In the age of digital transformation, scientific and social interest for data and data products is constantly on the rise. The quantity as well as the variety of digital research data is increasing significantly. This raises the question about the governance of this data. For example, how to store the data so that it is presented transparently, freely accessible and subsequently available for re-use in the context of good scientific practice. Research data repositories provide solutions to these issues.

Considering the variety of repository software, it is sometimes difficult to identify a fitting solution for a specific use case. For this purpose a detailed analysis of existing software is needed. Presented table of requirements can serve as a starting point and decision-making guide for choosing the most suitable for your purposes repository software. This table is dealing as a supplementary material for the paper "How to choose a research data repository software? Experience report." (persistent identifier to the paper will be added as soon as paper is published).

A dataset of metadata for UK academic institutional repositories, including a census of research software contained.

    URL
    The OAI url


    id
    CORE Identifier


    openDoarId
    Open DOAR identifier


    name
    Name of repository


    Russell_member
    If the university is a member of the Russell Group of research intensive universities


    RSE_group
    If an RSE group is present (based on Soc of RSE data)


    email
    Redacted


    uri
    Not used


    uni_sld
    Second level domain (the part of the url between . And .ac.uk


    homepageUrl
    University website


    source
    Not used


    ris_software
    the Research Information System software used


    ris_software_enum
    Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)


    metadataFormat
    the protocol used for metadata


    createdDate
    Repository creation date


    location
    location of university


    logo
    University logo (resolves in error)


    type
    Only = Repository for this dataset. Can be = journal etc.


    stats
    Not used


    contains_software_set
    Whether the OAI-PMH software set is present in the repository.


    Num_sw_records
    The response of the OAI-PMH query for software (erroneous as discussed in paper)


    Error
    The category of error returned by the experiment’s OAI-PMH queries (see paper)


    Manual_Num_sw_records
    The true amount of software contained in the repository as found by a manual exhaustive search of each university website


    Category
    Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar

This dataset contains raw data and processed data from the Dataverse Community Survey 2022. The main goal of the survey was to help the Global Dataverse Community Consortium (GDCC; https://dataversecommunity.global/) and the Dataverse Project (https://dataverse.org/) decide on what actions to take to improve the Dataverse software and the larger ecosystem of integrated tools and services as well as better support community members. The results from the survey may also be of interest to other communities working on software and services for managing research data. The survey was designed to map out the current status as well as the roadmaps and priorities of Dataverse installations around the world. The main target group for participating in the survey were the people/teams responsible for operating Dataverse installations around the world. A secondary target group were people/teams at organizations that are planning to deploy or considering deploying a Dataverse installation. There were 34 existing and planned Dataverse installations participating in the survey.

Software tools used to collect and analyze data. Parentheses for analysis software indicate the tools participants were taught to use as part of their education in research methods and statistics. “Other” responses for data collection software were largely comprised of survey tools (e.g. Survey Monkey, LimeSurvey) and tools for building and running behavioral experiments (e.g. Gorilla, JsPsych). “Other” responses for data analysis software largely consisted of neuroimaging-related tools (e.g. SPM, AFNI).

This dataset contains the results of an online survey run in summer of 2017 on the research data management (RDM) practices and perceptions of researchers using magnetic resonance imaging (MRI) to study human neuroscience (N=144). The dataset includes responses to multiple choice questions ordered roughly according the phases of a typical research project including data collection, analysis, and sharing. It focuses on a range of RDM topics, including the type of data collected, software and tools used to analyze and manage data, and the degree to which data management practices are standardized within a research group. It also includes participant ratings on the maturity of their data management practices and those of the field at large on a 1-5 scale from ad hoc to refined and responses about perceptions of new scholarly communications practices including data sharing, data reuse, and Open Access publishing.The survey instrument used can be found at the link in the reference below.

We conducted an analysis to confirm our observations that only a very small percentage of public research data is hosted in the Institutional Data Repositories, while the vast majority is published in the open domain-specific and generalist data repositories.

For this analysis, we selected 11 institutions, many of which have been our evaluation partners. For each institution, we counted the number of datasets published in their Institutional Data Repository (IDR) and tracked the number of public research datasets hosted in external data repositories via the Data Monitor API. External tracking was based on the corpus of 14+ mln data records checked against the institutional SciVal ID. One institution didn’t have an IDR.

We found out that 10 out of 11 institutions had most of their public research data hosted outside of their institution, where by research data we mean not only datasets, but a broader notion that includes, for example, software.

We will be happy to expand it by adding more institutions upon request.

Note: This is version 2 of the earlier published dataset. The number of datasets published and tracked in the Monash Institutional Data Repository has been updated based on the information provided by the Monash Library. The number of datasets in the NTU Institutional Data Repository now includes datasets only. Dataverses were excluded to avoid double counting.

The User Research Repositories Software market, valued at $406.6 million in 2025, is experiencing robust growth fueled by the increasing need for efficient data management and collaboration in user research. The shift towards digitalization across various sectors, including healthcare, finance, and e-commerce, is driving demand for centralized platforms to store, analyze, and share user research findings. Cloud-based solutions are gaining significant traction due to their scalability, accessibility, and cost-effectiveness compared to on-premise deployments. Key market trends include the integration of AI and machine learning for advanced analysis, the rise of collaborative features enhancing team workflows, and a growing emphasis on data security and compliance. While the market faces challenges like data privacy concerns and the need for user training, the overall outlook remains positive, indicating sustained growth throughout the forecast period (2025-2033). The diverse range of applications across various industries further solidifies the market's potential. Companies like Productboard, Dovetail, and others are leading the innovation in this space, constantly enhancing their platforms to meet evolving user needs. The competitive landscape is characterized by a mix of established players and emerging startups, each offering unique features and functionalities. Successful vendors differentiate themselves through superior data organization capabilities, intuitive interfaces, seamless integrations with other research tools, and robust security protocols. Geographical expansion, particularly in developing economies, also presents significant opportunities for growth. The market is expected to witness further consolidation as companies strive to offer comprehensive solutions catering to a wider range of client needs. This market's growth is closely tied to the broader adoption of user-centric design principles across industries, ensuring a positive trajectory for the foreseeable future.

Clinical Trial Data Repository Market Outlook

The global clinical trial data repository market size was estimated to be approximately $1.8 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 9.5% to reach around $4.1 billion by 2032. The primary growth factors include the increasing volume and complexity of clinical trials, rising need for efficient data management systems, and stringent regulatory requirements for data accuracy and integrity. The advent of advanced technologies such as artificial intelligence and big data analytics further drives market expansion by enhancing data processing capabilities and providing actionable insights.

The growth of the clinical trial data repository market is significantly influenced by the increasing number of clinical trials being conducted globally. With the rise in chronic diseases, the need for innovative treatments and therapies has surged, leading to an upsurge in clinical trials. This increase in clinical trials necessitates robust data management systems to handle vast amounts of data generated, thereby propelling the demand for clinical trial data repositories. Moreover, the complexity of modern clinical trials, which often involve multiple sites and diverse patient populations, further amplifies the need for sophisticated data management solutions.

Another critical driver for the market is the stringent regulatory landscape governing clinical trial data. Regulatory bodies such as the FDA, EMA, and other local authorities mandate rigorous data management standards to ensure data integrity, accuracy, and accessibility. These regulations necessitate the adoption of advanced data repository systems that can comply with regulatory requirements, thereby fueling market growth. Additionally, regulatory frameworks are becoming increasingly stringent, prompting pharmaceutical and biotechnology companies to invest in state-of-the-art data management systems to avoid compliance issues and potential financial penalties.

Technological advancements play a pivotal role in the market's growth. The integration of artificial intelligence, machine learning, and big data analytics into data repository systems enhances data processing and analysis capabilities. These technologies enable real-time data monitoring, predictive analytics, and improved decision-making, thereby improving the efficiency of clinical trials. Furthermore, the shift towards cloud-based solutions offers scalability, flexibility, and cost-effectiveness, making advanced data management systems accessible to even small and medium-sized enterprises.

Regionally, North America dominates the clinical trial data repository market owing to its robust healthcare infrastructure, high R&D investments, and presence of major pharmaceutical and biotechnology companies. Europe follows closely due to stringent regulatory standards and a strong focus on clinical research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to increasing clinical trial activities, growing healthcare expenditure, and the rising adoption of advanced technologies. Latin America and the Middle East & Africa are also likely to experience growth, albeit at a slower pace, driven by improving healthcare systems and increasing focus on clinical research.

Component Analysis

The clinical trial data repository market is segmented by components into software and services. The software segment is anticipated to hold a significant share of the market due to the essential role software plays in data management. Advanced software solutions offer capabilities such as data storage, management, retrieval, and analysis, which are critical for effective clinical trial management. The integration of AI and machine learning algorithms into these software systems further enhances their efficiency by enabling predictive analytics and real-time monitoring, thus driving the software segment's growth.

Software solutions in clinical trial data repositories also offer interoperability, enabling seamless integration with other clinical trial management systems (CTMS) and electronic data capture (EDC) systems. This interoperability is crucial for ensuring data consistency and accuracy across different platforms, thereby enhancing overall data management. Additionally, the increasing adoption of cloud-based software solutions provides scalability, cost-effectiveness, and remote acce

The User Research Repositories Software market is experiencing robust growth, projected to reach $182 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 14.3% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of user-centric design methodologies across diverse industries, including software, e-commerce, and healthcare, is a primary driver. Businesses are recognizing the critical role of organized user research in improving product development and customer satisfaction. Furthermore, the rising complexity of software applications and the need for efficient data management are contributing to the market's growth. The demand for centralized repositories that streamline the research process, enhance collaboration among teams, and facilitate easy access to valuable user insights is driving the adoption of these specialized software solutions. The competitive landscape features both established players like Productboard and Dovetail Research, and emerging innovators like Savio.io and Tetra Insights, indicating a dynamic and rapidly evolving market. The market’s growth is further aided by the increasing availability of sophisticated analytics tools integrated within these platforms, allowing researchers to derive actionable insights from user data more effectively. The market segmentation, while not explicitly detailed, likely encompasses various deployment models (cloud-based, on-premise), pricing tiers, and functionalities catering to different team sizes and research needs. Geographic expansion is expected to be a significant contributor to growth, with regions exhibiting strong digital adoption rates showing higher demand. Challenges might include the need for comprehensive user training, the integration of these platforms with existing research workflows, and the ongoing need to ensure data security and privacy compliance. However, the overall market outlook remains positive, driven by the sustained demand for improved user experiences and the efficiency gains provided by specialized user research repositories.

Collected in this dataset are the slideset and abstract for a presentation on Toward a Reproducible Research Data Repository by the depositar team at International Symposium on Data Science 2023 (DSWS 2023), hosted by the Science Council of Japan in Tokyo on December 13-15, 2023. The conference was organized by the Joint Support-Center for Data Science Research (DS), Research Organization of Information and Systems (ROIS) and the Committee of International Collaborations on Data Science, Science Council of Japan. The conference programme is also included as a reference.

Title

Toward a Reproducible Research Data Repository

Author(s)

Cheng-Jen Lee, Chia-Hsun Ally Wang, Ming-Syuan Ho, and Tyng-Ruey Chuang

Affiliation of presenter

Institute of Information Science, Academia Sinica, Taiwan

Summary of Abstract

The depositar (https://data.depositar.io/) is a research data repository at Academia Sinica (Taiwan) open to researhers worldwide for the deposit, discovery, and reuse of datasets. The depositar software itself is open source and builds on top of CKAN. CKAN, an open source project initiated by the Open Knowledge Foundation and sustained by an active user community, is a leading data management system for building data hubs and portals. In addition to CKAN's out-of-the-box features such as JSON data API and in-browser preview of uploaded data, we have added several features to the depositar, including sourcing from Wikidata as dataset keywords, a citation snippet for datasets, in-browser Shapefile preview, and a persistent identifier system based on ARK (Archival Resource Keys). At the same time, the depositar team faces an increasing demand for interactive computing (e.g. Jupyter Notebook) which facilitates not just data analysis, but also for the replication and demonstration of scientific studies. Recently, we have provided a JupyterHub service (a multi-tenancy JupyterLab) to some of the depositar's users. However, it still requires users to first download the data files (or copy the URLs of the files) from the depositar, then upload the data files (or paste the URLs) to the Jupyter notebooks for analysis. Furthermore, a JupyterHub deployed on a single server is limited by its processing power which may lower the service level to the users. To address the above issues, we are integrating the BinderHub into the depositar. BinderHub (https://binderhub.readthedocs.io/) is a kubernetes-based service that allows users to create interactive computing environments from code repositories. Once the integration is completed, users will be able to launch Jupyter Notebooks to perform data analysis and vsualization without leaving the depositar by clicking the BinderHub buttons on the datasets. In this presentation, we will first make a brief introduction to the depositar and BinderHub along with their relationship, then we will share our experiences in incorporating interactive computation in a data repository. We shall also evaluate the possibility of integrating the depositar with other automation frameworks (e.g. the Snakemake workflow management system) in order to enable users to reproduce data analysis.

Keywords

BinderHub, CKAN, Data Repositories, Interactive Computing, Reproducible Research

The purpose of the national research data repository HARDMIN (Hellenic Academic Research Data Management Initiative) is to collect all research data generated by Greek researchers and academics. The repository aims to address the critical need for the secure storage and publication of research data from the Greek scientific community, to increase transparency in research, to enable reuse by interested researchers worldwide, to accelerate the digital transformation of the research field in our country, and to adopt competitive practices in research proposals and scientific communication. All researchers from Greek Universities are connected to the repository with their credentials and can easily upload their research data. Special teams of editors, either within an academic unit (e.g., laboratory head) or at an institutional level (e.g., Library staff), can make your data public, which will have permanent identifiers, the ability to link to your unique ORCiD identifier, and coupling with your published work, e.g., with a scientific journal article. For special cases, access can be controlled and provided upon request. HARDMIN has been developed with the open-source software CKAN and, together with HELIX, constitutes the national digital scientific infrastructure (eInfrastructure) software for providing catalog and repository services for scientific data, part of the infrastructure network for Open Science. The repository will have the ability to connect to existing repositories and retrieve the corresponding data from already existing collections. The repository is accessible at https://hardmin.heal-link.gr, and interested researchers can contact their local Library for more details. Currently, the repository is operating on a pilot basis to resolve technical details. Translated from Greek Original Text: Σκοπός της λειτουργίας του εθνικού αποθετηρίου ερευνητικών δεδομένων HARDMIN (Hellenic Academic Research Data Management Initiative)είναι η συγκέντρωση του συνόλου των ερευνητικών δεδομένων που δημιουργούνται από Έλληνες ερευνητές και ακαδημαϊκούς. Το αποθετήριο έρχεται να καλύψει την καίρια ανάγκη ασφαλούς φύλαξης και δημοσίευσης ερευνητικών δεδομένων της ελληνικής επιστημονικής κοινότητας για την αύξηση της διαφάνειας στην έρευνα, τη δυνατότητα επαναχρησιμοποίησης από τους ενδιαφερόμενους ερευνητές ανά τον κόσμο, της επιτάχυνσής του ψηφιακού μετασχηματισμού του ερευνητικού πεδίου στη χώρα μας και την υιοθέτηση ανταγωνιστικών πρακτικών στον στίβο των ερευνητικών προτάσεων και της επιστημονικής επικοινώνησης. Στο αποθετήριο συνδέονται όλοι οι ερευνητές των ελληνικών Πανεπιστημίων με τα διαπιστευτήριά τους και μπορούν να αναρτήσουν με ευκολία τα ερευνητικά τους δεδομένα. Ειδικές ομάδες συντακτών, είτε εντός μιας ακαδημαϊκής μονάδας (π.χ. υπεύθυνος εργαστηρίου), είτε σε ι δρυματικό επίπεδο (π.χ. προσωπικό Βιβλιοθήκης), μπορούν να καταστήσουν δημόσια τα δεδομένα σας, τα οποία θα διαθέτουν μόνιμα αναγνωριστικά, δυνατότητες διασύνδεσης με το μοναδικό σας αναγνωριστικό ORCiD και σύζευξης με το δημοσιευμένο σας έργο, π.χ. με ένα άρθρο επιστημονικού περιοδικού. Για ειδικές περιπτώσεις, η πρόσβαση μπορεί να είναι ελεγχόμενη και να παρέχεται κατόπιν αιτήματος. Το HARDMIN έχει αναπτυχθεί με το ανοικτό λογισμικό CKAN και αποτελεί, μαζί με το HELIX την εθνική ψηφιακή επιστημονική υποδομή (eInfrastructure) λογισμικού για την παροχή υπηρεσιών καταλόγου και αποθετηρίου επιστημονικών δεδομένων, μέρος του πλέγματος υποδομών για την Ανοικτή Επιστήμη. Το αποθετήριο θα διαθέτει τη δυνατότητα σύνδεσης με τα υφιστάμενα αποθετήρια και άντλησης των αντίστοιχων δεδομένων από ήδη υπάρχουσες συλλογές. Το αποθετήριο είναι προσβάσιμο από τη διεύθυνση https://hardmin.heal-link.gr, ενώ οι ενδιαφερόμενοι ερευνητές μπορούν να επικοινωνούν με την οικεία Βιβλιοθήκη τους για περισσότερες λεπτομέρειες. Αυτή τη στιγμή, το αποθετήριο λειτουργεί πιλοτικά για τη διευθέτηση τεχνικών λεπτομερειών.

Project portal for publishing, citing, sharing and discovering research data. Software, protocols, and community connections for creating research data repositories that automate professional archival practices, guarantee long term preservation, and enable researchers to share, retain control of, and receive web visibility and formal academic citations for their data contributions. Researchers, data authors, publishers, data distributors, and affiliated institutions all receive appropriate credit. Hosts multiple dataverses. Each dataverse contains studies or collections of studies, and each study contains cataloging information that describes the data plus the actual data files and complementary files. Data related to social sciences, health, medicine, humanities or other sciences with an emphasis in human behavior are uploaded to the IQSS Dataverse Network (Harvard). You can create your own dataverse for free and start adding studies for your data files and complementary material (documents, software, etc). You may install your own Dataverse Network for your University or organization.

The Evidence-Based Software Portfolio Management (EBSPM) Research Repository is a collection of data of finalized software projects in four different software companies. The repository is part of the EBSPM approach as documented in the PhD-thesis EBSPM by Hennie Huijgens (2017).

User Research Repositories Software Market Outlook

The global user research repositories software market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a CAGR of 15.2% during the forecast period. The growth of this market is driven by increasing digitization and the need for businesses to better understand customer behavior and preferences. The expanding adoption of data-driven decision-making processes in enterprises is fuelling the demand for user research repositories software. With businesses increasingly focusing on customer-centric strategies, the market for these software solutions is expected to witness substantial growth.

One of the key growth factors fueling the user research repositories software market is the accelerating adoption of digital transformation initiatives across various industries. Organizations are increasingly recognizing the importance of understanding customer preferences and behaviors to enhance customer experience and drive business value. User research repositories software provides a centralized platform for storing, managing, and analyzing qualitative and quantitative user data, enabling organizations to make informed decisions based on comprehensive user insights. This growing emphasis on customer-centric strategies is significantly driving the demand for user research repositories software.

Another critical factor contributing to the market’s growth is the rising integration of artificial intelligence (AI) and machine learning (ML) technologies within these software solutions. AI and ML algorithms enhance the capabilities of user research repositories software by automating data analysis and generating actionable insights. These advanced technologies enable organizations to uncover hidden patterns, trends, and correlations in user data, thereby facilitating more accurate and data-driven decision-making processes. The incorporation of AI and ML functionalities in user research repositories software is expected to drive its adoption across various sectors, further boosting market growth.

The growing popularity of remote work and the increasing use of digital collaboration tools are also propelling the demand for user research repositories software. With the shift towards remote and hybrid work models, organizations are relying more on digital tools to conduct user research and gather feedback from distributed teams and customers. User research repositories software allows teams to collaborate seamlessly, share insights, and maintain a centralized repository of user data, irrespective of geographical locations. This trend is expected to continue, driving the adoption of user research repositories software in the coming years.

Regionally, North America is anticipated to hold the largest share of the user research repositories software market, followed by Europe and the Asia Pacific. The presence of major market players and the early adoption of advanced technologies in North America are significant factors contributing to this dominance. Additionally, the increasing focus on customer experience management and the robust IT infrastructure in the region are driving the market’s growth. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to rapid digital transformation and the growing adoption of user research tools in emerging economies.

Component Analysis

The user research repositories software market is segmented into software and services based on components. The software segment holds a significant share of the market due to its essential role in enabling organizations to collect, manage, and analyze user data effectively. This segment includes various types of software solutions, such as cloud-based platforms and on-premises software, that cater to different organizational needs. The increased adoption of digital tools for user research has led to a surge in demand for these software solutions, contributing to the overall market growth.

Within the software segment, cloud-based user research repositories software is witnessing rapid adoption across industries. Cloud-based solutions offer several advantages, including scalability, flexibility, and cost-effectiveness, making them an attractive choice for organizations of all sizes. These solutions allow businesses to store vast amounts of user data securely and access it from anywhere, facilitating remote

Thanks to a variety of software services, it has never been easier to produce, manage and publish Linked Open Data. But until now, there has been a lack of an accessible overview to help researchers make the right choice for their use case. This dataset release will be regularly updated to reflect the latest data published in a comparison table developed in Google Sheets [1]. The comparison table includes the most commonly used LOD management software tools from NFDI4Culture to illustrate what functionalities and features a service should offer for the long-term management of FAIR research data, including:

• ConedaKOR
• LinkedDataHub
• Metaphacts
• Omeka S
• ResearchSpace
• Vitro
• Wikibase
• WissKI

The table presents two views based on a comparison system of categories developed iteratively during workshops with expert users and developers from the respective tool communities. First, a short overview with field values coming from controlled vocabularies and multiple-choice options; and a second sheet allowing for more descriptive free text additions. The table and corresponding dataset releases for each view mode are designed to provide a well-founded basis for evaluation when deciding on a LOD management service. The Google Sheet table will remain open to collaboration and community contribution, as well as updates with new data and potentially new tools, whereas the datasets released here are meant to provide stable reference points with version control.

The research for the comparison table was first presented as a paper at DHd2023, Open Humanities – Open Culture, 13-17.03.2023, Trier and Luxembourg [2].

[1] Non-editing access is available here: docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link To get editing access contact the authors.

[2] Full paper will be made available open access in the conference proceedings.

The Scientific Data Management Software market is poised for significant growth, driven by the increasing need for efficient and effective management of scientific data in various industries. The market is expected to reach a value of USD XXX million by 2033, growing at a CAGR of XX% during the forecast period 2025-2033. Factors such as the rising volume and complexity of scientific data, the adoption of cloud-based solutions, and the increasing focus on data-driven research and development are推动市场增长. Additionally, the demand for software capable of handling large and diverse datasets, providing data visualization and analytics capabilities, and ensuring data security and compliance is fueling market growth. Key market trends include the growing adoption of cloud-based software, the increasing integration with artificial intelligence (AI) and machine learning (ML) technologies, and the rising demand for software that supports real-time data analysis and visualization. Prominent players in the market include Benchling, LabCollector, Labguru, BioData, SciNote, OpenLab, Thermo Fisher Scientific, MediaLab, Uncountable, LabArchives, MediaLab, Genemod, Shimadzu, SciCord, Arxspan, Labstep, MedGrid, Rspace, and others. The market is segmented by type (cloud-based, on-premises), application (biological and life sciences, chemistry and material science, environmental science, others), and region (North America, South America, Europe, the Middle East & Africa, Asia Pacific). Scientific Data Management (SDM) software enables researchers to organize, analyze, and share their data in an efficient and user-friendly way. It helps researchers optimize their research processes, increase the impact of their findings, and accelerate the pace of discovery.

Software toolkit which automates the loading, storage, linkage and provision of data sets. It also cleans, transforms and documents provenance meta-data and domain knowledge to make data sets “research ready”.

The Scientific Data Management Software (SDMS) market is experiencing robust growth, driven by the increasing volume and complexity of scientific data generated across various research domains. The market, estimated at $2.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This growth is fueled by several key factors. The rising adoption of cloud-based SDMS solutions offers enhanced scalability, accessibility, and collaboration capabilities, attracting researchers in academia and industry alike. Furthermore, stringent regulatory compliance requirements in sectors like pharmaceuticals and biotechnology are compelling organizations to invest in robust SDMS solutions for efficient data management and audit trails. The trend towards data-driven discovery and the integration of artificial intelligence (AI) and machine learning (ML) in scientific workflows further accelerates the market's expansion. Different application segments, including biological and life sciences, chemistry and material science, and environmental science, contribute significantly to the market's overall growth. The competitive landscape is marked by a blend of established players offering comprehensive solutions and emerging startups focusing on niche applications or innovative features. While the initial investment in SDMS implementation can be a barrier for some organizations, the long-term benefits in terms of improved efficiency, reduced costs, and enhanced data security outweigh the upfront expenses. The North American market currently holds a dominant share, attributed to the high concentration of research institutions, pharmaceutical companies, and biotechnology firms in the region. However, significant growth is anticipated in the Asia-Pacific region, driven by increasing government investments in research and development, coupled with the growing adoption of advanced technologies across various industries. The on-premises deployment model remains prevalent, particularly in organizations with stringent data security concerns. However, the cloud-based segment is poised for rapid expansion, driven by its flexibility, cost-effectiveness, and accessibility. Segmentation by application area (Biological and Life Sciences, Chemistry and Material Science, Environmental Science, and Others) and deployment type (Cloud-Based and On- Premises) provides a granular understanding of the market's dynamics. Continued innovation in areas such as data visualization, integration with laboratory instruments, and advanced analytics will be crucial for vendors to maintain a competitive edge in this dynamic market.

Since 2018, the Specialised Information Service for Mobility and Transport Research (FID move) has been established by the Saxon State and University Library Dresden (SLUB) and the TIB – Leibniz Information Centre for Science and Technology as part of the DFG funding program "Specialised Information Services for Science". The FID move aims to develop and establish services and online tools in close consultation with the transport and mobility science community, that support this community in the entire research cycle. Research data are the fuel of scientific progress, and especially in mobility and transport research, there would be no progress without them. This makes it all the more important to increase the availability, findability, and accessibility of reusable research data. To this end, the FID move has developed a Research Data Repository based on the open-source software CKAN, which provides a simple and low-barrier opportunity for data publication according to the FAIR Data Principles. Do you have any questions about the Research Data Repository, data publication and curation, or research data management in general? Then please feel free to contact us by phone or at the email address below. We will be happy to help you. Provider of the Repository: Technische Informationsbibliothek (TIB) Welfengarten 1 B 30167 Hannover Germany Contact: Mathias Begoin Tel.: 0511 762-14140 E-Mail: forschungsdaten@fid-move.de Translated from German Original Text: Der Fachinformationsdienst Mobilitäts- und Verkehrsforschung (FID move) wird seit 2018 von der Sächsischen Landesbibliothek – Staats und Universitätsbibliothek Dresden (SLUB) und der Technischen Informationsbibliothek Hannover (TIB) im Rahmen des DFG-Förderprogramms "Fachinformationsdienste für die Wissenschaft" aufgebaut. Ziel des FID move ist es, in enger Abstimmung mit der verkehrs- und mobilitätswissenschaftlichen Fachcommunity, Dienstleistungen und Online-Werkzeuge zu entwickeln und aufzubauen, die diese im gesamten Forschungskreislauf unterstützen. Forschungsdaten sind der Treibstoff des wissenschaftlichen Fortschritts und insbesondere in der Mobilitäts- und Verkehrsforschung würde es ohne sie nicht vorwärts gehen. Umso wichtiger ist es, die Verfügbarkeit, Auffindbarkeit und Zugänglichkeit nachnutzbarer Forschungsdaten zu erhöhen. Hierzu wurde im FID move ein Forschungsdatenrepositorium auf Basis der Open-Source-Software CKAN entwickelt, welches eine einfache und niederschwellige Möglichkeit zur Datenpublikation nach FAIR-Data-Prinzipien ermöglicht. Haben Sie Fragen zum Forschungsdatenrepositorium, zu Datenpublikation und -kuratierung oder zum Forschungsdatenmanagement allgemein? Dann kontaktieren Sie uns gerne telefonisch oder unter der unten angegebenen E-Mail-Adresse. Wir helfen Ihnen gerne weiter. Anbieter des Repositoriums: Technische Informationsbibliothek (TIB) Welfengarten 1 B 30167 Hannover Deutschland Ansprechpartner und Kontakt: Mathias Begoin Tel.: 0511 762-14140 E-Mail: forschungsdaten@fid-move.de

A handy decision tree to help you identify the right kind of repository for your research data, using just three questions to guide you through this process. This guide also shares top-level guidance on metadata, versioning, and software, and suggests resources for further reading on the subject.