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The global clinical genomic data analysis market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 6.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 17.2% during the forecast period. This market growth is driven by the increasing adoption of genomic sequencing technologies, advancements in bioinformatics, and the rising prevalence of chronic diseases that necessitate personalized medicine and targeted therapies.
A major growth factor for the clinical genomic data analysis market is the exponential increase in the volume of genomic data being generated. With the cost of sequencing dropping and the speed of sequencing increasing, more genomic data is being produced than ever before. This abundance of data requires sophisticated analysis tools and software to interpret and derive meaningful insights, driving the demand for advanced genomic data analysis solutions. Additionally, the integration of artificial intelligence and machine learning algorithms in genomics is further enhancing the capabilities of these analysis tools, enabling more accurate and faster data interpretation.
Another significant factor contributing to market growth is the rising incidence of genetic disorders and cancers, which necessitates comprehensive genomic analysis for accurate diagnosis and personalized treatment plans. Personalized medicine, which tailors medical treatment to the individual characteristics of each patient, relies heavily on the insights gained from genomic data analysis. As the understanding of the genetic basis of diseases deepens, the demand for clinical genomic data analysis is expected to surge, further propelling market growth.
The integration of NGS Informatics and Clinical Genomics is revolutionizing the field of personalized medicine. By leveraging next-generation sequencing (NGS) technologies, researchers and clinicians can now analyze vast amounts of genomic data with unprecedented speed and accuracy. This integration enables the identification of genetic variants that may contribute to disease, allowing for more precise diagnosis and the development of targeted therapies. As the capabilities of NGS technologies continue to expand, the role of informatics in managing and interpreting this data becomes increasingly critical. The seamless integration of NGS Informatics and Clinical Genomics is paving the way for more effective and personalized healthcare solutions, ultimately improving patient outcomes.
Government initiatives and funding in genomics research also play a crucial role in the expansion of the clinical genomic data analysis market. Many governments around the world are investing heavily in genomic research projects and infrastructure to advance medical research and improve public health outcomes. For instance, initiatives like the 100,000 Genomes Project in the UK and the All of Us Research Program in the US underscore the importance of genomics in understanding human health and disease, thereby boosting the demand for genomic data analysis tools and services.
Regional outlook reveals significant growth opportunities in emerging markets, particularly in the Asia Pacific region. Countries like China, India, and Japan are witnessing rapid advancements in healthcare infrastructure and increasing investments in genomics research. Additionally, favorable government policies and the presence of a large patient pool make this region a lucrative market for clinical genomic data analysis. North America continues to dominate the market due to high healthcare spending, advanced research facilities, and the early adoption of new technologies. Europe also shows steady growth with significant contributions from countries like the UK, Germany, and France.
The component segment of the clinical genomic data analysis market is divided into software and services. The software segment encompasses various bioinformatics tools and platforms used for genomic data analysis. These tools are essential for the effective management, storage, and interpretation of the massive amounts of genomic data generated. The growing complexity of genomic data necessitates the use of robust software solutions that can handle large datasets and provide accurate insights. As a result, the software segment is expected to witness significant growth during the forecast period.
The services segment includes
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There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.
National Center for Health Statistics (NCHS) population health survey data have been linked to VA administrative data containing information on military service history and VA benefit program utilization. The linked data can provide information on the health status and access to health care for VA program beneficiaries. In addition, researchers can compare the health of Veterans within and outside the VA health care system and compare Veterans to non-Veterans in the civilian non-institutionalized U.S. population. Due to confidentiality requirements, the Restricted-use NCHS-VA Linked Data Files are accessible only through the NCHS Research Data Center (RDC) Network. All interested researchers must submit a research proposal to the RDC. Please see the NCHS RDC website (https://www.cdc.gov/rdc/index.htm) for instructions on submitting a proposal.
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Values correspond to mean (standard deviation) or N (%), All of Us Research Program, 2017–2019.
The Health Information Technology for Economic and Clinical Health (HITECH) Act was passed as part of the American Recovery and Reinvestment Act (ARRA) to invest in the U.S. health IT infrastructure. The Office of the National Coordinator for Health IT (ONC) received over $2 billion of these HITECH funds, which was granted to health and community organizations across the U.S. This data set provides the full list of ONC's HITECH Act grantees. The data encompasses the five ONC HITECH Act programs: the Beacon Communities Program, the Health IT Regional Extension Centers Program, the Health IT Workforce Programs, the State Health Information Exchange Program, and the Strategic Health IT Advanced Research Projects (SHARP) Program. This data set includes geographic, federal funding, and grantee organization data. The data can be linked to other open data sets on the Health IT Dashboard and other sources.
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The Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and depletion of timber on the Nation's forest land. Before 1999, all inventories were conducted on a periodic basis. The passage of the 1998 Farm Bill requires FIA to collect data annually on plots within each State. This kind of up-to-date information is essential to frame realistic forest policies and programs. Summary reports for individual States are published but the Forest Service also provides data collected in each inventory to those interested in further analysis. Data is distributed via the FIA DataMart in a standard format. This standard format, referred to as the Forest Inventory and Analysis Database (FIADB) structure, was developed to provide users with as much data as possible in a consistent manner among States. A number of inventories conducted prior to the implementation of the annual inventory are available in the FIADB. However, various data attributes may be empty or the items may have been collected or computed differently. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Links to field collection manuals and the FIADB user's manual are provided in the FIA DataMart.
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NOAA’s Deep-Sea Coral Research and Technology Program (DSC-RTP) has compiled a national database of the known locations of deep-sea corals and sponges in U.S. territorial waters and beyond. The database is comprehensive, standardized, quality controlled, and networked to outside resources. The database schema accommodates both linear (trawls, transects) and point (samples, observations) data. The structure of the database is tailored to occurrence records of all the azooxanthellate corals, a subset of all corals, and all sponge species. Fish records are also included when annotated along with coral and sponge occurrences. Records shallower than 50 m are generally excluded in order to focus on predominantly deep-water species – the mandate of the DSC-RTP. The intention is to limit the overlap with light-dependent (and mostly shallow-water) corals. Query, visualize, and download data in its native format by visiting our map and data portal:
Deep-Sea Corals Map Portal ERDDAP Data Access Form NOAA Deep-Sea Coral Data
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Context
The dataset tabulates the Show Low population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Show Low. The dataset can be utilized to understand the population distribution of Show Low by age. For example, using this dataset, we can identify the largest age group in Show Low.
Key observations
The largest age group in Show Low, AZ was for the group of age 70-74 years with a population of 1,011 (8.70%), according to the 2021 American Community Survey. At the same time, the smallest age group in Show Low, AZ was the 85+ years with a population of 179 (1.54%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Show Low Population by Age. You can refer the same here
The dataset contains contact and description information for local supply chain organizations, offshore wind developers, and original equipment manufacturers that provide goods and services to support New York State’s offshore wind industry. To request placement in this database, or to update your company’s information, please visit NYSERDA’s Supply Chain Database webpage at https://www.nyserda.ny.gov/All-Programs/Offshore-Wind/Focus-Areas/Supply-Chain-Economic-Development/Supply-Chain-Database to submit a request form.
How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.
The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.
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This report describes the results of a nationwide survey on tropical cyclones in the United States. The 2021 Tropical Cyclone Survey (TC21) was designed and administered by the Institute for Public Policy Research and Analysis (IPPRA) at the University of Oklahoma. It was fielded June 22 – July 1, 2021, using an online questionnaire that was completed by 1,550 U.S. adults (age 18+) that were recruited from an Internet panel that matches the characteristics of the U.S. population as estimated in the U.S. Census. The TC20 survey was designed to establish baseline measures of the extent to which U.S. adults receive, understand, and respond to tropical cyclone forecasts and warnings as well as trust in the National Weather Service (NWS), extreme weather and climate risk perceptions, risk literacy, interpretations of probabilistic language, and weather preparedness. The TC21 survey refined these measures and included a few questions about information preferences along the event timeline. This report briefly describes the methodology, survey data collection, data weighting, and a reproduction of the survey instrument with weighted means and frequencies for the questions that elicited numeric responses. The University of Oklahoma provided funding for all data collection. NOAA’s Weather Program Office through the U.S. Weather Research Program provided funding for survey design and data analysis.
The northeastern North Carolina coastal system, from False Cape, Virginia, to Cape Lookout, North Carolina, has been studied by a cooperative research program that mapped the Quaternary geologic framework of the estuaries, barrier islands, and inner continental shelf. This information provides a basis to understand the linkage between geologic framework, physical processes, and coastal evolution at time scales from storm events to millennia. The study area attracts significant tourism to its parks and beaches, contains a number of coastal communities, and supports a local fishing industry, all of which are impacted by coastal change. Knowledge derived from this research program can be used to mitigate hazards and facilitate effective management of this dynamic coastal system. This regional mapping project produced spatial datasets of high-resolution geophysical (bathymetry, backscatter intensity, and seismic reflection) and sedimentary (core and grab-sample) data. The high-resolution geophysical data were collected during numerous surveys within the back-barrier estuarine system, along the barrier island complex, in the nearshore, and along the inner continental shelf. Sediment cores were taken on the mainland and along the barrier islands, and both cores and grab samples were taken on the inner shelf. Data collection was a collaborative effort between the U.S. Geological Survey (USGS) and several other institutions including East Carolina University (ECU), the North Carolina Geological Survey, and the Virginia Institute of Marine Science (VIMS). The high-resolution geophysical data of the inner continental shelf were collected during six separate surveys conducted between 1999 and 2004 (four USGS surveys north of Cape Hatteras: 1999-045-FA, 2001-005-FA, 2002-012-FA, 2002-013-FA, and two USGS surveys south of Cape Hatteras: 2003-003-FA and 2004-003-FA) and cover more than 2600 square kilometers of the inner shelf. Single-beam bathymetry data were collected north of Cape Hatteras in 1999 using a Furuno fathometer. Swath bathymetry data were collected on all other inner shelf surveys using a SEA, Ltd. SwathPLUS 234-kHz bathymetric sonar. Chirp seismic data as well as sidescan-sonar data were collected with a Teledyne Benthos (Datasonics) SIS-1000 north of Cape Hatteras along with boomer seismic reflection data (cruises 1999-045-FA, 2001-005-FA, 2002-012-FA and 2002-013-FA). An Edgetech 512i was used to collect chirp seismic data south of Cape Hatteras (cruises 2003-003-FA and 2004-003-FA) along with a Klein 3000 sidescan-sonar system. Sediment samples were collected with a Van Veen grab sampler during four of the USGS surveys (1999-045-FA, 2001-005-FA, 2002-013-FA, and 2004-003-FA). Additional sediment core data along the inner shelf are provided from previously published studies. A cooperative study, between the North Carolina Geological Survey and the Minerals Management Service (MMS cores), collected vibracores along the inner continental shelf offshore of Nags Head, Kill Devils Hills and Kitty Hawk, North Carolina in 1996. The U.S. Army Corps of Engineers collected vibracores along the inner shelf offshore of Dare County in August 1995 (NDC cores) and July-August 1995 (SNL cores). These cores are curated by the North Carolina Geological Survey and were used as part of the ground validation process in this study. Nearshore geophysical and core data were collected by the Virginia Institute of Marine Science. The nearshore is defined here as the region between the 10-m isobath and the shoreline. High-resolution bathymetry, backscatter intensity, and chirp seismic data were collected between June 2002 and May 2004. Vibracore samples were collected in May and July 2005. Shallow subsurface geophysical data were acquired along the Outer Banks barrier islands using a ground-penetrating radar (GPR) system. Data were collected by East Carolina University from 2002 to 2005. Rotasonic cores (OBX cores) from five drilling operations were collected from 2002 to 2006 by the North Carolina Geological Survey as part of the cooperative study with the USGS. These cores are distributed throughout the Outer Banks as well as the mainland. The USGS collected seismic data for the Quaternary section within the Albemarle-Pamlico estuarine system between 2001 and 2004 during six surveys (2001-013-FA, 2002-015-FA, 2003-005-FA, 2003-042-FA, 2004-005-FA, and 2004-006-FA). These surveys used Geopulse Boomer and Knudsen Engineering Limited (KEL) 320BR Chirp systems, except cruise 2003-042-FA, which used an Edgetech 424 Chirp and a boomer system. The study area includes Albemarle Sound and selected tributary estuaries such as the South, Pungo, Alligator, and Pasquotank Rivers; Pamlico Sound and trunk estuaries including the Neuse and Pamlico Rivers; and back-barrier sounds including Currituck, Croatan, Roanoke, Core, and Bogue.
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DataIntelo recently published a report, titled Global User Experience (UX) Research Software Market Insights, Forecast to 2025. The research includes collation of data that is gathered using primary and secondary research methodologies. The research is conducted by professionals who have remarkable expertise in the field. The report elaborates on all the aspect of the market for a comprehensive understanding of the market dynamics. The market is divided into various segments and all the segments follow a similar format for a detailed explanation of the market.
In report covers both sales and revenue and studies the segments pertaining to application, products, services, and regions. To assess the market’s future the research report also discusses the competitive landscape present in the global User Experience (UX) Research Software market.
In 2018 the global User Experience (UX) Research Software market size was 130 million US$ and will reach 417.1 million US$ by 2025, with a CAGR of 18.2% during the forecast period.
Global User Experience (UX) Research Software Market: Scope of the Market
User Experience (UX) research is the process of discovering the behaviors, motivations and needs of your customers through observation, task analysis, and other types of user feedback.
The report first uses historic data from different companies. The data collected is used to analyses the growth of industries in the past years. It includes data from the year 2014 to the year 2019. The forecast data provides the reader with an understating of the future of the market. The same data is used to predict the expectation of the companies and how they are expected to evolve in the coming years. The research provides historical as well as estimated data from the year 2019 to 2025. The details in the report give a brief overview of the market by examining its historical data, the current data, and forecast data to understand the growth of the market.
Global User Experience (UX) Research Software Market: Segment Analysis
The report also outlines the sales and revenue generated by the global User Experience (UX) Research Software market. It is broken down in many segments, such as regional, country level, by type, application, and others. This enables a granular view of the market, focusing on the government policies that could change the dynamics. It also assesses the research and development plans of the companies for better product innovation.
The report is based on research done specifically on consumer goods. The goods have bifurcated depending on their use and type. The type segment contains all the necessary information about the different forms and their scope in the global User Experience (UX) Research Software market. The application segment defines the uses of the product. It points out the various changes that these products have been through over the years and the innovation that players are bringing in. The focus of the report on the consumer goods aspect helps in explaining changing consumer behavior that will impact the global User Experience (UX) Research Software market.
The main consumer market is located in developed countries. North America is the largest consumption region, with the total market share of 44.69% in 2018, and USA accounts most of the North America market, with the market share of 88.08%, and account the total market share of 39.36% in 2018. Followed by Europe, accounting for 32.70%. In the coming years there is an increasing demand for User Experience (UX) Research Software in the regions of APAC and Europe.
Global User Experience (UX) Research Software Market: Regional Segment Analysis
Based on region, the global User Experience (UX) Research Software market is segmented into North America and Europe.. Asia Pacific has a large population, which makes its market potential a significant one. It is the fastest-growing and most lucrative region in the global economy. This chapter specifically explains the impact of population on the global User Experience (UX) Research Software market. Research views it through a regional lens, giving the readers a microscopic understanding of the changes to prepare for.
The report covers different aspects of the market from a consumer goods point of view. It aims to be a guiding hand to interested readers for making profitable business decisions.
The following players are covered in this report:
UserTesting
Qualtrics
Hotjar
Lookback
UserZoom
Validately
Userlytics
UsabilityHub
TryMyUI
Woopra
Usabilla
TechSmith
20 | 20
User Interviews
User Experience (UX) Research Software Breakdown Data by Type
Cloud Based
On-Premises
User Experience (UX) Rese
IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
The program described in the Oil Research Program Implementation Plan is a self-contained core research program whose goal is to maximize the economic producibility of the domestic oil resource. This plan was developed in support of the Hydrocarbon Geoscience Research Strategy, and in parallel with the Natural Gas Program Implementation Plan. While the objective, strategy, and management tactics of the program are described in some detail this Implementation Plan is not complete. It is based on the best analysis that is now available and will be revised as new analysis directs. Certain aspects are recognized to be preliminary in nature, such as the work in heavy oil, where the basic analysis required to identify the urgent issues affecting this resource is just now being initiated. Only about 40% of the known US resource has undergone the detailed assessment, classification, and analysis which is required for program planning. Classification and analysis of the full US oil resource is expected to be complete by October 1992. Work may be proposed in at least some of the priority classes as soon as they are identified, but before all of the classification work has been completed. Finally, the field RD D activities and supporting RD D projects are purposely nonspecific in this plan, because they must be detailed in response to the problems that will be identified as work begins with the states and industry on specific reservoir classes. The FY 1991 budget requests funds to begin work on the first two high priority reservoir classes to be identified as a result of the current National reservoir classification effort. This Implementation Plan will be revised annually, and the currently underspecified elements will become substantially more explicit as the takes shape. 43 figs., 20 tabs.
The northeastern North Carolina coastal system, from False Cape, Virginia, to Cape Lookout, North Carolina, has been studied by a cooperative research program that mapped the Quaternary geologic framework of the estuaries, barrier islands, and inner continental shelf. This information provides a basis to understand the linkage between geologic framework, physical processes, and coastal evolution at time scales from storm events to millennia. The study area attracts significant tourism to its parks and beaches, contains a number of coastal communities, and supports a local fishing industry, all of which are impacted by coastal change. Knowledge derived from this research program can be used to mitigate hazards and facilitate effective management of this dynamic coastal system. This regional mapping project produced spatial datasets of high-resolution geophysical (bathymetry, backscatter intensity, and seismic reflection) and sedimentary (core and grab-sample) data. The high-resolution geophysical data were collected during numerous surveys within the back-barrier estuarine system, along the barrier island complex, in the nearshore, and along the inner continental shelf. Sediment cores were taken on the mainland and along the barrier islands, and both cores and grab samples were taken on the inner shelf. Data collection was a collaborative effort between the U.S. Geological Survey (USGS) and several other institutions including East Carolina University (ECU), the North Carolina Geological Survey, and the Virginia Institute of Marine Science (VIMS). The high-resolution geophysical data of the inner continental shelf were collected during six separate surveys conducted between 1999 and 2004 (four USGS surveys north of Cape Hatteras: 1999-045-FA, 2001-005-FA, 2002-012-FA, 2002-013-FA, and two USGS surveys south of Cape Hatteras: 2003-003-FA and 2004-003-FA) and cover more than 2600 square kilometers of the inner shelf. Single-beam bathymetry data were collected north of Cape Hatteras in 1999 using a Furuno fathometer. Swath bathymetry data were collected on all other inner shelf surveys using a SEA, Ltd. SwathPLUS 234-kHz bathymetric sonar. Chirp seismic data as well as sidescan-sonar data were collected with a Teledyne Benthos (Datasonics) SIS-1000 north of Cape Hatteras along with boomer seismic reflection data (cruises 1999-045-FA, 2001-005-FA, 2002-012-FA and 2002-013-FA). An Edgetech 512i was used to collect chirp seismic data south of Cape Hatteras (cruises 2003-003-FA and 2004-003-FA) along with a Klein 3000 sidescan-sonar system. Sediment samples were collected with a Van Veen grab sampler during four of the USGS surveys (1999-045-FA, 2001-005-FA, 2002-013-FA, and 2004-003-FA). Additional sediment core data along the inner shelf are provided from previously published studies. A cooperative study, between the North Carolina Geological Survey and the Minerals Management Service (MMS cores), collected vibracores along the inner continental shelf offshore of Nags Head, Kill Devils Hills and Kitty Hawk, North Carolina in 1996. The U.S. Army Corps of Engineers collected vibracores along the inner shelf offshore of Dare County in August 1995 (NDC cores) and July-August 1995 (SNL cores). These cores are curated by the North Carolina Geological Survey and were used as part of the ground validation process in this study. Nearshore geophysical and core data were collected by the Virginia Institute of Marine Science. The nearshore is defined here as the region between the 10-m isobath and the shoreline. High-resolution bathymetry, backscatter intensity, and chirp seismic data were collected between June 2002 and May 2004. Vibracore samples were collected in May and July 2005. Shallow subsurface geophysical data were acquired along the Outer Banks barrier islands using a ground-penetrating radar (GPR) system. Data were collected by East Carolina University from 2002 to 2005. Rotasonic cores (OBX cores) from five drilling operations were collected from 2002 to 2006 by the North Carolina Geological Survey as part of the cooperative study with the USGS. These cores are distributed throughout the Outer Banks as well as the mainland. The USGS collected seismic data for the Quaternary section within the Albemarle-Pamlico estuarine system between 2001 and 2004 during six surveys (2001-013-FA, 2002-015-FA, 2003-005-FA, 2003-042-FA, 2004-005-FA, and 2004-006-FA). These surveys used Geopulse Boomer and Knudsen Engineering Limited (KEL) 320BR Chirp systems, except cruise 2003-042-FA, which used an Edgetech 424 Chirp and a boomer system. The study area includes Albemarle Sound and selected tributary estuaries such as the South, Pungo, Alligator, and Pasquotank Rivers; Pamlico Sound and trunk estuaries including the Neuse and Pamlico Rivers; and back-barrier sounds including Currituck, Croatan, Roanoke, Core, and Bogue.
The Earth Observing System Data and Information System (EOSDIS) is a major core capability within NASA''s Earth Science Data Systems Program. EOSDIS ingests, processes, archives and distributes data from a large number of Earth observing satellites. EOSDIS consists of a set of processing facilities and Earth Science Data Centers distributed across the United States and serves hundreds of thousands of users around the world, providing hundreds of millions of data files each year covering many Earth science disciplines. In order to serve the needs of a broad and diverse community of users, NASA''s Earth Science Data Systems Program is comprised of both Core and Community data system elements. Core data system elements reflect NASA''s responsibility for managing Earth science satellite mission data characterized by the continuity of research, access, and usability. The core comprises all the hardware, software, physical infrastructure, and intellectual capital NASA recognizes as necessary for performing its tasks in Earth science data system management. Community data system elements are those pieces or capabilities developed and deployed largely outside of NASA core elements and are characterized by their evolvability and innovation. Successful applicable elements can be infused into the core, thereby creating a vibrant and flexible, continuously evolving infrastructure. NASA''s Earth Science program was established to use the advanced technology of NASA to understand and protect our home planet by using our view from space to study the Earth system and improve prediction of Earth system change. To meet this challenge, NASA promotes the full and open sharing of all data with the research and applications communities, private industry, academia, and the general public. NASA was the first agency in the US, and the first space agency in the world, to couple policy and adequate system functionality to provide full and open access in a timely manner - that is, with no period of exclusive access to mission scientists - and at no cost. NASA made this decision after listening to the user community, and with the background of the then newly-formed US Global Change Research Program, and the International Earth Observing System partnerships. Other US agencies and international space agencies have since adopted similar open-access policies and practices. Since the adoption of the Earth Science Data Policy adoption in 1991, NASA''s Earth Science Division has developed policy implementation, practices, and nomenclature that mission science teams use to comply with policy tenets. Data System Standards NASA''s Earth Science Data Systems Groups anticipate that effective adoption of standards will play an increasingly vital role in the success of future science data systems. The Earth Science Data Systems Standards Process Group (SPG), a board composed of Earth Science Data Systems stakeholders, directs the process for both identification of appropriate standards and subsequent adoption for use by the Earth Science Data Systems stakeholders.
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United States US: Civil GBARD: Current PPP: Economic Development Programmes data was reported at 17.437 USD bn in 2023. This records a decrease from the previous number of 19.276 USD bn for 2022. United States US: Civil GBARD: Current PPP: Economic Development Programmes data is updated yearly, averaging 6.320 USD bn from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 19.276 USD bn in 2022 and a record low of 4.267 USD bn in 1987. United States US: Civil GBARD: Current PPP: Economic Development Programmes data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Government Budgets for Research and Development: OECD Member: Annual.
For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.
Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.
U.S. R&D data are generally comparable, but there are some areas of underestimation:
Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.
The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.
Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.
Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.
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This dataset is updated more frequently and can be visualized on NCWQR's data portal.
If you have any questions, please contact Dr. Laura Johnson or Dr. Nathan Manning.
The National Center for Water Quality Research (NCWQR) is a research laboratory at Heidelberg University in Tiffin, Ohio, USA. Our primary research program is the Heidelberg Tributary Loading Program (HTLP), where we currently monitor water quality at 22 river locations throughout Ohio and Michigan, effectively covering ~half of the land area of Ohio. The goal of the program is to accurately measure the total amounts (loads) of pollutants exported from watersheds by rivers and streams. Thus these data are used to assess different sources (nonpoint vs point), forms, and timing of pollutant export from watersheds. The HTLP officially began with high-frequency monitoring for sediment and nutrients from the Sandusky and Maumee rivers in 1974, and has continually expanded since then.
Each station where samples are collected for water quality is paired with a US Geological Survey gage for quantifying discharge (http://waterdata.usgs.gov/usa/nwis/rt). Our stations cover a wide range of watershed areas upstream of the sampling point from 11.0 km2 for the unnamed tributary to Lost Creek to 19,215 km2 for the Muskingum River. These rivers also drain a variety of land uses, though a majority of the stations drain over 50% row-crop agriculture.
At most sampling stations, submersible pumps located on the stream bottom continuously pump water into sampling wells inside heated buildings where automatic samplers collect discrete samples (4 unrefrigerated samples/d at 6-h intervals, 1974–1987; 3 refrigerated samples/d at 8-h intervals, 1988-current). At weekly intervals the samples are returned to the NCWQR laboratories for analysis. When samples either have high turbidity from suspended solids or are collected during high flow conditions, all samples for each day are analyzed. As stream flows and/or turbidity decreases, analysis frequency shifts to one sample per day. At the River Raisin and Muskingum River, a cooperator collects a grab sample from a bridge at or near the USGS station approximately daily and all samples are analyzed. Each sample bottle contains sufficient volume to support analyses of total phosphorus (TP), dissolved reactive phosphorus (DRP), suspended solids (SS), total Kjeldahl nitrogen (TKN), ammonium-N (NH4), nitrate-N and nitrite-N (NO2+3), chloride, fluoride, and sulfate. Nitrate and nitrite are commonly added together when presented; henceforth we refer to the sum as nitrate.
Upon return to the laboratory, all water samples are analyzed within 72h for the nutrients listed below using standard EPA methods. For dissolved nutrients, samples are filtered through a 0.45 um membrane filter prior to analysis. We currently use a Seal AutoAnalyzer 3 for DRP, silica, NH4, TP, and TKN colorimetry, and a DIONEX Ion Chromatograph with AG18 and AS18 columns for anions. Prior to 2014, we used a Seal TRAACs for all colorimetry.
2017 Ohio EPA Project Study Plan and Quality Assurance Plan
Data quality control and data screening
The data provided in the River Data files have all been screened by NCWQR staff. The purpose of the screening is to remove outliers that staff deem likely to reflect sampling or analytical errors rather than outliers that reflect the real variability in stream chemistry. Often, in the screening process, the causes of the outlier values can be determined and appropriate corrective actions taken. These may involve correction of sample concentrations or deletion of those data points.
This micro-site contains data for approximately 126,000 water samples collected beginning in 1974. We cannot guarantee that each data point is free from sampling bias/error, analytical errors, or transcription errors. However, since its beginnings, the NCWQR has operated a substantial internal quality control program and has participated in numerous external quality control reviews and sample exchange programs. These programs have consistently demonstrated that data produced by the NCWQR is of high quality.
A note on detection limits and zero and negative concentrations
It is routine practice in analytical chemistry to determine method detection limits and/or limits of quantitation, below which analytical results are considered less reliable or unreliable. This is something that we also do as part of our standard procedures. Many laboratories, especially those associated with agencies such as the U.S. EPA, do not report individual values that are less than the detection limit, even if the analytical equipment returns such values. This is in part because as individual measurements they may not be considered valid under litigation.
The measured concentration consists of the true but unknown concentration plus random instrument error, which is usually small compared to the range of expected environmental values. In a sample for which the true concentration is very small, perhaps even essentially zero, it is possible to obtain an analytical result of 0 or even a small negative concentration. Results of this sort are often “censored” and replaced with the statement “
Censoring these low values creates a number of problems for data analysis. How do you take an average? If you leave out these numbers, you get a biased result because you did not toss out any other (higher) values. Even if you replace negative concentrations with 0, a bias ensues, because you’ve chopped off some portion of the lower end of the distribution of random instrument error.
For these reasons, we do not censor our data. Values of -9 and -1 are used as missing value codes, but all other negative and zero concentrations are actual, valid results. Negative concentrations make no physical sense, but they make analytical and statistical sense. Users should be aware of this, and if necessary make their own decisions about how to use these values. Particularly if log transformations are to be used, some decision on the part of the user will be required.
Analyte Detection Limits
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For more information, please visit https://ncwqr.org/
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The dataset contains results of a genome-wide association study of back pain. Two files contain association summary statistics for discovery GWAS based on the analysis of 350,000 white British individuals from the UK Biobank and meta-analysis GWAS based on the meta-analysis of the same 350,000 individuals and additional 103,862 individuals of European Ancestry from the UK biobank (total N = 453,862). The phenotype of back pain was defined by the answer provided by the UK biobank participants to the following question: "Pain type(s) experienced in last month". Those who reported “Back pain”, were considered as cases, all the rest were considered as controls. Individuals who did not reply or replied: "Prefer not to answer" or "Pain all over the body" were excluded. This dataset is also available for graphical exploration in the genomic context at http://gwasarchive.org.
The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.
When using downloaded data, please cite corresponding paper and this repository:
Insight into the genetic architecture of back pain and its risk factors from a study of 509,000 individuals. Freidin, Maxim; Tsepilov, Yakov; Palmer, Melody; Karssen, Lennart; Suri, Pradeep; Aulchenko, Yurii; Williams, Frances MK,# CHARGE Musculoskeletal Working Group. PAIN: February 06, 2019 - Volume Articles in Press - Issue - p doi: 10.1097/j.pain.0000000000001514
Maxim B Freidin, Yakov A Tsepilov, Melody Palmer, Lennart Karssen, CHARGE Musculoskeletal Working Group, Pradeep Suri, … Frances MK Williams. (2018). Genome-wide association summary statistics for back pain (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1319332
Funding:
This study was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736). The research has been conducted using the UK Biobank Resource (project # 18219).
The development of software implementing SMR/HEIDI test and database for GWAS results was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Program”.
Dr. Suri’s time for this work was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development Service. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.
Dr. Tsepilov’s time for this work was supported in part by the Russian Ministry of Science and Education under the 5-100 Excellence Program.
Column headers - discovery (350K)
CHR: chromosome
POS: position (GRCh37 build)
ID: SNP rsID
REF: reference allele (coded as "0")
ALT: effect allele (coded as "1")
CASE_ALLELE_CT: allele observation count in cases
CTRL_ALLELE_CT: allele observation count in controls
ALT_FREQ: effect allele frequency
MACH_R2: imputation quality
TEST: model of association test (additive)
OBS_CT: sample size
BETA: effect size of effect allele
SE: standard error of effect size
T_STAT: Z-value of effect allele
P: P-value of association (without GC correction)
MAF: minor allele frequency
Column headers - meta-analysis (450K)
MarkerName: SNP rsID
Allele1: effect allele (coded as "1")
Allele2: reference allele (coded as "0")
Freq1: effect allele frequency
FreqSE: standard error of effect allele frequency
Effect: effect size of effect allele
StdErr: standard error of effect size
P-value: P-value of association (without GC correction)
Direction: sign of effect in discovery and replication samples
n_total: Total sample size
CHR: chromosome
POS: position (GRCh37 build)
MACH_R2_discovery: imputation quality in discovery sample
The northeastern North Carolina coastal system, from False Cape, Virginia, to Cape Lookout, North Carolina, has been studied by a cooperative research program that mapped the Quaternary geologic framework of the estuaries, barrier islands, and inner continental shelf. This information provides a basis to understand the linkage between geologic framework, physical processes, and coastal evolution at time scales from storm events to millennia. The study area attracts significant tourism to its parks and beaches, contains a number of coastal communities, and supports a local fishing industry, all of which are impacted by coastal change. Knowledge derived from this research program can be used to mitigate hazards and facilitate effective management of this dynamic coastal system. This regional mapping project produced spatial datasets of high-resolution geophysical (bathymetry, backscatter intensity, and seismic reflection) and sedimentary (core and grab-sample) data. The high-resolution geophysical data were collected during numerous surveys within the back-barrier estuarine system, along the barrier island complex, in the nearshore, and along the inner continental shelf. Sediment cores were taken on the mainland and along the barrier islands, and both cores and grab samples were taken on the inner shelf. Data collection was a collaborative effort between the U.S. Geological Survey (USGS) and several other institutions including East Carolina University (ECU), the North Carolina Geological Survey, and the Virginia Institute of Marine Science (VIMS). The high-resolution geophysical data of the inner continental shelf were collected during six separate surveys conducted between 1999 and 2004 (four USGS surveys north of Cape Hatteras: 1999-045-FA, 2001-005-FA, 2002-012-FA, 2002-013-FA, and two USGS surveys south of Cape Hatteras: 2003-003-FA and 2004-003-FA) and cover more than 2600 square kilometers of the inner shelf. Single-beam bathymetry data were collected north of Cape Hatteras in 1999 using a Furuno fathometer. Swath bathymetry data were collected on all other inner shelf surveys using a SEA, Ltd. SwathPLUS 234-kHz bathymetric sonar. Chirp seismic data as well as sidescan-sonar data were collected with a Teledyne Benthos (Datasonics) SIS-1000 north of Cape Hatteras along with boomer seismic reflection data (cruises 1999-045-FA, 2001-005-FA, 2002-012-FA and 2002-013-FA). An Edgetech 512i was used to collect chirp seismic data south of Cape Hatteras (cruises 2003-003-FA and 2004-003-FA) along with a Klein 3000 sidescan-sonar system. Sediment samples were collected with a Van Veen grab sampler during four of the USGS surveys (1999-045-FA, 2001-005-FA, 2002-013-FA, and 2004-003-FA). Additional sediment core data along the inner shelf are provided from previously published studies. A cooperative study, between the North Carolina Geological Survey and the Minerals Management Service (MMS cores), collected vibracores along the inner continental shelf offshore of Nags Head, Kill Devils Hills and Kitty Hawk, North Carolina in 1996. The U.S. Army Corps of Engineers collected vibracores along the inner shelf offshore of Dare County in August 1995 (NDC cores) and July-August 1995 (SNL cores). These cores are curated by the North Carolina Geological Survey and were used as part of the ground validation process in this study. Nearshore geophysical and core data were collected by the Virginia Institute of Marine Science. The nearshore is defined here as the region between the 10-m isobath and the shoreline. High-resolution bathymetry, backscatter intensity, and chirp seismic data were collected between June 2002 and May 2004. Vibracore samples were collected in May and July 2005. Shallow subsurface geophysical data were acquired along the Outer Banks barrier islands using a ground-penetrating radar (GPR) system. Data were collected by East Carolina University from 2002 to 2005. Rotasonic cores (OBX cores) from five drilling operations were collected from 2002 to 2006 by the North Carolina Geological Survey as part of the cooperative study with the USGS. These cores are distributed throughout the Outer Banks as well as the mainland. The USGS collected seismic data for the Quaternary section within the Albemarle-Pamlico estuarine system between 2001 and 2004 during six surveys (2001-013-FA, 2002-015-FA, 2003-005-FA, 2003-042-FA, 2004-005-FA, and 2004-006-FA). These surveys used Geopulse Boomer and Knudsen Engineering Limited (KEL) 320BR Chirp systems, except cruise 2003-042-FA, which used an Edgetech 424 Chirp and a boomer system. The study area includes Albemarle Sound and selected tributary estuaries such as the South, Pungo, Alligator, and Pasquotank Rivers; Pamlico Sound and trunk estuaries including the Neuse and Pamlico Rivers; and back-barrier sounds including Currituck, Croatan, Roanoke, Core, and Bogue.
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The global clinical genomic data analysis market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 6.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 17.2% during the forecast period. This market growth is driven by the increasing adoption of genomic sequencing technologies, advancements in bioinformatics, and the rising prevalence of chronic diseases that necessitate personalized medicine and targeted therapies.
A major growth factor for the clinical genomic data analysis market is the exponential increase in the volume of genomic data being generated. With the cost of sequencing dropping and the speed of sequencing increasing, more genomic data is being produced than ever before. This abundance of data requires sophisticated analysis tools and software to interpret and derive meaningful insights, driving the demand for advanced genomic data analysis solutions. Additionally, the integration of artificial intelligence and machine learning algorithms in genomics is further enhancing the capabilities of these analysis tools, enabling more accurate and faster data interpretation.
Another significant factor contributing to market growth is the rising incidence of genetic disorders and cancers, which necessitates comprehensive genomic analysis for accurate diagnosis and personalized treatment plans. Personalized medicine, which tailors medical treatment to the individual characteristics of each patient, relies heavily on the insights gained from genomic data analysis. As the understanding of the genetic basis of diseases deepens, the demand for clinical genomic data analysis is expected to surge, further propelling market growth.
The integration of NGS Informatics and Clinical Genomics is revolutionizing the field of personalized medicine. By leveraging next-generation sequencing (NGS) technologies, researchers and clinicians can now analyze vast amounts of genomic data with unprecedented speed and accuracy. This integration enables the identification of genetic variants that may contribute to disease, allowing for more precise diagnosis and the development of targeted therapies. As the capabilities of NGS technologies continue to expand, the role of informatics in managing and interpreting this data becomes increasingly critical. The seamless integration of NGS Informatics and Clinical Genomics is paving the way for more effective and personalized healthcare solutions, ultimately improving patient outcomes.
Government initiatives and funding in genomics research also play a crucial role in the expansion of the clinical genomic data analysis market. Many governments around the world are investing heavily in genomic research projects and infrastructure to advance medical research and improve public health outcomes. For instance, initiatives like the 100,000 Genomes Project in the UK and the All of Us Research Program in the US underscore the importance of genomics in understanding human health and disease, thereby boosting the demand for genomic data analysis tools and services.
Regional outlook reveals significant growth opportunities in emerging markets, particularly in the Asia Pacific region. Countries like China, India, and Japan are witnessing rapid advancements in healthcare infrastructure and increasing investments in genomics research. Additionally, favorable government policies and the presence of a large patient pool make this region a lucrative market for clinical genomic data analysis. North America continues to dominate the market due to high healthcare spending, advanced research facilities, and the early adoption of new technologies. Europe also shows steady growth with significant contributions from countries like the UK, Germany, and France.
The component segment of the clinical genomic data analysis market is divided into software and services. The software segment encompasses various bioinformatics tools and platforms used for genomic data analysis. These tools are essential for the effective management, storage, and interpretation of the massive amounts of genomic data generated. The growing complexity of genomic data necessitates the use of robust software solutions that can handle large datasets and provide accurate insights. As a result, the software segment is expected to witness significant growth during the forecast period.
The services segment includes