The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in machine learning and artificial intelligence applications. The market's expansion is fueled by several factors: the rising adoption of AI across various sectors (including IT, automotive, healthcare, and finance), the need for cost-effective data annotation solutions, and the inherent flexibility and customization offered by open-source tools. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant, particularly for organizations with stringent data security requirements. The market's growth is further propelled by advancements in automation and semi-supervised learning techniques within data labeling, leading to increased efficiency and reduced annotation costs. Geographic distribution shows a strong concentration in North America and Europe, reflecting the higher adoption of AI technologies in these regions; however, Asia-Pacific is emerging as a rapidly growing market due to increasing investment in AI and the availability of a large workforce for data annotation. Despite the promising outlook, certain challenges restrain market growth. The complexity of implementing and maintaining open-source tools, along with the need for specialized technical expertise, can pose barriers to entry for smaller organizations. Furthermore, the quality control and data governance aspects of open-source annotation require careful consideration. The potential for data bias and the need for robust validation processes necessitate a strategic approach to ensure data accuracy and reliability. Competition is intensifying with both established and emerging players vying for market share, forcing companies to focus on differentiation through innovation and specialized functionalities within their tools. The market is anticipated to maintain a healthy growth trajectory in the coming years, with increasing adoption across diverse sectors and geographical regions. The continued advancements in automation and the growing emphasis on data quality will be key drivers of future market expansion.

The AI Data Labeling Solutions market is experiencing robust growth, driven by the increasing demand for high-quality data to train and improve the accuracy of AI and machine learning models. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This substantial growth is fueled by several key factors. The proliferation of AI applications across diverse sectors like healthcare, automotive, and finance necessitates extensive data labeling. The rise of sophisticated AI algorithms that require larger and more complex datasets is another major driver. Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and ease of access, contributing significantly to market expansion. However, challenges remain, including data privacy concerns, the need for skilled data labelers, and the potential for bias in labeled data. These restraints need to be addressed to ensure the sustainable and responsible growth of the market. The segmentation of the market reveals a diverse landscape. Cloud-based solutions currently dominate, reflecting the industry shift toward flexible and scalable data processing. Application-wise, the IT sector is currently the largest consumer, followed by automotive and healthcare. However, growth in financial services and other sectors indicates the broadening application of AI data labeling solutions. Key players in the market are constantly innovating to improve accuracy, efficiency, and cost-effectiveness, leading to a competitive and rapidly evolving market. The regional distribution shows strong market presence in North America and Europe, driven by early adoption of AI technologies and a well-established technological infrastructure. Asia-Pacific is also demonstrating significant growth potential due to increasing technological advancements and investments in AI research and development. The forecast period of 2025-2033 presents substantial opportunities for market expansion, contingent upon addressing the challenges and leveraging emerging technologies.

Global Data Labeling Software market size 2025 was XX Million. Data Labeling Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

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Enterprise Labeling Software Market Size 2024-2028

The enterprise labeling software market size is forecast to increase by USD 133.9 mn at a CAGR of 6.59% between 2023 and 2028.

The market is witnessing significant growth due to several key trends. The adoption of enterprise labeling solutions is increasing as businesses seek to streamline their labeling processes and improve efficiency. Dynamic labeling, which allows for real-time updates to labels, is gaining popularity as it enables companies to quickly respond to changing regulations or product information. The market is experiencing growth as companies leverage data integration and analytics to streamline labeling processes, ensuring greater accuracy, compliance, and operational efficiency. Moreover, stringent government regulations mandating accurate and compliant labeling are driving the need for enterprise labeling software. These factors are expected to fuel market growth In the coming years. The market landscape is constantly evolving, and staying abreast of these trends is essential for businesses looking to remain competitive.

What will be the Size of the Enterprise Labeling Software Market During the Forecast Period?

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The market encompasses solutions designed for creating, managing, and printing labels in various industries. Compliance with regulations and ensuring labeling accuracy are key drivers for this market. Real-time updates and customizable templates enable businesses to maintain consistency and adapt to changing requirements. Integration capabilities with enterprise systems, data management planning, and the printing process are essential for streamlining workflows and improving efficiency. Innovative technology, such as automation and machine learning, enhances labeling quality and speed, providing a competitive edge.
A user-friendly interface and economic conditions influence market demand. Urbanization and the growing need for packaging solutions, branding, and on-premises-based software further expand the market's reach. Overall, the market continues to grow, offering significant benefits to businesses seeking to optimize their labeling processes.

How is this Enterprise Labeling Software Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premise
  Cloud


End-user

  FMCG
  Retail and e-commerce
  Healthcare
  Warehousing and logistics
  Others


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    Germany


  Middle East and Africa



  South America

By Deployment Insights

The on-premise segment is estimated to witness significant growth during the forecast period.

The market is driven by the need for compliance, creation, management, printing, and real-time updates of labels in various industries. Large enterprises require unique labeling solutions to meet diverse industry standards and traceability regulations, ensuring product quality and customer satisfaction. On-premise and cloud-based enterprise labeling software offer agility, scalability, and flexibility, optimizing operations and enhancing resilience and adaptability. Compliance management, seamless collaboration, contactless processes, safety measures, and predictive analytics are key features. Driving factors include digitalization, automation, and evolving challenges in logistics and e-commerce. However, varying industry standards, implementation costs, legacy systems, and integration challenges pose restraining factors. Enterprise labeling software solutions offer customizable templates, integration capabilities, and language support, catering to the economic condition, urbanization, and packaging solutions.

Brands prioritize a data-driven approach and regulatory requirements In their labeling strategy. The market is expected to grow, with key players catering to enterprise sizes and time to market.

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The On-premise segment was valued at USD 163.80 mn in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 41% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in APAC is projected to experience significant growth due to the increasing number of end-users in sectors such as food and beverage, personal care products, and pharmaceuticals.

The size and share of the market is categorized based on Application (Data classification tools, Data tagging software, Information categorization tools, Data labeling tools, Data organization solutions) and Product (Data management, Security enhancement, Compliance management, Information governance, Risk management) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The U.S. Data Collection And Labeling Market size was valued at USD 855.0 million in 2023 and is projected to reach USD 3964.16 million by 2032, exhibiting a CAGR of 24.5 % during the forecasts period. The US Data Collection and Labeling Market implies the process of gathering and labeling data for the creation of machine learning, artificial intelligence, as well as other data-related applications. The market helps various sectors including retail health care, automotive, and finance through supplying labeled data which is critical in training and improving models used in AI and overall decision-making. Some of the primary applications are related to image and speech recognition, self-driving cars and many others related to Predictive analysis. New directions promote the development of a greater degree of automatization of processes, the use of highly specialized annotation tools, and the need for further development of specialized data labeling services. The market is also experiencing incorporation of artificial intelligence for the automation of several data labeling tasks. Recent developments include: In July 2022, IBM announced the acquisition of Databand.ai to augment its software portfolio across AI, data and automation. For the record, Databand.ai was IBM's fifth acquisition in 2022, signifying the latter’s commitment to hybrid cloud and AI skills and capabilities. , In June 2022, Oracle completed the acquisition of Cerner as the Austin-based company gears up to ramp up its cloud business in the hospital and health system landscape. .

--- OpenStack Tag DataIn order to support future replication studies, we have used publicly available datasets and have made our manually tagged review data available here.[File] spsn_abandon172.csv:The tags for abandoned contentious patches. The total number of tags is 172. spsn_integrate189.csv:The tags for integrated contentious patches. The total number of tags is 189.non-contention_abandon342_emse:The tags for abandoned non-contentious patches. The total number of tags is 342. non-contention_integrate381_emse:The tags for integrated non-contentious patches. The total number of tags is 381.[Column in a file] "Review Id" is an identification number of a patch. For example, in the paper, review #33395 is Review Id 33395. "Tag" is a key concern associated with abandoned contentious patches or integrated contentious patches. For example, in abandoned contentious patches, the key concern of Review Id 33395 is "Shallow Fix".--- Definitions of Contentious PatternsHere is the definitions that explain a each tag of contentious patches.RQ3Unnecessary Fix Unclear Intention: The rationale for the patch was not clear to reviewers. Already Fixed: The patch addresses an issue that has already been addressed by another (set of) patch(es). Not an Issue: The motivation for the patch is not compelling.Integration Planning Patch Dependency: A patch that depends on other patches, which have not been approved for integration Blueprint: Collections of related OpenStack issues are grouped using Blueprints. Blueprints are used for long-term release planning of OpenStack work. Release Schedule: A patch whose integration depends on the release schedule of internal projects.Integration Policy Compliance Squashing Commits: Every author needs to clean their commit log history to avoid integration problems. Branch Placement: The submitted patch should be addressed on an appropriate branch.Lack of Interest Lost by an author: The author may lose interest. Lost by a reviewer: The reviewer(s) may lose interest.Design Alternative Solution: Another solution to resolve the issue under discussion is proposed. Flawed Changes: A patch that suffers from a design-level problem. Shallow Fix: A patch that does not completely address the underlying issue. Side Effect: A patch that has unintended (negative) consequences.Implementation Patch Size: The size of a submitted patch may be not appropriate. Backward Compatibility: A submitted patch may break backward compatibility.Testing Test Coverage: A submitted patch with a lack of test coverage. Test Failure: A submitted patch that test failure may uncover issues.RQ4Legal Problems Legal Issues: Legal issues can also block a patch from being integrated.Withdrawal of Negative Score Self-change: The reviewer who opposes integration can withdraw their score without discussion. Persuasion: The opposing reviewer can be persuaded by the the author or another reviewer to approve integration.

What is in this release?

In this release you will find data about software distributed and/or crafted publicly on the Internet. You will find information about its development, its distribution and its relationship with other software included as a dependency. You will not find any information about the individuals who create and maintain these projects.

Further information and documentation on this data set can be found at https://libraries.io/data

For enquiries please contact data@libraries.io

This dataset contains seven csv files:

Projects

A project is a piece of software available on any one of the 34 package managers supported by Libraries.io.

Versions

A Libraries.io version is an immutable published version of a Project from a package manager. Not all package managers have a concept of publishing versions, often relying directly on tags/branches from a revision control tool.

Tags

A tag is equivalent to a tag in a revision control system. Tags are sometimes used instead of Versions where a package manager does not use the concept of versions. Tags are often semantic version numbers.

Dependencies

Dependencies describe the relationship between a project and the software it builds upon. Dependencies belong to Version. Each Version can have different sets of dependencies. Dependencies point at a specific Version or range of versions of other projects.

Repositories

A Libraries.io repository represents a publically accessible source code repository from either github.com, gitlab.com or bitbucket.org. Repositories are distinct from Projects, they are not distributed via a package manager and typically an application for end users rather than component to build upon.

Repository dependencies

A repository dependency is a dependency upon a Version from a package manager has been specified in a manifest file, either as a manually added dependency committed by a user or listed as a generated dependency listed in a lockfile that has been automatically generated by a package manager and committed.

Projects with related Repository fields

This is an alternative projects export that denormalizes a projects related source code repository inline to reduce the need to join between two data sets.

Licence

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International Licence.

This licence provides the user with the freedom to use, adapt and redistribute this data. In return the user must publish any derivative work under a similarly open licence, attributing Libraries.io as a data source. The full text of the licence is included in the data.

Access, Attribution and Citation

The dataset is available to download from Zenodo at https://zenodo.org/record/1068916.

Please attribute Libraries.io as a data source by including the words ‘Includes data from Libraries.io’ and reference the Digital Object identifier: 10.5281/Zenodo.1068916.

Tag management solutions offer a comprehensive suite of features, including tag deployment, data collection, data analysis, and reporting. These tools cater to various business needs, such as campaign management, user experience optimization, content management, and risk and compliance adherence. Potential restraints include: Lack of reliable network infrastructure need for high-speed connectivity, Security & Privacy Concerns.

Global Data Center RFID Market size valued at US$ 1.33 Billion in 2023, set to reach US$ 7.48 Billion by 2032 at a CAGR of about 21.13% from 2024 to 2032.

This data is a subset of a larger human curated, machine assisted gold standard data set of RRID citations within the text of the scientific literature. The full set is accessible via Hypothes.is at https://hypothes.is/users/scibot?q=group%3A_world_ and via individual RRID records such as RRID:SCR_016250 https://scicrunch.org/resolver/SCR_016250/mentions?q=&i=rrid:scr_016250

The data is based on authors who added RRIDs into their manuscripts. The data was then extracted by SciBot (RRID:SCR_016250), into Hypothes.is (RRID:SCR_000430) and then manually checked by a curator to determine if the author and the database agreed. The list of annotators is available in Hypothes.is user group: SciBotCurationGroup.

There are 78,140 rows and each row contains an annotation (annotation id, URI), linked to a paper (paper identifiers: PMID, DOI, PMC) and linked to the RRID (scr_id, exact, text_quote_selector).

A second spreadsheet contains a list of 8,322 software tools from the SciCrunch Registry (available here https://scicrunch.org/resources/data/source/nlx_144509-1/search ), enhanced by additions by thousands of individual authors, and curated over 10 years (Ozyurt et al., 2016).

The third spreadsheet contains a data dictionary and links to related ontologies, and tagging sets.

A JSON that is used to build the content on code.nasa.gov. This JSON contains names, descriptions, links, and keyword tags for all NASA open-sourced code projects released through the SRA (Software Release Authority) and available on code.nasa.gov.

It was updated on August, 2019.

46 school shark were tagged with archival tags during 1997-98, in South Australian and Tasmanian waters. 19 tags were recovered. The tags yielded 15.3 years of data on light level, depth and temperature collected at 4 minute intervals. The basic release-recapture data has been entered into the CSIRO pelagic tag data base but not the actual electronic data. The electronic data for the Lotek tags is in a different format to that of the Wildlife Computer tags, and may require dedicated geolocation software to process. Wildlife Computers provides geolocation software for their tags free of charge. While longitudinal movements have been described, there was no analysis of corresponding latitudes, as light-based latitude estimation was unreliable. There is scope for additional research into latitudinal movements based on the depth data. The depth pattern shown by the sharks can be used to examine if the fish was close to the bottom, and combined with a longitude estimate for a particular day, latitude can be estimated as across much of southern Australia where depth increases with latitude. However, there is a software development challenge associated with this, as there may be more than one depth fit for a particular longitude, especially towards eastern Australia. In this eastern region the restricted depth of Bass Strait can provide additional information on the latitude, as fish data at >86m indicates that it was too deep for Bass Strait. An additional factor that was not examined was the temperature data from the tags. In pelagic species surface water temperature is used to estimate latitude and at times school shark do come close to the surface. Some of the tags were set up to record internal as well as external temperatures but this data was not examined. There have been 2 recaptures of Wildlife Computers tags since West & Stevens (1996) published the results. There have also been two Lotek tags returned since this publication but the data for these tags was corrupted.

The global structured product labeling (SPL) software market is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The growth of the market is attributed to factors such as the increasing demand for efficient and accurate product labeling, growing regulatory requirements for product safety, and the adoption of cloud-based SPL solutions. The market is segmented based on application, type, and region. Among applications, the biopharmaceutical segment is expected to hold a significant market share due to the stringent regulatory requirements for pharmaceutical products. In terms of type, the cloud hosting deployment segment is anticipated to witness substantial growth due to its cost-effectiveness, flexibility, and scalability. Geographically, North America is projected to dominate the market, followed by Europe and Asia Pacific. The presence of a large number of biopharmaceutical companies and CROs in these regions is a key driver for market growth.

SoftwareKG is a knowledge graph that contains software mentions of 51,165 articles from PLoS that are tagged with the keyword ""Social Science"". The software mentions are extracted by use of an automated pipeline. more than 133,000 software mention were identified. The software mentions were then linked by use of their potential abbreviations and the DBpedia. The identified software mentions are then structured in the SoftwareKG together with meta data about the articles. The data is represented in an RDF/S model by using established W3C standards and vocabularies. More information about SoftwareKG is provided at https://data.gesis.org/softwarekg/site/. This dataset contains:

N-Triples file for the final SoftwareKG: software_kg.zip Reference to the source code necessary to reproduce the results softwareKG SoSciSoCi corpus used for training and evaluation of the NER model SoSciSoCi SoSciSoCi-SSC silver standard corpus used for pre-training of the NER model SoSciSoCi-SSC

The work is described and used in the following publication: David Schindler and Benjamin Zapilko and Frank Krüger: Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach, In Proceedings of the 17th Extended Semantic Web Conference, Heraklion, Crete, Greece, May 31 - June 4 2020 Please cite this publication, when using the corpus. The Code and all data is also available on github at: https://github.com/f-krueger/ESWC-SoftwareKG/releases/tag/v1.0

This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020. 1. data folder The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL. 2. scripts floder The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms. smell-term/general_smell_terms.txt contains general code smell terms, such as "code smell". smell-term/specific_smell_terms.txt contains specific code smell terms, such as "dead code". smell-term/misspelling_terms_of_smell.txt contains the misspelling terms of 'smell', such as "ssell". get_changes.py is used for getting code changes from OpenStack. get_comments.py is used for getting review comments for each code change. smell_search.py is used for searching review comments that contain code smell terms. 3. project folder The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download. Data Labeling & Encoding for RQ2.mx12 is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool. Data Labeling & Encoding for RQ3.mx12 is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.

Data Center RFID Market size was valued at USD 4861.14 Billion in 2023 and is projected to reach USD 44033.41 Billion by 2031, growing at a CAGR of 37.0% from 2024 to 2031.

Key Market Drivers:

Rising Demand for Real-Time Asset Management: As data processing and storage demand increases significantly data centrescenters become more complicated. This level of complexity necessitates the use of effective and reliable asset management systems. Data centrecenter RFID technology enables real-time asset tracking and visibility, maximisingmaximizing utilisationutilization and improving overall operational efficiency.

Improved Inventory Management and Reduced Downtime: Manually managing a large inventory of data centrecenter assets can be time-consuming and error-prone. RFID improves inventory procedures by automating asset tracking and lowering the possibility of human error. Improved visibility into asset location saves time searching for equipment, reduces downtime, and ensures smooth operations.

Proactive Maintenance and Extended Asset Lifespan: RFID tags can record information about specific assets, such as maintenance history and servicing requirements, allowing for proactive maintenance and an extended asset life cycle. This enables real-time data-driven preventative maintenance scheduling, which improves equipment performance and extends its lifespan. Predictive maintenance decreases the likelihood of unexpected equipment breakdowns, which can result in costly downtime.

RFID Technology Advancements: As RFID technology evolves, it becomes more appealing for data centrecenter applications. RFID systems are more effective thanks to improvements in tag size, durability, and read range. Furthermore, advances in RFID software and reader technology help to accelerate data processing and increase integration with current infrastructure.

The set contains data about the software used to maintain the accounting system, software version, links to a web resource that gives access to the accounting system data

Data-Supporting-Figure4-and-FigureS14Number of Illumina HiSeq 2000 reads returned for TruSeq indexes versus indexes designed using EDITTAG and incorporated to TruSeq-style sequencing adapters.Data-Supporting-FigureS11Number of 454 FLX Titanium reads returned for PCR primers incorporating 10 nt edit distance sequence tags and amplifying RbcLa in land plants.Data-Supporting-FigureS12Number of Illumina GAIIx reads returned for Epicentre Nextera indexes versus indexes designed using EDITTAG and incorporated to Epicentre Nextera-style sequencing adapters.Data-Supporting-FigureS13Number of Illumina GAIIx reads returned for TruSeq-style adapters incorporating indexes designed using EDITTAG and ligated onto pooled DNA templates (Tab 1) versus the number of Illumina HiSeq 2000 reads returned for TruSeq-style adapters incorporating indexes designed using EDITTAG and ligated onto DNA individually. The variance in read number is the important difference to note.