Data labelling refers to data that has been tagged or marked up to identify the goal or the outcome that your model must predict. Data Tagging, Data Annotation, Data Classification, Data Moderation, Data Transcribing, and Data Processing are all included in the Data Labelling technique.

The process of labelling data frequently coincides with data annotation. Data labelling and data annotation are commonly used synchronously.

The data labelling process displays the attributes of the data, such as its traits, characteristics, or classifications, which can be studied for trends to raise the predictive power of the model.

A data labeller, for instance, can take frame-by-frame samples from the video and use data labelling tools to show the location of street signs, people, or other vehicles in automotive image processing for self-driving automobiles.

To meet this expanding need for data labelling services, a large number of data labelling businesses have sprung up across the globe.

Outsourcing data labeling can be beneficial for researchers in several ways:

1. Cost-effective: Outsourcing data labelling can be cost-effective, especially for small or medium-sized research projects. It can be expensive to hire and train in-house staff to label data, and outsourcing can help reduce those costs.
2. Time-saving: Outsourcing data labelling can save time for researchers, allowing them to focus on their core research activities. It can be time-consuming to label large datasets, and outsourcing can help expedite the process.
3. Expertise and quality: Outsourcing data labelling to a professional data labelling service provider can ensure that the data is labelled accurately and consistently. These service providers have trained staff and quality control measures to ensure that the labelling is done correctly. Many researchers and research scholars choose this data labelling as side earning profession. You can visit my blog post on “Data Annotation (Data Labelling): A  Part-Time Job for Research Scholars ” for more details.
4. Scalability: Outsourcing data labeling can provide scalability to research projects that require large datasets to be labelled. Service providers can scale up or down based on project needs and timelines.
5. Access to diverse labelling options: Outsourcing data labelling can provide access to diverse labelling options, including multilingual labelling, sentiment analysis, or custom labeling options. This can help researchers get more comprehensive insights into their data.

Overall, outsourcing data labelling can help researchers save time and money, improve the quality of their data, and access a wider range of labelling options. However, it is essential to select a reliable and trustworthy data labelling service provider to ensure the accuracy and quality of the labelled data.

Website: https://haidata.ai

Founded: 06 November 2020

Headquarters: Bangalore

Domains:

• Image / Video Annotation
• Audio Annotation
• Text Annotation
• 3D Point Cloud Annotation
• Image Background Removal
• AI Data Sourcing

Every customer’s dataset and requirements are unique. Different pricing models.

Pricing Options (As on Feb 2023):

• Per Image:Starting from 3.31 INR (0.04 USD) per image (Less than 2 bounding boxes per image, without any attributes)
• Per annotation (bounding box/polygon, etc..):Starting from 1.66 INR (0.02 USD) per bounding box (minimum 8 to 10 avg. bounding boxes per image)
• Contract-to-hire: For longer duration projects, hire our highly skilled Data Specialists who’ll work either on-site or off-site.

Volume discounts available

All prices include stringent 3 Levels of QC (Quality Check) and post-delivery support.

Anolytics is praised as one of the best AI training data developers with a talent for data annotation, data labelling, and natural language processing as a full-stack workforce solution provider (NLP). The AI training data is required by the AI development lifecycle in order to teach machine learning models, computer vision systems, and AI algorithms the required automation inputs.

Anolytics is an expert at sorting, filtering, and segmenting raw datasets, such as unstructured photos, videos, texts, and audio data, in order to provide high-quality AI training data.

Website: https://www.anolytics.ai/

Founded: 2016

Headquarters: Levittown, NY

Website: https://www.cogitotech.com/

Founded: 2006

Headquarters: Levittown, NY

Specialities:

Categorize and moderate data for machine learning algorithms.

Generate training data for Computer vision and Autonomous vehicles, 

Strengthen your brand through customer sentiment analysis, 

Train machine learning algorithms through structured data sets, and moderate images, videos and content accurately and in real-time.

Turn big data into rich data through accurate categorization. Collect big data using our skilled and scalable workforce.

Select and categorize objects in images for AI or business intelligence, Train your search algorithms through structured data sets, Fast and accurate transcription of audio and video files, and Transcribe financial statements, invoices, and receipts quickly & efficiently.

Services:

Website: http://www.shaip.com/

Founded: 2018

Headquarters: Ahmedabad, India

Specialities:

Data Collection, Data Annotation & Labelling, Data De-identification, Data Transcription, Cloud-Based Transcription 

Qualitas Global Services is a global company with offices in Europe, the United States, and India. It specialises in core data labelling techniques like image tracking, data annotation, and video analytics for machine learning in industries like autonomous vehicles, agri-tech, sports, drones, retail, security and surveillance, and medical annotations, among others.

Website: http://www.qualitasglobal.com

Founded: 2014

Headquarters: Pune, Maharashtra

Services:

• 2D Bounding Box Annotation
• Polygon/Contour Annotation
• Semantic Segmentation
• Cuboidal Annotation
• Keypoint Annotation
• Polyline Annotation

Website: http://www.labelops.ai

Founded: 2020

Headquarters: Mannarkkad, Kerala

Specialities:

• Image annotation,
• Video Annotation,
• Text Annotation,
• Audio Transcription,
• Image Segmentation,
• Lidar annotation,
• Product categorization,
• Sentiment Analysis,
• Content Moderation.

Website: http://www.zuru.ai/

Founded: 2019

Headquarters: Bengaluru, India

Specialities:

• language services,
• data annotation,
• languages,
• interpretation,
• annotation,
• Artificial Intelligence,
• Machine learning,
• Data Science, and
• Data labelling

Autonomous vehicles & robotics

Healthcare & diagnostics

Retail & E-commerce

Media & Telecommunications

BFSI & Fintech

Beverages & Foodtech

Whether outsourcing data for labelling is ethical or not depends on several factors. Here are a few key considerations:

1. Data Privacy: One of the most critical ethical considerations is ensuring that the data being labelled is properly de-identified and that the privacy of individuals is protected. Researchers need to ensure that any sensitive or personally identifiable information is removed before the data is sent to a labelling service provider.
2. Data Security: Researchers must ensure that the labelling service provider has appropriate security measures in place to protect the data from unauthorized access, theft, or loss.
3. Quality of labelling: Researchers need to ensure that the labelling service provider is adequately trained and has quality control measures in place to ensure accurate and consistent labelling.
4. Compliance with regulations: Researchers need to ensure that they comply with any relevant regulations, such as the General Data Protection Regulation (GDPR) in the European Union or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, when outsourcing data for labelling.
5. Transparency: Researchers should be transparent about the use of outsourcing for data labelling and obtain informed consent from participants, where applicable.

In summary, outsourcing data for labelling can be ethical if researchers take appropriate measures to protect the privacy and security of the data, ensure accurate and consistent labelling, comply with relevant regulations, and are transparent about the use of outsourcing.

It is important to have a clear and detailed agreement with the company you outsource the data for labelling to ensure that both parties understand the scope of work, the quality requirements, and the expectations. Here are some key elements that should be included in the agreement:

1. Data security: The agreement should clearly outline the security measures that the company will take to protect the data, including measures to prevent unauthorized access, data loss, and data breaches.
2. Quality control: The agreement should specify the quality control measures that the company will take to ensure that the labelling is accurate and consistent.
3. Scope of work: The agreement should define the scope of work, including the types of data to be labelled, the number of labels required, and the labelling criteria.
4. Timeline: The agreement should include a timeline for completing the labelling work, including any milestones or deadlines.
5. Pricing and payment terms: The agreement should specify the pricing for the labelling work and the payment terms, including any deposit requirements, invoicing, and payment schedule.
6. Confidentiality: The agreement should include a confidentiality clause to ensure that the company will not disclose any confidential or proprietary information to third parties.
7. Liability and indemnification: The agreement should include a liability and indemnification clause to specify the responsibilities of each party and the remedies for any breaches.

It is important to have a legal professional review the agreement to ensure that it is comprehensive and meets all legal requirements.

As researchers work to make sense of the vast amounts of data available to them, data labelling has become an increasingly valuable tool for creating labelled datasets that can be used to train machine learning models and gain new insights into complex phenomena.

However, labelling large datasets can be a time-consuming and labor-intensive process, and researchers often turn to outsourcing providers to handle this task. While outsourcing data labelling can be an efficient way to process large volumes of data, it is essential for researchers to approach this process thoughtfully and carefully to ensure that their data is being handled in a secure and confidential manner.

One critical step in this process is the creation of a data labelling agreement, which can help to define the scope of work, outline timelines and payment terms, specify confidentiality requirements, and establish liability and indemnification.

In this discussion, we will explore the key considerations for outsourcing data labelling for research purposes and the importance of creating a data labelling agreement to ensure a successful outsourcing partnership.

Here are some key parameters you may find in Data Labelling agreement:

1. Scope of work: The agreement should specify the scope of work, including the types of data to be labelled, the labelling criteria, and the number of labels required.
2. Quality control: The agreement should define the quality control measures that the labelling service provider will take to ensure that the labelling is accurate and consistent, including the use of multiple labellers, regular reviews of labelled data, and feedback mechanisms for correcting errors.
3. Data privacy and security: The agreement should specify the security measures that the labelling service provider will take to protect the privacy and security of the data, including encryption, access controls, and data backup and recovery procedures.
4. Timelines: The agreement should include a timeline for completing the labelling work, including any milestones or deadlines, and specify the consequences for missing deadlines.
5. Pricing and payment terms: The agreement should specify the pricing for the labelling work and the payment terms, including any deposit requirements, invoicing, and payment schedule.
6. Confidentiality: The agreement should include a confidentiality clause to ensure that the labelling service provider will not disclose any confidential or proprietary information to third parties.
7. Liability and indemnification: The agreement should include a liability and indemnification clause to specify the responsibilities of each party and the remedies for any breaches.
8. Termination: The agreement should specify the conditions for terminating the agreement, including the consequences of early termination.
9. Applicable law and jurisdiction: The agreement should specify the governing law and jurisdiction for any disputes that may arise under the agreement.

It is essential to work with a legal professional to ensure that the agreement is legally binding and provides adequate protection for your research data. A legal professional can also help you navigate any regulatory requirements that may apply to your research projects, such as data privacy or confidentiality laws.

The main prerequisite for the researchers to successfully complete their investigations is high-quality annotated data. The accuracy of the annotation will affect how well the algorithm works. Now more than ever, there is a critical need to establish a safe and affordable method for image annotation.

Data labelling firms assist you with the best method to further your research and give you the necessary tools. Correct annotations can be produced by outsourcing your task to the most well-known and prosperous businesses.

Outsourcing data labelling can be a highly effective way for researchers to efficiently process and analyze large amounts of data. However, it is important to approach this process carefully and thoughtfully to ensure that both the researcher and the labelling service provider understand their respective roles and responsibilities, and that the researcher’s data is handled in a secure and confidential manner.

Creating a comprehensive data labelling agreement can help to clarify these expectations and minimize the risk of misunderstandings or disputes. By taking the time to define the scope of the work, the labelling methodology, the timeline, pricing and payment terms, confidentiality requirements, liability and indemnification, termination conditions, and applicable law and jurisdiction, researchers can build a strong foundation for a successful and productive outsourcing partnership.

Ultimately, the creation of a thorough and mutually agreeable data labelling agreement can help to establish trust, promote clear communication, and drive successful outcomes for all parties involved.