Stay updated with Market Research Intellect's Data Enrichment Tool Market Report, valued at USD 3.5 billion in 2024, projected to reach USD 9.2 billion by 2033 with a CAGR of 12.5% (2026-2033).

The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use.

Global Data Enrichment Solutions market size is expected to reach $4.65 billion by 2029 at 12.5%, segmented as by cloud, public cloud, private cloud, hybrid cloud

The data enrichment tool market is projected to reach $3.5 billion by 2033, experiencing a CAGR of 12.5% during the forecast period. The growth is attributed to the increasing adoption of data-driven decision-making across industries, including marketing, sales, and customer service. Data enrichment tools enable businesses to enhance their existing data by adding additional attributes, such as demographics, firmographics, and behavioral data. This enriched data provides valuable insights into customer preferences, buying patterns, and market trends, enabling businesses to make more informed and targeted decisions. The market is segmented by type (cloud-based and on-premises) and application (SMEs and large enterprises). Cloud-based solutions are gaining popularity due to their scalability, cost-effectiveness, and ease of deployment. SMEs are adopting data enrichment tools to improve their customer understanding and marketing campaigns, while large enterprises are leveraging these tools to enhance their data-driven initiatives and drive operational efficiency. Key players in the market include Clearbit, Snov.io API, InsideView, and BeenVerified, among others. North America holds the largest market share, followed by Europe and Asia Pacific. The growing adoption of data analytics and the increasing importance of customer data are driving the growth in these regions.

Success.ai’s B2B Contact Data Enrichment API empowers businesses to optimize their sales and marketing initiatives by providing seamless access to verified, continuously updated B2B contact information. Leveraging a database of over 700 million global profiles, our API enriches your existing records with critical data points, including job titles, work emails, phone numbers, LinkedIn URLs, and more.

This real-time, AI-validated enrichment ensures that you are always engaging with the most relevant and high-potential prospects. Supported by our Best Price Guarantee, the Contact Enrichment API is indispensable for organizations aiming to streamline workflows, improve targeting, and maximize conversion rates.

Why Choose Success.ai’s Contact Enrichment API?

1. Real-Time Enrichment for Precision Outreach

   • Instantly enhance your CRM or marketing platform with verified work emails, phone numbers, job titles, and professional histories.
   • AI-driven validation ensures 99% accuracy, minimizing outreach errors and boosting engagement efficiency.

2. Comprehensive Global Coverage

   • Includes professionals from all major industries, sectors, and geographic regions.
   • Gain insights into decision-makers, technical leads, and influencers across diverse markets, enabling you to scale your outreach globally.

3. Anytime Access with Powerful Filtering

   • Query the API anytime to enrich records on-demand, ensuring your contact data remains current and relevant.
   • Leverage granular filters to target the specific roles, industries, or company sizes that align with your strategic goals.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing responsible, lawful data usage for all enrichment activities.

Data Highlights:

• 700M+ Verified Global Profiles: Enhance your contact lists with up-to-date information on professionals worldwide.
• Job Titles and Professional Histories: Gain visibility into career progressions, areas of expertise, and leadership roles.
• Verified Contact Details: Add work emails, phone numbers, and LinkedIn URLs to refine targeting and streamline communication.
• Industry and Location Data: Utilize firmographic and demographic insights to tailor campaigns and focus on high-value prospects.

Key Features of the Contact Enrichment API:

1. Seamless Integration with Your Systems

   • Easily integrate the API into CRMs, marketing platforms, and analytics tools.
   • Automated workflows ensure that your contact data is always current, reducing manual data updates and saving resources.

2. Granular Filtering and Query Capabilities

   • Filter contacts by industry, geographic region, company size, or job function.
   • Focus on the most relevant prospects, ensuring your outreach resonates and drives higher conversion rates.

3. Real-Time Updates and Continuous Enrichment

   • Receive real-time updates to keep your data fresh, reflecting new hires, promotions, and organizational changes.
   • Stay agile and adapt to market shifts by always targeting the most pertinent decision-makers.

4. AI-Validated Accuracy

   • AI-driven validation ensures 99% data accuracy, providing confidence in your targeting and reducing wasted outreach efforts.

Strategic Use Cases:

1. Sales and Lead Generation

   • Arm your sales team with the latest contact details and job titles, ensuring they always approach the right prospects.
   • Use enriched data to segment leads and tailor pitches for higher conversion rates.

2. Marketing Campaigns and ABM Strategies

   • Refine account-based marketing (ABM) strategies by enriching contacts with detailed firmographic and demographic insights.
   • Personalize messaging and content based on prospect roles, responsibilities, and organizational priorities.

3. Partnership Development and Vendor Evaluation

   • Identify and connect with potential partners or vendors aligned with your product offerings and market objectives.
   • Foster collaborations that expand market reach, improve customer experiences, or enhance operational efficiencies.

4. Recruitment and Talent Acquisition

   • Enrich candidate pipelines with accurate, updated contact information and professional backgrounds.
   • Streamline recruitment workflows and ensure you’re reaching high-quality candidates in niche industries.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality enrichment data at competitive prices, ensuring strong ROI for your sales and marketing investments.

2. Seamless Integration

   • Incorporate verified contact data directly into your existing workflows and tools, eliminating data silos and manual updates.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and improve engagement outcomes in all campaigns.

4. Custo...

The Data Enrichment Tool market is experiencing robust growth, driven by the increasing need for businesses to improve data quality and enhance customer relationship management (CRM) systems. The market's expansion is fueled by a surge in digital transformation initiatives across various industries, leading to a greater reliance on accurate and comprehensive customer data. Businesses are leveraging data enrichment tools to improve marketing campaign effectiveness, personalize customer interactions, and enhance sales conversion rates. The market size in 2025 is estimated at $5 billion, reflecting a considerable expansion from previous years. This growth is projected to continue at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, indicating a significant and sustained market opportunity. This positive outlook is underpinned by factors such as the growing adoption of cloud-based solutions, advancements in artificial intelligence (AI) and machine learning (ML) technologies within data enrichment platforms, and the increasing availability of diverse data sources for integration. However, challenges remain. Data privacy regulations and concerns about data security are significant restraints. The complexity of integrating data enrichment tools into existing CRM and marketing automation systems can also hinder adoption. Despite these challenges, the market is segmented by various factors including deployment mode (cloud-based vs. on-premise), organization size (SMEs vs. large enterprises), and industry vertical (e.g., finance, healthcare, retail). Leading vendors such as Clearbit, ZoomInfo, and Experian are constantly innovating and expanding their offerings, further fueling market competition and growth. The market’s continued expansion will be driven by the imperative for businesses to leverage high-quality data for informed decision-making, competitive advantage, and optimized operational efficiency.

Unacast’s Consumer Behavior Data Enrichment provides a robust view of consumers’ affinities, lifestyles, stages of life, and travel patterns. Utilize Consumer Data Enrichment to build multi-dimensional customer profiles, generate look-alike models, extend reach to different marketing channels, and clarify households and connections between devices.

Our on-demand API can enrich opted-in customer data (Mobile Advertiser IDs) or hashed email addresses (HEMs) and FLIPs (frequently leveraged IPs). Based on the query, the API will deliver associated data such as HEM, app data, Unacast Persona Data, demographic data, countries traveled, and more.

Companies use Unacast Consumer Data Enrichment for: - Understanding reach - Identity Graphs - Measuring campaign performance - Measuring return on advertising spend (ROAS) - Addressability - Audience creation - Psychographic analysis - Enhancing customer data - Personalization - Market research - Creating look-alike panels

Make your customer data more powerful, illuminate data gaps, and understand the relationships between devices.

Pricing can vary based on product, scope, region, etc. Reach out to us here to get more details on pricing.

The Mathematics Enrichment Classes market has seen significant evolution over recent years, driven by a growing emphasis on STEM (Science, Technology, Engineering, and Mathematics) education across the globe. These classes provide students with advanced mathematical skills, catering not only to those who seek to enh

The MRO (Maintenance, Repair, and Operations) Data Cleansing and Enrichment Service market is experiencing robust growth, driven by the increasing need for accurate and reliable data across various industries. The digital transformation sweeping manufacturing, oil & gas, and transportation sectors is creating a surge in data volume, but much of this data is fragmented, incomplete, or inconsistent. This necessitates sophisticated data cleansing and enrichment solutions to improve operational efficiency, predictive maintenance capabilities, and informed decision-making. The market's expansion is fueled by the adoption of Industry 4.0 technologies, including IoT sensors and connected devices, generating massive datasets requiring rigorous cleaning and enrichment processes. Furthermore, regulatory compliance pressures and the need for improved supply chain visibility are contributing to strong market demand. We estimate the 2025 market size to be $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is primarily driven by the Chemical, Oil & Gas, and Pharmaceutical industries' increasing reliance on data-driven insights for optimizing operations and reducing downtime. Significant regional variations exist, with North America and Europe currently holding the largest market shares, but rapid growth is anticipated in the Asia-Pacific region due to the increasing industrialization and digitalization initiatives underway. The market segmentation by application reveals a diverse landscape. The Chemical and Oil & Gas industries are early adopters, followed closely by Pharmaceuticals, leveraging data cleansing and enrichment to improve safety, comply with regulations, and optimize asset management. The Mining and Transportation sectors are also rapidly adopting these services to enhance operational efficiency and predictive maintenance. Within the types of services offered, data cleansing represents a larger share currently, focusing on identifying and removing inconsistencies and inaccuracies. However, data enrichment, which involves augmenting existing data with external sources to improve its completeness and context, is experiencing accelerated growth due to its capacity to unlock deeper insights. While several established players operate in the market, such as Enventure, Sphera, and OptimizeMRO, the landscape is also characterized by numerous smaller, specialized service providers, indicative of a competitive and dynamic market structure. The presence of regional players further suggests opportunities for both consolidation and expansion in the coming years.

A biomedical dataset supporting ontology enrichment from texts, by concept discovery and placement, adapting the MedMentions dataset (PubMed abstracts) with SNOMED CT of versions in 2014 and 2017 under the Diseases (disorder) sub-category and the broader categories of Clinical finding, Procedure, and Pharmaceutical / biologic (CPP) product.

The dataset is documented in the work, Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement, on arXiv: https://arxiv.org/abs/2306.14704 (CIKM 2023). The companion code is available at https://github.com/KRR-Oxford/OET.

Out-of-KB mention discovery (including the settings of mention-level data) is further partly documented in the work, Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking, on arXiv: https://arxiv.org/abs/2302.07189 (CIKM 2023).

ver4: we made a version of mention-level data for out-of-KB discovery and concept placement separately: the former (for out-of-KB discovery) has out-of-KB mentions in training data, while the latter (for concept placement) has only out-of-KB mentions during the evaluation (validation and test) and not in the training data. Also, we split the original "test-NIL.jsonl" (now "test-NIL-all.jsonl") into "valid-NIL.jsonl" and "test-NIL.jsonl" for a better evaluation.

ver3: we revised and updated mention-level data (syn_full, synonym augmentation setting) and the folder structure, and also updated the edge catalogues with complex edges.

ver2: we revised the mention-level data by only keeping out-of-KB mentions (or "NIL" mentions) associated with one-hop edges (including leaf nodes, as

Acknowledgement of data sources and tools below:

SNOMED CT https://www.nlm.nih.gov/healthit/snomedct/archive.html (and use snomed-owl-toolkit to form .owl files) UMLS https://www.nlm.nih.gov/research/umls/licensedcontent/umlsarchives04.html (and mainly use MRCONSO for mapping UMLS to SNOMED CT)

MedMentions https://github.com/chanzuckerberg/MedMentions (source of entity linking)

Protégé http://protegeproject.github.io/protege/

snomed-owl-toolkit https://github.com/IHTSDO/snomed-owl-toolkit DeepOnto https://github.com/KRR-Oxford/DeepOnto (based on OWLAPI https://owlapi.sourceforge.net/) for ontology processing and complex concept verbalisation

The email enrichment tool market is experiencing robust growth, driven by the increasing need for businesses to improve data quality and personalize customer interactions. The market, currently valued at approximately $2 billion in 2025, is projected to expand at a compound annual growth rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market value of over $6 billion by 2033. This growth is fueled by several key factors. Firstly, the rising adoption of marketing automation platforms and CRM systems necessitates accurate and comprehensive customer data, driving demand for email enrichment tools to enhance data quality and completeness. Secondly, the growing focus on personalized marketing campaigns and improved customer engagement necessitates accurate email addresses and associated contact information for targeted outreach. Thirdly, regulatory compliance requirements, such as GDPR and CCPA, further incentivize businesses to maintain accurate and up-to-date customer data, making email enrichment tools essential for compliance. The cloud-based segment dominates the market due to its scalability, cost-effectiveness, and ease of integration with existing systems. Key application areas include marketing and sales (the largest segment), followed by customer service and human resources, each contributing significantly to overall market demand. Competition in the email enrichment tool market is intense, with numerous players offering diverse solutions. Established players like ZoomInfo and Lead411 compete with newer, agile entrants such as Snov.io and Hunter.io, creating a dynamic landscape characterized by innovation and continuous improvement. Regional variations exist, with North America currently holding the largest market share, driven by high adoption rates and advanced technological infrastructure. However, growth in regions like Asia-Pacific is expected to accelerate significantly over the forecast period, fueled by increasing digitalization and e-commerce adoption in developing economies. Despite the positive outlook, market growth might face challenges from data privacy concerns and potential increases in data acquisition costs. Nevertheless, the overall trend indicates a consistent, positive growth trajectory for the email enrichment tool market over the next decade.

We maintain outstanding customer satisfaction with high quality products and services using mature and cost-effective processes​. By using manual operations, semi-automatic operations​ and A.I./deep learning technologies, we research, source, aggregate and enrich ​third party​ data​, customer​ proprietary data​ or GeoJunxion​ proprietary data​ and deliver excellent, reliable results based on customer specific requirements.

The usual process flow includes:

1. External data: Databases/documents/sensor data/own data​

2. Data ingestion/normalization/harmonization/aggregation/enrichment

3. Match/mingle them against an existing GeoJunxion database​ if requested

4. Export data in required customer’s format​

5. Our customer creates products/solutions with our delivery​

This dataset contains the supplementary data for the research paper "Haploinsufficiency of the intellectual disability gene SETD5 disturbs developmental gene expression and cognition".

The contained files have the following content: 'Supplementary Figures.pdf' Additional figures (as referenced in the paper). 'Supplementary Table 1. Statistics.xlsx' Details on statistical tests performed in the paper. 'Supplementary Table 2. Differentially expressed gene analysis.xlsx' Results for the differential gene expression analysis for embryonic (E9.5; analysis with edgeR) and in vitro (ESCs, EBs, NPCs; analysis with DESeq2) samples. 'Supplementary Table 3. Gene Ontology (GO) term enrichment analysis.xlsx' Results for the GO term enrichment analysis for differentially expressed genes in embryonic (GO E9.5) and in vitro (GO ESC, GO EBs, GO NPCs) samples. Differentially expressed genes for in vitro samples were split into upregulated and downregulated genes (up/down) and the analysis was performed on each subset (e.g. GO ESC up / GO ESC down). 'Supplementary Table 4. Differentially expressed gene analysis for CFC samples.xlsx' Results for the differential gene expression analysis for samples from adult mice before (HC - Homecage) and 1h and 3h after contextual fear conditioning (1h and 3h, respectively). Each sheet shows the results for a different comparison. Sheets 1-3 show results for comparisons between timepoints for wild type (WT) samples only and sheets 4-6 for the same comparisons in mutant (Het) samples. Sheets 7-9 show results for comparisons between genotypes at each time point and sheet 10 contains the results for the analysis of differential expression trajectories between wild type and mutant. 'Supplementary Table 5. Cluster identification.xlsx' Results for k-means clustering of genes by expression. Sheet 1 shows clustering of just the genes with significantly different expression trajectories between genotypes. Sheet 2 shows clustering of all genes that are significantly differentially expressed in any of the comparisons (includes also genes with same trajectories). 'Supplementary Table 6. GO term cluster analysis.xlsx' Results for the GO term enrichment analysis and EWCE analysis for enrichment of cell type specific genes for each cluster identified by clustering genes with different expression trajectories (see Table S5, sheet 1). 'Supplementary Table 7. Setd5 mass spectrometry results.xlsx' Results showing proteins interacting with Setd5 as identified by mass spectrometry. Sheet 1 shows protein protein interaction data generated from these results (combined with data from the STRING database. Sheet 2 shows the results of the statistical analysis with limma. 'Supplementary Table 8. PolII ChIP-seq analysis.xlsx' Results for the Chip-Seq analysis for binding of RNA polymerase II (PolII). Sheet 1 shows results for differential binding of PolII at the transcription start site (TSS) between genotypes and sheets 2+3 show the corresponding GO enrichment analysis for these differentially bound genes. Sheet 4 shows RNAseq counts for genes with increased binding of PolII at the TSS.

The B2B Data Enrichment Tool market is witnessing a significant transformation as businesses increasingly recognize the value of high-quality, comprehensive data in driving decision-making processes and enhancing customer relationships. At its core, a B2B data enrichment tool enables organizations to enhance their e

The global Email Enrichment Tool market size was valued at USD 4,481.9 million in 2022 and is projected to register a CAGR of 17.2% from 2023 to 2030. Increasing demand for improved email marketing campaigns, growing popularity of AI and ML-powered tools, and rising need for personalized customer interactions drive market growth. Key market segments include cloud-based deployment, customer service applications, and North America as a prominent region. Prominent players like BeenVerified, FullContact, PeopleLooker, Clearbit, and BetaPage offer innovative solutions. Market growth is anticipated due to factors such as the growing volume of email communications, advancements in data analytics, and regulatory compliance requirements. However, concerns about data privacy and ethical considerations may pose challenges to the market's expansion. The global email enrichment tool market size is expected to reach USD 1.4 billion by 2026, exhibiting a CAGR of 16.5% during the forecast period. The market growth is primarily driven by the increasing adoption of email marketing campaigns, growing need for accurate and up-to-date customer data, and rising demand for personalized marketing experiences.

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts. , The proposed network is built starting from two distinct data sources:

the entire dataset dump from Semantic Scholar (with particular emphasis on the authors and papers datasets) the entire list of Italian faculty members as maintained by Cineca (under appointment by the Italian Ministry of University and Research).

By means of a custom name-identity recognition algorithm (details are available in the accompanying paper published in Scientific Data), the names of the authors in the Semantic Scholar dataset have been mapped against the names contained in the Cineca dataset and authors with no match (e.g., because of not being part of an Italian university) have been discarded. The remaining authors will compose the nodes of the network, which have been enriched with node-related (i.e., author-related) attributes. In order to build the network edges, we leveraged the papers dataset from Semantic Scholar: specifically, any two authors are said to be connected if there is at least one pap..., , # Data cleaning and enrichment through data integration: networking the Italian academia

https://doi.org/10.5061/dryad.wpzgmsbwj

Manuscript published in Scientific Data with DOI .

Description of the data and file structure

This repository contains two main data files:

• edge_data_AGG.csv, the full network in comma-separated edge list format (this file contains mainly temporal co-authorship information);
• Coauthorship_Network_AGG.graphml, the full network in GraphML format.Â

along with several supplementary data, listed below, useful only to build the network (i.e., for reproducibility only):

• University-City-match.xlsx, an Excel file that maps the name of a university against the city where its respective headquarter is located;
• Areas-SS-CINECA-match.xlsx, an Excel file that maps the research areas in Cineca against the research areas in Semantic Scholar.

Description of the main data files

The `Coauthorship_Networ...

These data contain aggregated survey responses assessing the quality and completeness of metadata for datasets deposited in public repositories and for the same datasets after professional curation.Responses were provided by 10 professional editors representing life, social and physical sciences. Each were randomly assigned four datasets to assess, half (20) of which had been curated according to the standards of Springer Nature's Research Data Support service and half (20) which had not.Curated datasets were shared privately with research participants. The versions that did not receive curation via Springer Nature's Research Data Support are openly accessible.Single-blind testing was employed; the researchers were not made aware which datasets had been curated and which had not, and it was ensured that no participant assessed the same dataset before and after curation. Responses were collected via an online survey. The relevant question and scoring is provided below:Rate the overall quality and completeness of the metadata for the dataset (with regards to finding and accessing and citing the data, not reusing the data)1 = not complete, 5 = very complete

Additional file 2. Complete use case for the GeneTonic package, based on the RNA-seq dataset for macrophage immune stimulation (Interferon Gamma treatment vs naive cells, ERP020977).

ABSTRACT

The complexity of brain circuits is sculpted both by innate genetic programs and environmental stimuli. Since the 1960s scientists have noticed that raising rodents in an enriched environment (EE) is able to improve all aspects of brain plasticity, from learning and memory to visual plasticity in adult and developing animals. Importantly, EE has also been shown to have beneficial effects on a variety of preclinical models of central nervous system diseases: Alzheimer’s and Parkinson’s disease, Rett syndrome, epilepsy etc, prompting intervention protocols in humans. However, the “enrichment derived key signals” through which this special environment performs its broad positive effects on brain health have not been completely elucidated yet. Here, we focused on signals coming from the body periphery and in particular on the gut microbiota. We found that the intestinal microbiota composition of EE mice is significantly different from the one of standard raised (ST) animals. Treatment of EE mice with an antibiotic cocktail completely prevented the EE-driven enhancement of OD plasticity. Strikingly, the fecal microbiota transplant from EE donors to adult ST mice was able to re-activate OD plasticity in the ST recipients. Thus, taken together our data suggest that experience-dependent changes in gut microbiota regulate brain plasticity.

METHODS

In the first dataset (Dataset1, files called zr2423) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J mice raised in EE or in ST from birth and collected at different time points during their lives.

To analyze the composition of the microbiota of ST and EE mice at different ages, fresh faeces were collected longitudinally in the same subject at postnatal day (P)20 (n=6), P25 (n=6) and P90 (n=6).

In the second dataset (Dataset2, files called zr2747) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J: adult donor mice living in EE (EE, n=8), adult recipient mice living in ST condition before the fecal transplantation (preFT, n=8) and 4 weeks after the fecal transplantation (postFT, n=8).

For further details about the sample names see the “Explanation Table”.

Bacterial DNA was extracted using a specific kit (QIAamp Powerfecal DNA kit, Qiagen) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Irvine, CA, USA).

Targeted Library Preparation: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).

Sequencing: The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). The sequencing was performed with >10% PhiX spike-in.

ABSTRACT Students with high abilities/giftedness stand out for their superior intelligence or special talent in different areas of knowledge, requiring specialized care. Enrichment activities are educational alternatives planned to serve this public. The aim of this research was to describe the enrichment activities experienced by students with high abilities/giftedness, based on the reports of students themselves, their families, and teachers. Questionnaires and specific protocols were developed and applied for each group of informants. As a result, it was found that students attended few enrichment activities, most of which available in laboratories of a university. They often consisted of exploratory activities, without considering the students’ interest and abilities, not reaching type II and III enrichments, according to reports from all segments.