Content Curation Software Market Outlook

The global Content Curation Software market size is projected to grow from USD 1.2 billion in 2023 to USD 3.8 billion by 2032, at a compound annual growth rate (CAGR) of 13.7% over the forecast period. This significant growth can be attributed to increasing demand for personalized content, advancements in artificial intelligence (AI) and machine learning, and the growing importance of content marketing strategies among businesses.

The rising demand for personalized content is a major growth factor for the Content Curation Software market. In an era where consumers are bombarded with information from multiple sources, personalized content helps in capturing their attention and retaining their interest. Businesses are increasingly leveraging content curation software to deliver customized content that resonates with their target audience. This trend is particularly pronounced in sectors such as retail and e-commerce, where personalized product recommendations can significantly impact sales and customer loyalty.

Advancements in AI and machine learning technologies are also driving the growth of the Content Curation Software market. These technologies enable more effective data analysis and content recommendations, thereby enhancing the user experience. AI-powered content curation tools can analyze vast amounts of data to identify trends, preferences, and behaviors, which in turn allows for more accurate content suggestions. This capability is particularly valuable for social media management and content discovery applications, where timely and relevant content is crucial.

Another key growth driver is the increasing emphasis on content marketing strategies among businesses. Content marketing has proven to be an effective way to engage customers, build brand awareness, and drive conversions. As a result, companies are investing in content curation software to streamline their content marketing efforts. These tools help in aggregating and sharing high-quality content from various sources, thereby saving time and resources. The integration of content curation software with other marketing tools further amplifies its benefits, making it a critical component of modern marketing strategies.

The evolution of Content Curation Software is deeply intertwined with the broader landscape of digital content. As businesses strive to maintain a competitive edge, the ability to manage and optimize their Content has become paramount. This software not only aids in organizing and presenting information but also plays a crucial role in enhancing user engagement and driving brand loyalty. By leveraging sophisticated algorithms and analytics, content curation tools provide insights that help businesses tailor their strategies to meet the ever-changing demands of their audience. The integration of these tools into existing workflows ensures that content remains relevant, timely, and impactful.

From a regional perspective, North America dominates the Content Curation Software market, owing to the high adoption of advanced technologies and the presence of major market players. Europe is also a significant market, driven by the increasing focus on digital marketing and content personalization. The Asia Pacific region is expected to witness the highest growth during the forecast period, fueled by the rapid digitalization of businesses and the growing popularity of social media platforms. Latin America and the Middle East & Africa are emerging markets, with increasing investments in digital infrastructure and marketing technologies.

Component Analysis

The Content Curation Software market is segmented by components into software and services. The software component dominates the market, driven by the increasing adoption of advanced content curation tools. These software solutions are designed to automate the process of discovering, aggregating, and sharing content, making it easier for businesses to manage their content marketing efforts. Features such as AI-powered content recommendations, analytics, and integration capabilities with other marketing tools make these software solutions highly valuable for businesses of all sizes.

The services segment, although smaller in comparison to the software segment, plays a critical role in the overall market. Services include consulting, implementation, training, and support, which are essential for the successful deployment and utilization of content curati

Additional file 1. Supplementary Table 1. Comparison of different omics data tools.

Tools for research data curation.

Data Prep Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 16.12 Billion by 2031, growing at a CAGR of 19% from 2024 to 2031.

Global Data Prep Market Drivers

Increasing Demand for Data Analytics: Businesses across all industries are increasingly relying on data-driven decision-making, necessitating the need for clean, reliable, and useful information. This rising reliance on data increases the demand for better data preparation technologies, which are required to transform raw data into meaningful insights. Growing Volume and Complexity of Data: The increase in data generation continues unabated, with information streaming in from a variety of sources. This data frequently lacks consistency or organization, therefore effective data preparation is critical for accurate analysis. To assure quality and coherence while dealing with such a large and complicated data landscape, powerful technologies are required. Increased Use of Self-Service Data Preparation Tools: User-friendly, self-service data preparation solutions are gaining popularity because they enable non-technical users to access, clean, and prepare data. independently. This democratizes data access, decreases reliance on IT departments, and speeds up the data analysis process, making data-driven insights more available to all business units. Integration of AI and ML: Advanced data preparation technologies are progressively using AI and machine learning capabilities to improve their effectiveness. These technologies automate repetitive activities, detect data quality issues, and recommend data transformations, increasing productivity and accuracy. The use of AI and ML streamlines the data preparation process, making it faster and more reliable. Regulatory Compliance Requirements: Many businesses are subject to tight regulations governing data security and privacy. Data preparation technologies play an important role in ensuring that data meets these compliance requirements. By giving functions that help manage and protect sensitive information these technologies help firms negotiate complex regulatory climates. Cloud-based Data Management: The transition to cloud-based data storage and analytics platforms needs data preparation solutions that can work smoothly with cloud-based data sources. These solutions must be able to integrate with a variety of cloud settings to assist effective data administration and preparation while also supporting modern data infrastructure.

Quantitative structure–activity relationship (QSAR) modeling is a well-known in silico technique with extensive applications in several major fields such as drug design, predictive toxicology, materials science, food science, etc. Handling small-sized datasets due to the lack of experimental data for specialized end points is a crucial task for the QSAR researcher. In the present study, we propose an integrated workflow/scheme capable of dealing with small dataset modeling that integrates dataset curation, “exhaustive” double cross-validation and a set of optimal model selection techniques including consensus predictions. We have developed two software tools, namely, Small Dataset Curator, version 1.0.0, and Small Dataset Modeler, version 1.0.0, to effortlessly execute the proposed workflow. These tools are freely available for download from https://dtclab.webs.com/software-tools. We have performed case studies employing seven diverse datasets to demonstrate the performance of the proposed scheme (including data curation) for small dataset QSAR modeling. The case studies also confirm the usability and stability of the developed software tools.

Supporting scalable curation is a part of the mission of the Elixir Data Platform.Thus far, we have established infrastructure capable of ingesting and aggregating text-mined outputs from multiple providers and making these available via an API. This public API is used by Europe PMC to display specific entities and relationships on full text articles (via the SciLite application). To ensure that the future development of this infrastructure meets the needs of curators, we first carried out user research to understand and identify common workflow patterns and practices via an observational study. Building on these outcomes, we then devised a curator community survey to more specifically understand which entity types, sections of a paper and tools are of top priority to address. The results of the project is presented here.

Historical biodiversity documents comprise an important link to the long-term data life cycle and provide useful insights on several aspects of biodiversity research and management. However, because of their historical context, they present specific challenges, primarily time- and effort-consuming in data curation. The data rescue process requires a multidisciplinary effort involving four tasks: (a) Document digitisation (b) Transcription, which involves text recognition and correction, and (c) Information Extraction, which is performed using text mining tools and involves the entity identification, their normalisation and their co-mentions in text. Finally, the extracted data go through (d) Publication to a data repository in a standardised format. Each of these tasks requires a dedicated multistep methodology with standards and procedures. During the past 8 years, Information Extraction (IE) tools have undergone remarkable advances, which created a landscape of various tools with distinct capabilities specific to biodiversity data. These tools recognise entities in text such as taxon names, localities, phenotypic traits and thus automate, accelerate and facilitate the curation process. Furthermore, they assist the normalisation and mapping of entities to specific identifiers. This work focuses on the IE step (c) from the marine historical biodiversity data perspective. It orchestrates IE tools and provides the curators with a unified view of the methodology; as a result the documentation of the strengths, limitations and dependencies of several tools was drafted. Additionally, the classification of tools into Graphical User Interface (web and standalone) applications and Command Line Interface ones enables the data curators to select the most suitable tool for their needs, according to their specific features. In addition, the high volume of already digitised marine documents that await curation is amassed and a demonstration of the methodology, with a new scalable, extendable and containerised tool, “DECO” (bioDivErsity data Curation programming wOrkflow) is presented. DECO’s usage will provide a solid basis for future curation initiatives and an augmented degree of reliability towards high value data products that allow for the connection between the past and the present, in marine biodiversity research.

The study uses the data curation profiling (DCP) method to capture all actions across the data lifecycle. Participants responded to these open-ended questions that relate to aspects of the data lifecycle–the accession, organization, storage, and use of their collections. The purpose of this study is to understand the curation perceptions and behaviors of physical collection managers across domains to inform cross-disciplinary research data management. Ten focus groups were conducted with thirty-two participants across several physical collection communities. Participants responded to open-ended questions that relate to the entire data lifecycle for their physical objects. Results indicated that physical collections attempt to use universal metadata and data storage standards to increase discoverability, but interdisciplinary physical collections and derived data reuse require more investments to increase reusability of these invaluable items. This study concludes with a domain-agnostic reuse facets matrix to inform investment in cyberinfrastructure tools and services.

English:

The DiBiLit corpus was created by homogenising various derivatives of texts from the »Digital Library« – originally published by DirectMedia Publishing – and extensively enriching them with (bibliographic) metadata within the collaborative project CLARIAH-DE at the BBAW. The more than 2,000 texts come from renowned authors, are DTABf-encoded and were made accessible under a CC-BY-SA 4.0 license within the DTA infrastructure. Thus, the text collection can be researched using the DDC search engine integrated in the DTA as well as other DTA tools for linguistic analysis. Further tools/services for text analysis are available, for instance via the link to the Language Resource Switchboard (LRS).

German:

Das DiBiLit-Korpus entstand durch die Homogenisierung verschiedener Derivate von Texten aus der »Digitalen Bibliothek« – ursprünglich von DirectMedia Publishing veröffentlicht – sowie die umfängliche Anreicherung durch (bibliographische) Metadaten im Rahmen des Verbundprojekts CLARIAH-DE an der BBAW . Die mehr als 2.000 Texte stammen von namhaften Autorinnen und Autoren, sie sind DTABf-kodiert und wurden innerhalb der DTA-Infrastruktur unter einer CC BY-SA 4.0 Lizenz zugänglich gemacht. So kann die Textsammlung mittels der im DTA integrierten DDC-Suchmaschine sowie weiterer DTA-Werkzeuge zur linguistischen Analyse beforscht werden. Weitere Werkzeuge/Dienste zur Textanalyse stehen über die Verknüpfung bspw. mit dem Language Ressource Switchboard (LRS) zur Verfügung.

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is the de facto standard for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are limited on Flywheel. To address these challenges, we developed “FlywheelTools,” a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

The global data preparation market is estimated to reach $1978 million in 2033, growing at a CAGR of 13.7% from 2025 to 2033. The increasing volume and complexity of data, along with the need for data-driven decision-making, are driving the growth of the market. Organizations are looking for ways to make their data more usable and accessible, and data preparation tools can help them do just that. Key trends in the market include the rise of self-service data preparation tools, the adoption of cloud-based data preparation platforms, and the increasing use of artificial intelligence (AI) and machine learning (ML) in data preparation. Data Curation, Data Cataloging, and Data Quality are the major types of data preparation tools, and Hosted and On-premises are the two main deployment modes. North America is the largest region in the market, followed by Europe and Asia Pacific. The market is highly competitive, with a number of vendors offering data preparation tools. Key vendors in the market include Alteryx, Inc, Informatica, IBM, Tibco Software Inc., Microsoft, SAS Institute, Datawatch Corporation, Tableau Software, Qlik Technologies Inc., SAP SE., Talend, Microstrategy Incorporated, among others.

A variety of teams and individuals are involved in biocuration worldwide. However, they may not self-identify as biocurators per se, as they may be unaware of biocuration as a career path or because biocuration is only part of their role. The lack of a clear, up-to-date profile of biocuration creates challenges for organisations like ELIXIR, the ISB and GOBLET to systematically support biocurators and for biocurators themselves to develop their own careers. Therefore, the ELIXIR Training Platform launched an Implementation Study in order to i) identify communities of biocurators, ii) map the type of curation work being done, iii) assess biocuration training, and iv) draw a picture of biocuration career development. To achieve the goals of the study we carried out a global survey about the nature of biocuration work, the tools and resources that are used, training that has been received and additional training needs. To examine these topics in more detail we ran workshop-based discussions at ISB Biocuration Conference 2019 and the ELIXIR All Hands Meeting 2019. We also had guided conversations with selected people from the EMBL-European Bioinformatics Institute.

The following files represent the underlying data for this study:

Pilot survey questions.docx (questionnaire sent to staff of Wellcome Genome Campus)

Questions to guide conversations with biocurators.docx (conversation guide outlines the type of questions to be asked)

Global survey questions.docx (globally disseminated questionnaire revised on the basis of the pilot survey)

Themed summary of the responses given in the guided conversations – deidentified.docx (de-identified outcomes of the guided conversations)

Information that may lead to the identification of respondents has been redacted.

Global_survey_deidentified.xlsx (de-identified responses to the global survey)

This file includes de-identified responses to the survey questions. Responses that may lead to the identification of respondents have been redacted. Free text responses to questions 6, 14 and 15 have been categorised into tasks, topics and skills, respectively.

Bar graphs of global survey.xlsx (quantitative responses to multiple choice questions in the global survey. For some questions, respondents could choose more than one option)

Tools and resources.xlsx (Tools and resources used for biocuration work and listed by the respondents of the global survey)

Biocuration training course list.xlsx (formal training courses listed by respondents of the global survey)

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.

The global data preparation market is anticipated to escalate by 14.3% CAGR from 2023 to 2033, amassing a value of USD 2210.8 million by 2033. With enterprises generating massive volumes of data, data preparation has become crucial for effective data analysis and decision-making. Driving this market growth are the increasing adoption of cloud-based data storage and processing platforms, the need for data privacy and governance, and the growing use of artificial intelligence (AI) and machine learning (ML) in data analysis. Market segmentation includes different applications such as hosted and on-premises, and types such as data curation, cataloging, quality, ingestion, and governance. Key market players include Alteryx, Inc., Informatica, IBM, Tibco Software Inc., Microsoft, and SAS Institute. Regionally, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. Factors restraining market growth include data privacy concerns and the lack of skilled professionals in data preparation. However, technological advancements, such as the integration of AI and ML in data preparation tools, are expected to create growth opportunities in the future.

Result of classification of the RABV datasets samples returned by RABV-GLUE.

The Facility Registry Service (FRS) provides quality facility data to support EPA's mission of protecting human health and the environment by identifying and geospatially locating facilities, sites, or places subject to environmental regulations of environmental interest. Facility data is improved with geospatial processing of incoming data and data curation tools to provide an integrated, dataset to partners and the public through a variety of methods and products. For more detailed information about these facilities, use the FRS Query tool. US Power Generation Facilities, compiled from most-current (as of June 2014) Energy Information Administration EIA-860 powerplant data, together with EPA FRS data.

The Facility Registry Service (FRS) provides quality facility data to support EPA's mission of protecting human health and the environment by identifying and geospatially locating facilities, sites, or places subject to environmental regulations of environmental interest. Facility data is improved with geospatial processing of incoming data and data curation tools to provide an integrated, dataset to partners and the public through a variety of methods and products. For more detailed information about these facilities, use the FRS Query tool. US Power Generation Facilities, compiled from most-current (as of June 2014) Energy Information Administration EIA-860 powerplant data, together with EPA FRS data.

The Facility Registry Service (FRS) provides quality facility data to support EPA's mission of protecting human health and the environment by identifying and geospatially locating facilities, sites, or places subject to environmental regulations of environmental interest. Facility data is improved with geospatial processing of incoming data and data curation tools to provide an integrated, dataset to partners and the public through a variety of methods and products. For more detailed information about these facilities, use the FRS Query tool. US Power Generation Facilities, compiled from most-current (as of June 2014) Energy Information Administration EIA-860 powerplant data, together with EPA FRS data.

Curation and ISA representation of a SARS-Cov2/Covid-19 Proteomics Dataset deposited in PRIDE database with accession number: PXD107710

ISA-Tab annotation for the "SARS-CoV-2 infected host cell proteomics reveal potential therapy targets" publication.

Github repository: https://github.com/ISA-tools/PXD017710

This is part of an effort to (re-)annotate: https://dx.doi.org/10.21203/rs.3.rs-17218/v1

Additional work done as part of:

https://github.com/virtual-biohackathons/covid-19-bh20

https://github.com/virtual-biohackathons/covid-19-bh20/wiki/FairData

Proteomics data

Available from PRIDE at https://www.ebi.ac.uk/pride/archive/projects/PXD017710 and [MassIVE/CCMS Maestro+MSstats reanalysis of MSV000085096 / PXD017710]

ISA-Tab representation:

Rationale: Demonstrate suitability of the ISA format for representing MS based protein profiling experiment with more granularity and details, thus providing a better representation of the experiment design. The formatting and re-annotation are based on information extracted from: - the original publication - the supplementary tables available from the publishers site - the 'filtered-results.csv' helper file as supplied to @sneumann during the HUPO-PSI meeting March 2020

Viewing the ISA-tab formatted and re-annotated PXD017710 with ISATab-Viewer

Viewing the ISA-tab formatted and re-annotated PXD017710 locally, do the following:

python -m http.server 8000

Then point your browser to http://0.0.0.0:8000/isaviewer-demo.html

Curation tasks performed:

• initial structure of the study design in ISA format:

• linkage of Proteome and Translatome data (supplementary material) to ISA assay tables (via Derived Data File)

• processing the Proteome and Translatome data (supplementary material) with python pandas library to generate the following csv files:

  • proteome_intensities_long_table_ggplot2.txt
  • proteome_diffanal_ratio_pvalue_long_table_ggplot2.txt
  • translatome_intensities_long_table_ggplot2.txt
  • translatome_diffanal_ratio_pvalue_long_table_ggplot2

  The files are long table corresponding to a melt on the Excel file originally generated by the users and can be readily loaded in R ggplot2 library for graphical representation. The statistical relevant elements have been annotated with the STATO ontology and the tables comply with a Frictionless.io Data Package. The jupyter notebook for the transformation is available.

• conversion of raw data to mzML format: detailed in https://github.com/ISA-tools/PXD017710

install docker: bash >brew update >brew install docker

sign in to docker bash >docker start >docker login

pull docker container for ProteoWizard: ```bash

docker pull chambm/pwiz-i-agree-to-the-vendor-licenses ```

:warning: be sure to sign-up and login to https://hub.docker.com/

in order to be able to reach

https://hub.docker.com/r/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses

run the pwiz tool from the container over the raw data: bash docker run -it --rm -e WINEDEBUG=-all -v /Users/Downloads/PXD017710/raw/:/data chambm/pwiz-skyline-i-agree-to-the-vendor-licenses wine msconvert /data/*.raw --mzML

• ontology markup for:
  • declaration of independent variables as ISA Study Factors:{biological agent, dose, time point, replicate} ->OBI
  • Taxonomic information (host cells and virus) -> NCBITaxonomy
  • Cell line: CaCo-2 cells -> Cell Line Ontology
  • Disease: Colon Cancer -> Human Phenotype Ontology
  • MS specific aspect (TMT reagent, instrument ... ) -> PSI-MS
  • Statistical Tests -> STATO

Unresolved curatorial issues:

1. ambiguities related to Tandem Mass Tag labelling protocol

   • the publication mentions TMT11 (see Figure 2 in https://www.researchsquare.com/article/rs-17218/v1)
   • the information available from PRIDE mentions TMT6 (https://www.ebi.ac.uk/pride/archive/projects/PXD017710) This may require another round of annotation on the TMT agents and fractions in the ISA a_assay representation

2. SARS-Cov2 isolate: no clear NCBI Taxonomic anchoring and unclear origin: -> the markup is made to the parent class (as of 06.04.2020)

Release and packaging as a BDBAG:

The tgz file associated with this upload has been producing using https://github.com/fair-research/bdbag. It contains several manifest files detailing metadata and data files, providing md5 and sha256 checksums.

Github repository: https://github.com/ISA-tools/PXD017710

A web-based central resource that integrates vaccine literature data mining, vaccine research data curation and storage, and curated vaccine data analysis for vaccines and vaccine candidates developed against various pathogens of high priority in public health and biological safety. The vaccine data includes research data from vaccine studies using humans, natural and laboratory animals.VIOLIN extracts and stores vaccine-related, peer-reviewed papers from PubMed. Several powerful literature searching and data mining programs have been developed. These include an advanced keywords search program, a natural languagae processing (NLP) based literature retrieval program, a MeSH-based literature browser, and a literature alert program. Registered users can subscribe to our email alert service and will be notified of any newly published vaccine papers in the areas of interest. These literature mining programs are designed to help the user and VIOLIN database curators to find efficiently needed vaccine articles and sentences within full-text articles that contain searched keywords or categories.A web-based literature mining and curation system (Limix) is available for registered users/curators to search, curate, and submit structured vaccine data into the VIOLIN database. The curated vaccine-related information contains many categories such as general pathogenesis, protective immunity, vaccine preparation and characteristics, host responses including vaccination protocol and efficacy against virulent pathogen infections. All data within the database is edited manually and is derived primarily from peer-reviewed publications. The curated data is stored in a relational database and can be queried using various VIOLIN search programs. Vaccine-related pathogen and host genes are annotated and available for searchs based on a customized BLAST program. All VIOLIN data are available for download into an XML-based data exchange format.VIOLIN is designed to be a vital source of vaccine information and will provide researchers in basic and clinical sciences with curated data and bioinformatics tools to facilitate understanding and development of vaccines to fight infectious diseases. Category: Other Molecular Biology Databases Subcategory: Drugs and drug design