR principles are implemented in the two practical examples, MO|RE data and COMBINE.

Table 1.

Implementation of the FAIR Principles in the Examples of MO|RE Data and COMBINE

FAIR criterion from Wilkinson et al. (2016)	MO|RE data	R	COMBINE	R
Findable
F1. (Meta)data are assigned a globally unique and persistent identifier	(Meta)data are assigned globally unique persistent DOIs.	+	(Meta)data are assigned globally unique persistent DOIs.	+
F2. Data are described with rich metadata (defined by R1 below)	Mapped motor performance data have standardized variables, units, and field types. Unmapped data are described with global metadata.	0	Data are described with rich metadata including variable, label, field type, validation, choices, calculations, etc.	+
F3. Metadata clearly and explicitly include the identifier of the data it describes	DOIs are integrated into the metadata, enabling unambiguous referencing.	+	Metadata for creation, update, and deletion of data include identifier of the data it describes. Metadata for digital objects are not associated with identifiers of the data.	0
F4. (Meta)data are registered or indexed in a searchable resource	Metadata follow the DataCite scheme and are indexed in RADAR4KIT.	0	Metadata are registered, indexed, and searchable for approved users.	+
Accessible
A1. (Meta)data are retrievable by their identifier using a standardized communications protocol	Data can be retrieved via HTTPS. Metadata are accessible via machine-readable PDF.	+	(Meta)data can be retrieved via REDCap API or REDCap GUI.	+
A1.1. The protocol is open, free, and universally implementable	HTTPS is open and freely accessible.	+	REDCap is universally implementable and free for approved users.	0
A1.2. The protocol allows for an authentication and authorization procedure, where necessary	Registration with an email address is required. Identification via institution is necessary for accessing scientific files.	+	Registration and approval by the COMBINE team are required.	+
A2. Metadata are accessible, even if data not available	Metadata are accessible without login.	+	Metadata are available prior to approval to access data.	+
Interoperable
I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation	(Meta)data are defined in a formal and shared user manual.	0	(Meta)data are defined in a formal and shared data dictionary and are recorded with broadly used REDCap logging standards.	+
I2. (Meta)data use vocabularies that use FAIR principles	Metadata vocabulary is systematically standardized according to the DataCite scheme.	0	Metadata structures are systematically standardized at data entry and maintained.	+
I3. Include qualified references to other (meta)data	Metadata references to other (meta)data is recommended.	−	Metadata include qualified references to related metadata. Data include data set and individual level identifiers.	+
Reusable
R1. (Meta)data are richly described with a plurality of accurate and relevant attributes	Metadata include around 35 variables, including 11 mandatory variables as authors, affiliation, license, and publication year.	0	Metadata are richly described. Primary variables of interest are well defined; however, there are mixed levels of definition of secondary variables.	0
R1.1. (Meta)data are released with a clear and accessible data usage license	Data are published under Creative Commons CC-BY 4.0, CC-BY SA 4.0, or Scientific Use File License, according to the decision of the data contributor.	+	Data are published under Creative Commons CC-BY-NC-SA 4.0 license.	+
R1.2. (Meta)data are associated with detailed provenance	Data are associated with provenance including author (mandatory), institution (mandatory), country, region, and postal code (all recommended).	0	Data are associated with detailed provenance including principal investigator and location of collection. More detailed provenance including data collector and qualifications could be provided. Metadata for creation, update, and deletion of (meta)data are associated with detailed provenance.	0
R1.3. (Meta)data meet domain-relevant community standards	Data are provided in common formats .xlsx and .xls. Metadata are provided in pdf format.	+	Data are provided in common formats (.csv, .xlsx, .sps, and .rda), and metadata are provided in .xml format.	+
R = Rating
+ Satisfactorily implemented
0 Implemented with room for improvement
− Needs further development
Rated accordingly:
- Wilkinson, M., Dumontier, M., Aalbersberg, I., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
- https://specs.fairdatapoint.org/fdp-specs-v1.2.html
- https://www.sciengine.com/DI/doi/10.1162/dint_a_00160

Note. REDCap = research electronic data capture; FAIR = findable, accessible, interoperable, and reusable; MO|RE = MOtor REsearch data repository; COMBINE = Consortium for Motor Behavior in Neurodivergence; API = application programming interface; DOI = digital object identifier; GUI = graphical user interface; HTTPS = Hypertext Transfer Protocol Secure.

Technological Advancements

Cloud-based storage solutions and secure data-sharing infrastructures have significantly improved the feasibility of sharing data, increasing the accessibility and visibility of our research. For less sensitive data, cloud-based storage solutions have been utilized for sharing across numerous fields (e.g., physics, genetics, and archaeology) on platforms including Zenodo and Open Science Framework. For sharing more sensitive data, secure, managed repositories (e.g., MO|RE data, COMBINE, and Databrary) can provide secure storage solutions, clear access protocols, and tools for data harmonization to be effective.

Secure, structured data management systems like REDCap, which has over three million users and is freely available for nonprofit organizations, rapidly scale up the ability to manage large data sets without extensive information technology infrastructure requirements. Platforms such as MO|RE data and COMBINE have already implemented structured data management systems to support researchers in securely storing and accessing motor performance data sets (Eberhardt et al., in review; Klemm et al., 2024). Moreover, the use of automated data harmonization tools (such as those implemented for COMBINE) ensures that data from different sources can be efficiently integrated and compared. Future innovations such as blockchain technology may also offer new possibilities for data integrity, ensuring transparent and tamper-proof data repositories. Blockchain is a decentralized digital ledger that stores data in linked blocks, each secured by cryptographic hashes. This structure makes records transparent, tamper-resistant, and verifiable across distributed networks. In research data management, blockchain has been discussed as a potential tool for ensuring secure and traceable records of data access and transactions.

Policy Support

The growing emphasis on open science policies is driving many funding agencies and research institutions to mandate open data practices. Initiatives like the European Open Science Cloud and the National Research Data Infrastructure have been instrumental in promoting structured data-sharing policies. Specifically, motor development research benefits from these policies as data sets become increasingly interoperable and accessible, allowing researchers to collaborate across institutions and disciplines (Keller et al., 2025; Krüger et al., 2023).

Encouraging collaborative data-sharing efforts and providing training in data management fosters a culture of openness. The establishment of international working groups, such as the German Society of Sport Science’s research data management committee, plays a crucial role in educating researchers about open data practices (Keller et al., 2025; Krüger et al., 2023).

Enhanced Reproducibility

Transparent data-sharing practices improve the credibility and replicability of motor development research. Given the heterogeneity of test protocols and measurement tools, ensuring that data sets adhere to FAIR principles is essential (Wilkinson et al., 2016). Platforms like MO|RE data and COMBINE have implemented multitiered data quality checks, including peer-reviewed data validation, to ensure that shared data sets maintain high scientific standards. Open-access repositories also help to reduce publication bias by making negative or null findings publicly available, ultimately contributing to a more comprehensive and balanced body of evidence in motor development research.

As data-sharing practices continue to evolve, integrating these opportunities into motor development research will not only enhance methodological rigor but also accelerate scientific discovery in understanding and improving motor competence across diverse populations.

Practical Examples for Motor Development Research: MO|RE Data and COMBINE

MO|RE data and COMBINE represent motor development data-sharing initiatives at two different stages of development and scopes. Established in 2014, MO|RE data is an example of the large scale at which this type of data-sharing effort is feasible for a broad research scope. Established in 2022, COMBINE is an example of how this type of effort can also be successfully applied to a narrower scope such as a specific population like neurodivergence.

As of 2025, MO|RE data includes 47 data sets, representing approximately 90,434 participants from 26 contributing institutions. COMBINE holds data from more than 700 participants across more than 10 data sets, contributed by four institutions.

MO|RE Data: eResearch Infrastructure for Motor Performance Data and Research Data Center for Motor Performance

Studies across various scientific disciplines have demonstrated a strong willingness among researchers to share their own data and utilize data from others (Tenopir et al., 2020; Wallis et al., 2013; Whitlock, 2011). In sports science, this trend is particularly evident, with 81.7% of sports scientists in German-speaking countries expressing interest in data sharing and an even higher rate (91.5%) among those who generate their own data (Kloe et al., 2019).

Until 2013, no dedicated solution existed for managing, storing, and publishing data in sports science or for human performance test data in general. Recognizing this gap, the MO|RE data project (https://motor-research-data.de/) was initiated in 2014, with funding from the German Research Foundation. The project aimed to establish an eResearch infrastructure for motor research data, allowing scientists from different disciplines to contribute their data sets to address broad interdisciplinary research questions (see Figure 1). These include investigations into the relationship between motor abilities and health-related factors.

Figure 1 —

Purpose and functions of the data repository MO|RE data (illustration by T. Eberhardt). MO|RE = MOtor REsearch data repository.

Opportunities

The repository is designed based on national and international research standards. Test items have been carefully selected to align with both German and international research practices, fostering global cooperation in data sharing. By building networks and encouraging collaboration, the repository continues to expand, providing a valuable resource for researchers in sports science and motor performance studies.

Challenges

Despite its many benefits, data sharing in motor research faces some challenges. Due to strict data protection regulations, it is currently not possible to openly publish critical health-related information such as blood pressure or personal details like geolocation and socioeconomic status. Even with anonymization, some variables—such as precise geolocation—can facilitate reidentification when combined with other data sets, necessitating restricted access. Addressing these privacy concerns, the project is actively working on developing secure data access solutions that will allow researchers to use sensitive information while ensuring compliance with legal frameworks (e.g., remote access solutions or safe rooms).

Another limitation is that the database is not yet widely used internationally, limiting its global impact. Expanding international collaborations and promoting the repository beyond German-speaking countries is a key goal moving forward.

Outlook and Relevance

MO|RE data plays a crucial role in facilitating data sharing and advanced analysis, significantly contributing to motor performance research. By providing structured and accessible data sets, the repository enables researchers to conduct interdisciplinary studies, linking motor skills development with broader health and educational outcomes. The initiative opens new research avenues, fostering a deeper understanding of human motor performance and its implications for public health, education, and sports science.

COMBINE: Aggregating a Transdiagnostic, Lifespan Motor Development Data Set

The COMBINE (https://combinedata.org/) is an international data-sharing initiative dedicated to understanding neurodivergent motor behavior across the lifespan. By collaborating with researchers worldwide, COMBINE aims to create a comprehensive database that enhances the generalizability and replicability of findings in this field (Figure 2).

Figure 2 —

COMBINE data contribution and use structure. BOT = Bruininks-Oseretsky Test of Motor Proficiency; COMBINE = Consortium for Motor Behavior in Neurodivergence; IRB = Institutional Review Board; MABC = Movement Assessment Battery for Children; PDMS = Peabody Developmental Motor Scales.

Opportunities

COMBINE offers consortium members access to a global data set, facilitating large-scale analyses and cross-disciplinary collaborations. COMBINE includes data from across the lifespan (e.g., children, adolescents, and adults) as well as across a wide-range of developmental contexts, including autism, developmental coordination disorder/dyspraxia, attention disorders, intellectual disability, and others. This resource enables researchers to identify developmental patterns within and between diagnostic groups, advancing knowledge of neurodivergent motor behavior.

It is well known that motor skills are a common signature of neurodivergence. However, motor behavior is still often overlooked by both researchers and practitioners, which is a disservice to neurodivergent individuals. COMBINE provides a powerful overview of neurodivergent motor behavior and promotes translational knowledge that can impact the process of assessment, interventions, and services for those who need it.

Challenges

Key challenges for COMBINE include ensuring data privacy and standardization across diverse data sets. Standardizing data from various sources necessitates meticulous coordination to maintain data integrity and comparability. To maximize relevance of key outcome measures to the population of interest (neurodivergence), COMBINE is harmonized on one of three motor development tests optimized to identify motor problems or clinically significant motor differences (Burgess et al., 2025; Wuang et al., 2012). As new versions of tests are released (e.g., Movement Assessment Battery for Children Third Edition and Bruininks–Oseretsky Test of Motor Proficiency Third Edition), these will be added to COMBINE as accepted harmonizing measures. As the field of motor development evolves, the COMBINE leadership team will continue to consider the addition of new core measures that align with the focus of the consortium. New and existing consortium members are provided with customized data contribution templates, and semiautomated processing scripts are used to ensure that data contributed to COMBINE meet stringent requirements. This process is somewhat labor intensive for the COMBINE leadership team as new users join from countries and institutions with diverse regulatory requirements. As global representation increases among consortium members, this process will become more streamlined and replicable, reducing the need for new data management processes and regulatory support.

Recruitment of consortium members is also an ongoing challenge, both for COMBINE and for other similar platforms, given that many researchers and clinicians must manage high productivity expectations and workloads from their institutions. Professional meetings offer excellent settings in which to identify new members and generate new research questions. In particular, the 2024 joint meeting of the International Motor Development Research Consortium and the International Society for Advocacy and Research for developmental coordination disorder offered a unique opportunity for those studying neurotypical and neurodivergent motor development to exchange ideas and build collaborative relationships. Future joint meetings, workshops, and special interest groups may aid in building community and identifying research teams committed to the pursuit of open science. Further, collaborative grant proposals can generate revenue to support effort for researchers, students, and clinicians in order to protect the time needed to contribute to and maximize the use of data-sharing resources.

Outlook and Relevance

By promoting data sharing and collaboration, COMBINE has the potential to significantly advance research in neurodivergent motor behavior. The consortium’s efforts can lead to improved methodologies and interventions, benefiting individuals with neurodivergent motor profiles.

Most importantly, the COMBINE consortium can make a meaningful impact on the community of researchers and practitioners working with neurodivergent individuals: by providing a clear, strong, and direct overview of motor behavior in neurodivergence, consortium members can make the case for changes in the public setting and policy. Isolated studies hardly influence these aspects of translation, but a combined large and comprehensive data set provides the horsepower for meaningful change.

Implications for Researchers

For motor development researchers, embracing data sharing requires a proactive and strategic approach. Given the complexity of motor performance assessment and the ethical considerations involved in working with children and neurodivergent populations, researchers must develop clear protocols for ethical data management, informed consent, and anonymization to comply with regulations such as GDPR (Krüger et al., 2023).

By adhering to best practices in data sharing, leveraging existing initiatives such as MO|RE data and COMBINE, and actively participating in standardization efforts (e.g., Youth International Fitness Test), researchers can contribute to a more transparent, reproducible, and collaborative scientific environment. The use of structured metadata, harmonization frameworks, and secure data repositories ensures that data remains accessible and interpretable across different studies and disciplines.

A key area for future research is the development of automated data-sharing frameworks that facilitate secure and ethical data exchange. Machine learning algorithms and blockchain-based data management could enhance data security, interoperability, and scalability in motor development research. Moreover, international collaborations should work toward unifying assessment tools and protocols, ensuring that longitudinal and cross-sectional data sets can be effectively pooled for large-scale analyses.

In addition, the creation of incentive structures for data sharing, such as coauthorship opportunities and funding mechanisms tied to open data initiatives, could help overcome reluctance among researchers. Early-career researchers who have compelling ideas but who do not yet have funding or infrastructure for primary data collection can benefit tremendously from access to shared data sets. These secondary analyses may provide a springboard to launch their program of work or support new applications for funding. Funding agencies, journal publishers, and research institutions should continue promoting open data policies and recognizing data contributions as valuable scientific outputs.

Conclusion and Outlook

Data sharing is a cornerstone of scientific progress in motor development research, offering unprecedented opportunities to enhance methodological rigor, cross-study comparability, and interdisciplinary collaboration. By integrating data sets from diverse sources, researchers can develop more comprehensive normative values, track longitudinal motor development trends, and identify environmental and behavioral factors influencing motor competence.

However, realizing the full potential of data sharing requires careful consideration of ethical, technical, and cultural barriers. Issues related to participant privacy, data set standardization, and infrastructure sustainability must be addressed through transparent governance models, secure data-sharing platforms, and interdisciplinary cooperation.

International collaboration in motor development research, as exemplified by MO|RE data and COMBINE, represents a significant opportunity to harmonize global data-sharing practices. Because data protection regulations vary across countries, fostering an international dialogue is essential to establishing common ethical and legal frameworks that allow researchers to share data securely while adhering to national and regional requirements.

A major challenge in motor development research has been the fragmentation of data sets due to differing privacy laws, methodological standards, and access restrictions. By working together, initiatives like MO|RE data and COMBINE facilitate discussions on best practices for data governance, standardization, and legal compliance. These efforts can help streamline data access protocols, ensuring that researchers from different jurisdictions can contribute to and benefit from large-scale, high-quality data sets.

To further strengthen international collaboration, development of these joint initiatives among leading researchers in the field are currently underway. These initiatives aim to establish global platforms that provide researchers with the necessary tools, guidelines, and infrastructure to overcome legal and technical barriers associated with cross-border data sharing. By fostering transparent and structured data-sharing policies, these initiatives seek to ensure that motor development researchers worldwide can access and contribute to comprehensive, standardized data sets that drive scientific progress and evidence-based interventions. Interested researchers are warmly invited to participate in these initiatives, and the authors encourage direct contact for further discussion and collaboration opportunities.

By adopting best practices, leveraging collaborative initiatives such as MO|RE data and the COMBINE project, and aligning with global standardization efforts, researchers can establish a more open and inclusive research ecosystem. The future of motor development research depends on harnessing shared data to drive scientific discovery, inform policy, and ultimately improve motor skills development across populations and life stages.

Key Points.

• Data sharing enhances transparency, reproducibility, and collaboration in motor development research, enabling larger and more diverse analyses than individual studies can achieve.

• Ethical, technical, and legal challenges—especially related to privacy and standardization—require structured frameworks and secure repositories for sustainable data sharing.

• The MO|RE data and COMBINE initiatives demonstrate practical solutions for implementing FAIR data principles in motor development research.