Habitat classification map based on surveys completed along the 58‐km long Assateague barrier island stretching from the Ocean City inlet in Maryland, down past Chincoteague Island in northern Virginia. The data was collected June 20th-25th, 2014 and May 12th - 21th, 2015. Full coverage side-scan sonar and partial coverage bathymetry data were collected using an EdgeTech 6205 Multiphase Echosounder. In total, 73 square kilometers were mapped at primarily at 100m line spacing and 80 m swath range per channel (to allow overlap between lines).

According to Cognitive Market Research, the global Data Classification market size was USD 1842.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 25.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 736.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 552.66 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 423.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 27.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 92.11 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 36.84 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.9% from 2024 to 2031.
The Solutions is the fastest growing segment of the Data Classification industry

Market Dynamics of Data Classification Market

Key Drivers for Data Classification Market

Increasing Data Privacy and Security Regulations to Boost Market Growth

The growing emphasis on statistics privateness and protection rules is using boom inside the records type marketplace. As businesses face stricter compliance requirements and heightened scrutiny over facts managing practices, there's an increasing need for sturdy data category solutions. These answers assist in categorizing and managing records based on their sensitivity and compliance desires, thereby mitigating risks related to records breaches and non-compliance consequences. Enhanced rules, which include GDPR and CCPA, are prompting corporations to spend money on superior records-type technology to shield touchy statistics and make certain adherence to prison standards, for that reason, fueling marketplace enlargement. For instance, In order to assist Indian businesses in consolidating all facets of risk under one roof via integrated risk management technology, Rotiviti India partnered with Riskconnect.

Expansion of the Data Breaches and Cyberattacks to Drive Market Growth

The surge in statistics breaches and cyberattacks is significantly boosting the facts category market. As cyber threats become more sophisticated and common, businesses are more and more adopting information classification answers to protect sensitive records. These technologies assist in figuring out, categorizing, and securing facts in line with their sensitivity, thereby minimizing the impact of ability breaches. With cyberattacks concentrated on valuable information and regulatory pressures mounting, agencies are investing in information-type systems to decorate their safety posture and ensure compliance. This developing demand for sturdy facts safety measures is riding the growth of the data category market.

Restraint Factor for the Data Classification Market

Complexity and Cost, will Limit Market Growth

The complexity and cost related to records classification are hindering the market boom. Implementing complete information classification solutions often calls for sizeable investment in advanced technology and professional personnel. The complexity of integrating those systems with present IT infrastructure and ensuring correct classification throughout various records assets provides to the mission. Additionally, ongoing maintenance and updates to hold pace with evolving threats and regulatory adjustments contribute to excessive prices. These factors can be especially burdensome for small and medium-sized organizations, limiting their capability to undertake powerful records class answers and thereby restraining usual marketplace enlargement.

Impact of Covid-19 on the Data Classification Market

The COVID-19 pandemic has had a combined effect on the statistics classification market. On the one hand, the improved shift too far-off work and expanded reliance on virtual systems heightened the want for robust statistics classification answers to stable, sensitive records and make sure compliance with data protection policies. On the other hand, economic uncertainties and price range constraints in the course of the p...

Data Classification Market size was valued at USD 1664.66 Million in 2024 and is projected to reach USD 9486.25 Million by 2032, growing at a CAGR of 24.3% during the forecast period 2026-2032.

Global Data Classification Market Drivers

The market drivers for the Data Classification Market can be influenced by various factors. These may include:

Increasing Data Volume: In order to maintain data security, compliance, and effective use, there is an increasing requirement to manage and classify the data produced by enterprises in an exponentially growing amount. Regulatory Compliance: Organizations must categorize their data based on the sensitivity levels required by strict data protection laws like the GDPR, CCPA, HIPAA, and others. Adoption of data classification solutions is driven by compliance requirements, which guarantee adherence to regulatory standards and prevent heavy penalties.

Data Security Concerns: Organizations are concentrating on strengthening their data security procedures due to the increase in cyber threats and data breaches. Classifying data makes it easier to find sensitive information and implement the right security measures to keep it safe from theft or unwanted access.

Growing Adoption of Cloud Services: As cloud computing services become more widely used, strong data classification techniques are required to guarantee data security and compliance, particularly when data is transferred between different cloud environments and storage locations. Increasing Awareness of Data Privacy: The need for solutions that allow for better management and protection of sensitive data through classification and encryption is being driven by heightened awareness of data privacy issues among consumers and enterprises. Combining Data Loss Prevention (DLP) Systems: Through the identification, monitoring, and prevention of sensitive information leakage or unlawful transfer, data categorization integrated with DLP systems improves data protection capabilities. Emergence of AI and Machine Learning Technologies: By incorporating these technologies into data categorization systems, data may be identified and classified more automatically and accurately, saving labor and increasing efficiency. Demand for Data Governance and Lifecycle Management: In order to maintain data quality, integrity, and compliance throughout its lifecycle, organizations are realizing more and more how important it is to have effective data governance and lifecycle management. A key component of putting into practice efficient data governance procedures is data classification.

The Governance, Risk Management, and Compliance (GRC) Data Classification market is poised for substantial growth, projected to reach an estimated $5,800 million by 2025, driven by an impressive Compound Annual Growth Rate (CAGR) of 16.5% through 2033. This expansion is primarily fueled by escalating data volumes across all sectors, coupled with increasingly stringent regulatory landscapes and the ever-present threat of sophisticated cyberattacks. Organizations are recognizing data classification not just as a compliance necessity but as a strategic imperative for effective risk management and the enablement of advanced analytics. The burgeoning adoption of cloud computing and the proliferation of sensitive data across hybrid environments further necessitate robust data classification solutions to ensure data privacy, security, and integrity. The BFSI sector, grappling with extensive customer data and regulatory pressures like GDPR and CCPA, leads the adoption, followed closely by government and defense agencies prioritizing national security and citizen data protection. The market's trajectory is further shaped by key trends such as the increasing demand for automated data classification powered by Artificial Intelligence (AI) and Machine Learning (ML), which enhance accuracy and efficiency while reducing manual effort. The integration of GRC data classification with broader data governance frameworks and security operations centers (SOCs) is becoming a standard practice, offering a holistic approach to data management. However, the market faces certain restraints, including the complexity of classifying unstructured data, the high cost of implementing and maintaining advanced classification solutions, and a potential shortage of skilled professionals with expertise in data security and compliance. Despite these challenges, the overarching need for robust data protection and regulatory adherence will continue to propel the GRC data classification market forward, with significant opportunities in emerging economies and specialized industry verticals. This report provides a comprehensive analysis of the Governance, Risk Management and Compliance (GRC) Data Classification market, offering insights into its current state and future trajectory. Focusing on the period between 2019 and 2033, with a base year of 2025, this study leverages historical data from 2019-2024 and provides a detailed forecast for the upcoming years. The market's intricate dynamics, driven by regulatory pressures, technological advancements, and evolving data security needs, are explored in depth. With an estimated market size projected to reach several million dollars by 2025 and significant growth anticipated throughout the forecast period, this report is an indispensable resource for stakeholders seeking to navigate this critical domain.

Benthic habitat maps were developed for the CACO study areas following the top-down mapping approach, for which habitat map units are geologically defined based on the presumption that geologic environments or features contain distinct biological assemblages. The resulting habitats are classified according to the CMECS framework and are referred to as “biotopes.” The term “biotope” is specific in that it integrates biotic-abiotic characteristics to offer more ecologically meaningful information. In this study, biotopes reflect the relationship between macrofaunal communities and geological features of their associated environments within the defined map units. The resulting biotopes are considered preliminary because the relationships identified have not been repeatedly demonstrated over time.

Satellite-derived data for sea surface temperature, salinity, chlorophyll; euphotic depth; and modeled bottom to surface temperature differences were evaluated to assess the utility of these products as proxies for in situ measurements. The data were used to classify surface waters in three regions of the Gulf of Mexico using subcomponents and modifiers from the Coastal and Marine Ecological Classification Standard (CMECS) Water Column Component (WC) to determine if CMECS categories could be effectively used to categorize in situ data into meaningful management units. The Naval Research Laboratory at the Stennis Space Center (NRL/SSC) processed MODIS-Aqua satellite imagery covering the Gulf of Mexico from January 2005 to December 2009. Daily, level-1B image files from the NASA LAADS Web were processed through the NRL/SSC Automated Processing System (APS). Sea surface temperature and salinity were classified into CMECS WC temperature and salinity subcomponent categories, respectively. Three modifiers from the WC were also used for the pelagic classification: water column stability, productivity, and photic quality. Modeled bottom to surface temperature differences were used to assign classification for water column stability, surface chlorophyll was used to determine productivity, and euphotic depth was used to indicate the photic quality. Maps showing the CMECS Water Column Component classes for chlorophyll concentration, euphotic depth, sea surface salinity, sea surface temperature (HDF4), and bottom-to-surface temperatures (netCDF) were produced from the APS output images.

Benthic habitat maps were developed for the Otis Pike and Sunken Forest study areas following the top-down mapping approach, for which habitat map units are geologically defined based on the presumption that geologic environments or features contain distinct biological assemblages. The resulting habitats are classified according to the CMECS framework and are referred to as “biotopes.” The term “biotope” is specific in that it integrates biotic-abiotic characteristics to offer more ecologically meaningful information. In this study, biotopes reflect the relationship between macrofaunal communities and geological features of their associated environments within the defined map units. The resulting biotopes are considered preliminary because the relationships identified have not been repeatedly demonstrated over time, as this study represents the first of its kind within FIIS.

Data Classification as a Service Market Outlook

According to our latest research, the global Data Classification as a Service market size reached USD 1.52 billion in 2024, reflecting robust growth driven by the rising need for data security and compliance across industries. The market is projected to expand at a CAGR of 22.8% during the forecast period, reaching an estimated USD 11.45 billion by 2033. This remarkable growth is attributed to the increasing adoption of cloud computing, stringent regulatory requirements, and the growing emphasis on data governance and risk management. As organizations worldwide grapple with vast volumes of sensitive data, the demand for scalable, efficient, and compliant data classification solutions continues to surge.

One of the primary growth factors propelling the Data Classification as a Service market is the escalating frequency and sophistication of cyber threats. With data breaches becoming more prevalent and costly, organizations are prioritizing proactive measures to safeguard their information assets. Data classification as a service enables enterprises to identify, categorize, and secure sensitive data, thereby reducing the risk of unauthorized access and data leakage. Furthermore, the proliferation of remote work and digital transformation initiatives has expanded the attack surface, compelling businesses to adopt advanced data security solutions that can be seamlessly integrated across distributed environments. This trend is particularly pronounced in highly regulated industries such as BFSI, healthcare, and government, where data protection is paramount.

Another significant driver for the market is the evolving regulatory landscape, which mandates stringent data privacy and compliance standards. Regulations such as GDPR, CCPA, HIPAA, and others have heightened the need for organizations to implement robust data classification frameworks to ensure compliance and avoid hefty penalties. Data Classification as a Service offers automated, policy-driven solutions that help organizations maintain compliance by accurately identifying and managing sensitive information. The ability to provide real-time visibility into data assets, coupled with automated reporting and audit capabilities, has made these services indispensable for compliance management. As regulatory requirements continue to evolve, organizations are increasingly turning to specialized service providers to stay ahead of compliance challenges.

The rapid adoption of cloud technologies and the migration of critical workloads to hybrid and multi-cloud environments are further fueling market growth. Cloud-based data classification services offer scalability, flexibility, and cost-effectiveness, enabling organizations to manage data across diverse platforms without compromising security. These services support seamless integration with existing IT infrastructure and provide centralized control over data assets, regardless of location. As businesses embrace digital transformation and cloud-first strategies, the demand for cloud-native data classification solutions is expected to accelerate, driving overall market expansion. Additionally, innovations in artificial intelligence and machine learning are enhancing the accuracy and efficiency of data classification, further strengthening the value proposition of these services.

From a regional perspective, North America currently dominates the Data Classification as a Service market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology providers, high awareness of data security, and strict regulatory frameworks. Europe follows closely, driven by robust data protection regulations and increasing investments in cybersecurity infrastructure. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding internet penetration, and rising cybersecurity threats. Emerging markets in Latin America and the Middle East & Africa are also experiencing increased adoption, supported by government initiatives and growing awareness of data privacy. Overall, the global market is characterized by dynamic growth across regions, with each exhibiting unique drivers and opportunities.

Component Analysis

The Data Classification as a Service market by component is broadly segmented into software and services. The software segment encompasses a range of solutions designed to automate the identification, labeling, and categorization of data based on sensitivity a

Data Classification Software Market Outlook

According to our latest research, the global Data Classification Software market size reached USD 2.47 billion in 2024, reflecting robust adoption across industries. The market is projected to expand at a CAGR of 20.6% from 2025 to 2033, reaching a substantial USD 16.12 billion by 2033. This remarkable growth is primarily driven by the increasing prioritization of data security, stringent regulatory requirements, and the surge in digital transformation initiatives across diverse sectors.

One of the most significant growth factors propelling the Data Classification Software market is the escalating frequency and sophistication of cyber threats. As organizations generate and store massive volumes of sensitive data, the risk of data breaches and unauthorized access has intensified. Data classification solutions enable enterprises to categorize information based on sensitivity and compliance requirements, thereby implementing more effective security protocols. This capability is especially crucial in industries such as BFSI, healthcare, and government, where the protection of confidential data is paramount. The growing awareness about the financial and reputational repercussions of data breaches has compelled organizations to invest in advanced data classification tools, further fueling market expansion.

Another key driver is the evolving regulatory landscape. Governments and regulatory bodies worldwide are introducing stringent data protection regulations, such as GDPR in Europe, CCPA in California, and similar frameworks in Asia Pacific. These regulations mandate robust data governance practices, including the accurate classification and management of sensitive information. Non-compliance can result in hefty fines and legal consequences, which has heightened the urgency for businesses to deploy comprehensive data classification software. The need to demonstrate compliance and maintain audit readiness is pushing organizations to adopt solutions that streamline data discovery, classification, and policy enforcement processes.

The rapid digitalization of business operations and the proliferation of cloud computing are also contributing significantly to the growth of the Data Classification Software market. As enterprises migrate data to cloud environments and embrace hybrid work models, the complexity of data management increases. Data classification software provides the necessary visibility and control over data assets, regardless of where they reside. This empowers organizations to enforce consistent data protection policies across on-premises and cloud infrastructures. Additionally, the integration of artificial intelligence and machine learning technologies into data classification solutions is enhancing their accuracy and scalability, making them indispensable tools in the modern data security arsenal.

From a regional perspective, North America continues to dominate the Data Classification Software market, accounting for the largest share in 2024, driven by the presence of major technology vendors, early adoption of advanced security solutions, and stringent regulatory frameworks. Europe follows closely, with strong demand fueled by GDPR compliance requirements. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, increasing cyber threats, and evolving regulatory standards. Latin America and the Middle East & Africa are also showing steady adoption, albeit at a slower pace, as organizations in these regions gradually recognize the importance of robust data governance and security measures.

Component Analysis

The Data Classification Software market is segmented by component into software and services, each playing a pivotal role in the overall value proposition. The software segment encompasses standalone data classification platforms and integrated solutions that offer automated data discovery, classification, and policy enforcement. These platforms are increasingly leveraging artificial intell

Data Classification for Public Sector Market Outlook

According to our latest research, the global Data Classification for Public Sector market size in 2024 stands at USD 1.82 billion, reflecting the sector's growing emphasis on data security and regulatory compliance. The market is experiencing a robust compound annual growth rate (CAGR) of 18.7%, and is forecasted to reach USD 9.63 billion by 2033. This expansion is primarily driven by increasing digital transformation initiatives across public sector organizations, rising cybersecurity threats, and the mounting need for effective data governance frameworks to handle sensitive public data.

Several key growth factors are propelling the Data Classification for Public Sector market forward. Firstly, the surge in digitalization within government agencies and public sector entities has significantly amplified the volume of data generated and stored. With this increase in data comes the heightened risk of data breaches and unauthorized access, making robust data classification solutions essential. Public sector organizations are increasingly adopting advanced data classification tools to segment, categorize, and secure sensitive information, thereby minimizing the risk of data leaks and ensuring compliance with evolving data privacy regulations. The integration of artificial intelligence and machine learning within these solutions is further enhancing their efficacy, enabling real-time data monitoring and adaptive classification protocols.

Secondly, the regulatory landscape for the public sector is becoming increasingly stringent, with governments worldwide enacting comprehensive data protection laws and compliance standards. Regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar legislations in Asia Pacific and Latin America are compelling public sector organizations to implement robust data classification frameworks. These regulatory mandates not only require organizations to identify and protect sensitive data but also demand transparent data handling practices and regular audits. As a result, compliance management has emerged as a critical application area within the market, driving further investments in data classification technologies.

Another significant growth driver is the rising sophistication of cyber threats targeting public sector entities. Government agencies, law enforcement bodies, and public healthcare institutions are increasingly becoming targets for cybercriminals seeking to exploit vulnerabilities in data security. The adoption of data classification solutions enables these organizations to enforce granular access controls, monitor data usage, and detect anomalous activities in real-time. Additionally, the growing trend of remote work and cloud adoption in the public sector has necessitated enhanced data governance measures, further fueling the demand for data classification tools that can operate seamlessly across hybrid and multi-cloud environments.

From a regional perspective, North America currently dominates the Data Classification for Public Sector market, accounting for the largest revenue share in 2024. This dominance is attributed to the region's advanced technological infrastructure, stringent regulatory environment, and high awareness of data security best practices among public sector organizations. Europe follows closely, driven by robust data protection regulations and significant investments in digital government initiatives. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digital transformation in emerging economies, increasing government focus on cybersecurity, and rising adoption of cloud-based data classification solutions. Latin America and the Middle East & Africa are also experiencing steady growth, albeit at a relatively slower pace, as governments in these regions ramp up their efforts to modernize public sector IT infrastructure and enhance data security.

Component Analysis

The

Unstructured Data Classification Market Outlook

According to our latest research, the global unstructured data classification market size reached USD 2.31 billion in 2024, reflecting robust demand across sectors. The market is anticipated to grow at a CAGR of 22.8% from 2025 to 2033, with the market size projected to reach USD 17.3 billion by 2033. This remarkable growth is primarily driven by the exponential increase in unstructured data generation, alongside heightened requirements for data security, compliance, and intelligent information management solutions.

The primary growth driver for the unstructured data classification market is the rapid proliferation of data from diverse sources such as emails, social media, IoT devices, and multimedia content. Organizations globally are witnessing a data deluge, with over 80% of enterprise data estimated to be unstructured. This surge has created an urgent need for advanced classification solutions that can efficiently process, categorize, and extract actionable insights from vast volumes of data. Furthermore, the integration of artificial intelligence and machine learning algorithms has significantly enhanced the accuracy and scalability of unstructured data classification, making these solutions indispensable for modern enterprises seeking to optimize operations and extract value from their data assets.

Another significant growth factor is the evolving regulatory landscape that mandates stringent data governance and compliance. With regulations like GDPR, CCPA, and industry-specific standards, businesses are compelled to implement robust data classification frameworks to ensure sensitive information is properly identified, protected, and managed. This has led to increased investments in unstructured data classification solutions, particularly in highly regulated industries such as BFSI, healthcare, and government. Additionally, the rising threat of data breaches and cyberattacks has heightened the focus on data security, further fueling the adoption of classification tools that can proactively identify and safeguard critical information.

The digital transformation wave sweeping across industries is also propelling the market forward. Enterprises are increasingly adopting cloud-based platforms, remote work models, and digital collaboration tools, all of which contribute to the exponential growth of unstructured data. As organizations strive for improved operational efficiency and agility, the demand for scalable and automated data classification solutions is set to escalate. Additionally, the emergence of big data analytics and the growing focus on deriving business intelligence from unstructured sources are expected to provide significant impetus to market expansion over the forecast period.

Regionally, North America continues to dominate the unstructured data classification market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the presence of major technology providers, advanced IT infrastructure, and high regulatory awareness. However, Asia Pacific is expected to witness the fastest growth rate, driven by rapid digitalization, increasing cloud adoption, and expanding investments in data security initiatives. Europe also holds a substantial market share, bolstered by stringent data privacy regulations and a mature enterprise landscape. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing awareness and adoption of data management solutions.

Component Analysis

The unstructured data classification market by component is segmented into software and services. Software solutions constitute the backbone of this market, offering advanced tools for automated data discovery, classification, and management. The software segment has seen significant innovation, with vendors integrating AI, NLP, and deep learning technologies to improve the accuracy and efficiency of data classification

The Global Industry Classification Standard (GICS) is an industry taxonomy developed in 1999 by MSCI and Standard & Poor's (S&P) for use by the global financial community. The GICS structure consists of

• 11 sectors
• 25 industry groups,
• 74 industries
• 163 sub-industries into which S&P has categorized all major public companies.

The system is similar to ICB (Industry Classification Benchmark), a classification structure maintained by FTSE Group.

GICS is used as a basis for S&P and MSCI financial market indexes in which each company is assigned to a sub-industry, and to an industry, industry group, and sector, by its principal business activity.

"GICS" is a registered trademark of McGraw Hill Financial and MSCI Inc.

The GICS schema follows this hierarchy: - Sector - Industry Group - Industry - Sub-industry

That is, a sector is composed by industry groups, which are composed by industries which are composed by sub-industries.

Each item in the hierarchy has an id. Each ids are prefixed by the id of the parent in the hierarchy and generally the number of the ids are increased by 5 or 10. For example the Sector Industrials has the id 20, the Industry group Capital Goods has the id is prefixed by that 20, resulting in 2010.

Dataset

The dataset is composed by CSV files (currently 2 files). Each representing a different version of the GICS classification.

For each file the columns are:

• SectorId (2 digits)
• Sector (string)
• IndustryGroupId (4 digits)
• IndustryGroup (string)
• IndustryId (7 digits)
• Industry (string)
• SubIndustryId (10 digits)
• SubIndustry (string)
• SubIndustryDescription (string)

References

• https://en.wikipedia.org/wiki/Global_Industry_Classification_Standard
• https://www.msci.com/our-solutions/indexes/gics

Get the standard geographical classifications used by Statistics Canada for the Census of Population and Census of Agriculture in Ontario. Standard geographical classifications are used to classify geographic areas in Ontario.

The data identifies:

• census agricultural regions
• census divisions
• census consolidated subdivisions
• census subdivisions

Sediment Profile Image (SPI) Analysis and Coastal/Marine Ecological Classification Standard (CMECS) Classification Data used to construct benthic habitat maps for FIIS. Sediment profile imagery (SPI) provides an in-situ perspective of the seafloor and associated characteristics. Specifically, the camera takes a profile photograph of the sediment-water interface, which offers information about the biological and environmental attributes of the surface of the seafloor, the substrate just below the seafloor, and the overlying water column. SPI imagery has been used as the basis for or to complement ecological studies for several decades (for further reading, refer to Germano et al., 2011; Solan et al., 2003; Germano et al., 1989). For this study, SPI images were collected to corroborate and complement the acoustic and grab sample data. Images were taken at each grab sample site and also along a series of planned transects designed to cross boundaries identified in the sidescan mosaics. All deployments of the camera were done in triplicate, resulting in six images per site (one deployment of the camera captures two images ten seconds apart). The images were processed and analyzed in Adobe Photoshop CS3. Color and contrast adjustments were applied to enhance the images for detection of features. Geological and biological features were identified and described through expert interpretation of the images, including relative grain size, bedforms, biogenic features, and presence of seagrass and organisms (identified to lowest taxonomic level). Furthermore, consistent settings were applied when taking penetration and apparent redox potential discontinuity (aRPD) measurements. The aRPD value indicates the depth at which the sediment transitions from being oxidized to reduced (Gerwing et al., 2017).

The Cat and Dog Classification dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or a cat. This dataset is provided as a subset of photos from a much larger dataset of approximately 25 thousands.

The dataset contains 24,998 images, split into 12,499 Cat images and 12,499 Dog images. The training images are divided equally between cat and dog images, while the test images are not labeled. This allows users to evaluate their models on unseen data.

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The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.

The Coastal and Marine Ecological Classification Standard (CMECS) Catalog is the authoritative collection of ecological units (terms + definitions) and unit relationships (the CMECS classification framework).

The CMECS Catalog is the complete representation of the CMECS classification. It contains all units that are or have been members of the CMECS classification throughout its lifecycle, as well as various annotations that provide metadata for each unit that enable Findability, Accessibility, Interoperability, and Reuse (FAIR, https://www.go-fair.org/fair-principles/). The CMECS Catalog (cmecs.owl) file is stored and managed in a Git repository; authoritative versions are publicly released via the NOAA-OCM/cmecs GitHub as changes are made. Version releases also include the CMECS Catalog in CSV and XLSX formats. A browsable text output of the CMECS Catalog ecological units and implementation guidance, the CMECS Thesaurus, is also available in PDF and MD formats.

This release includes changes to the Substrate Component Unit Codes and fixes to Biotic Component typographical errors. Details are available on the CMECS GitHub v1.1.1 Release Page.

Questions? Please contact the CMECS Implementation Group at ocm.cmecs-ig@noaa.gov

For more information about the CMECS Catalog, see the https://github.com/NOAA-OCM/cmecs/wiki.

For more information about CMECS, including technical guidance and classification examples, visit the NOAA Integrated Ocean and Coastal Mapping (IOCM) team's CMECS webpage.

CMECS follows a Dynamic Standard Process to review and adopt changes that are proposed by the CMECS user community when necessary. More information about CMECS maintenance can be found on the NOAA National Centers for Environmental Information (NCEI) CMECS webpage under the Vocabulary Maintenance section, along with instructions for proposing revisions to CMECS and a form for submitting proposals.

The data presented here were used to produce the following paper:

Archibald, Twine, Mthabini, Stevens (2021) Browsing is a strong filter for savanna tree seedlings in their first growing season. J. Ecology.

The project under which these data were collected is: Mechanisms Controlling Species Limits in a Changing World. NRF/SASSCAL Grant number 118588

For information on the data or analysis please contact Sally Archibald: sally.archibald@wits.ac.za

Description of file(s):

File 1: cleanedData_forAnalysis.csv (required to run the R code: "finalAnalysis_PostClipResponses_Feb2021_requires_cleanData_forAnalysis_.R"

The data represent monthly survival and growth data for ~740 seedlings from 10 species under various levels of clipping.

The data consist of one .csv file with the following column names:

treatment Clipping treatment (1 - 5 months clip plus control unclipped) plot_rep One of three randomised plots per treatment matrix_no Where in the plot the individual was placed species_code First three letters of the genus name, and first three letters of the species name uniquely identifies the species species Full species name sample_period Classification of sampling period into time since clip. status Alive or Dead standing.height Vertical height above ground (in mm) height.mm Length of the longest branch (in mm) total.branch.length Total length of all the branches (in mm) stemdiam.mm Basal stem diameter (in mm) maxSpineLength.mm Length of the longest spine postclipStemNo Number of resprouting stems (only recorded AFTER clipping) date.clipped date.clipped date.measured date.measured date.germinated date.germinated Age.of.plant Date measured - Date germinated newtreat Treatment as a numeric variable, with 8 being the control plot (for plotting purposes)

File 2: Herbivory_SurvivalEndofSeason_march2017.csv (required to run the R code: "FinalAnalysisResultsSurvival_requires_Herbivory_SurvivalEndofSeason_march2017.R"

The data consist of one .csv file with the following column names:

treatment Clipping treatment (1 - 5 months clip plus control unclipped) plot_rep One of three randomised plots per treatment matrix_no Where in the plot the individual was placed species_code First three letters of the genus name, and first three letters of the species name uniquely identifies the species species Full species name sample_period Classification of sampling period into time since clip. status Alive or Dead standing.height Vertical height above ground (in mm) height.mm Length of the longest branch (in mm) total.branch.length Total length of all the branches (in mm) stemdiam.mm Basal stem diameter (in mm) maxSpineLength.mm Length of the longest spine postclipStemNo Number of resprouting stems (only recorded AFTER clipping) date.clipped date.clipped date.measured date.measured date.germinated date.germinated Age.of.plant Date measured - Date germinated newtreat Treatment as a numeric variable, with 8 being the control plot (for plotting purposes) genus Genus MAR Mean Annual Rainfall for that Species distribution (mm) rainclass High/medium/low

File 3: allModelParameters_byAge.csv (required to run the R code: "FinalModelSeedlingSurvival_June2021_.R"

Consists of a .csv file with the following column headings

Age.of.plant Age in days species_code Species pred_SD_mm Predicted stem diameter in mm pred_SD_up top 75th quantile of stem diameter in mm pred_SD_low bottom 25th quantile of stem diameter in mm treatdate date when clipped pred_surv Predicted survival probability pred_surv_low Predicted 25th quantile survival probability pred_surv_high Predicted 75th quantile survival probability species_code species code Bite.probability Daily probability of being eaten max_bite_diam_duiker_mm Maximum bite diameter of a duiker for this species duiker_sd standard deviation of bite diameter for a duiker for this species max_bite_diameter_kudu_mm Maximum bite diameer of a kudu for this species kudu_sd standard deviation of bite diameter for a kudu for this species mean_bite_diam_duiker_mm mean etc duiker_mean_sd standard devaition etc mean_bite_diameter_kudu_mm mean etc kudu_mean_sd standard deviation etc genus genus rainclass low/med/high

File 4: EatProbParameters_June2020.csv (required to run the R code: "FinalModelSeedlingSurvival_June2021_.R"

Consists of a .csv file with the following column headings

shtspec species name species_code species code genus genus rainclass low/medium/high seed mass mass of seed (g per 1000seeds)
Surv_intercept coefficient of the model predicting survival from age of clip for this species Surv_slope coefficient of the model predicting survival from age of clip for this species GR_intercept coefficient of the model predicting stem diameter from seedling age for this species GR_slope coefficient of the model predicting stem diameter from seedling age for this species species_code species code max_bite_diam_duiker_mm Maximum bite diameter of a duiker for this species duiker_sd standard deviation of bite diameter for a duiker for this species max_bite_diameter_kudu_mm Maximum bite diameer of a kudu for this species kudu_sd standard deviation of bite diameter for a kudu for this species mean_bite_diam_duiker_mm mean etc duiker_mean_sd standard devaition etc mean_bite_diameter_kudu_mm mean etc kudu_mean_sd standard deviation etc AgeAtEscape_duiker[t] age of plant when its stem diameter is larger than a mean duiker bite AgeAtEscape_duiker_min[t] age of plant when its stem diameter is larger than a min duiker bite AgeAtEscape_duiker_max[t] age of plant when its stem diameter is larger than a max duiker bite AgeAtEscape_kudu[t] age of plant when its stem diameter is larger than a mean kudu bite AgeAtEscape_kudu_min[t] age of plant when its stem diameter is larger than a min kudu bite AgeAtEscape_kudu_max[t] age of plant when its stem diameter is larger than a max kudu bite

The 2018 Standard Occupational Classification (SOC) system is a federal statistical standard used by federal agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of 867 detailed occupations according to their occupational definition. To facilitate classification, detailed occupations are combined to form 459 broad occupations, 98 minor groups, and 23 major groups. Detailed occupations in the SOC with similar job duties, and in some cases skills, education, and/or training, are grouped together. For additional information visit: https://www.bls.gov/soc/