Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Reproducibility data for the AntiBody Sequence Database (ABSD) article. This dataset contains the raw data (antibody sequences) extracted on June 20, 2024, from various databases, as well as the several scripts, to ensure the reproducibility of our results. External databases used: ABDB, AbPDB, CoV-AbDab, Genbank, IMGT, PDB, SACS, SAbDab, TheraSAbDab, UniProt, KABAT Scripts usage: each external database has a corresponding script to format all antibody sequences extracted from it. A last script enable merging all extracted antibody sequences while removing redundancy, standardizing and cleaning data.

The Database Security market is experiencing robust growth, projected to reach $2556.1 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 11.4% from 2025 to 2033. This expansion is fueled by the increasing frequency and sophistication of cyberattacks targeting sensitive data stored in databases, coupled with stringent data privacy regulations like GDPR and CCPA. The rising adoption of cloud computing and the proliferation of big data also contribute significantly to market growth, as organizations require robust security solutions to protect their valuable data assets across diverse environments. The market is segmented by application (SMEs, Large Enterprises) and type (Marketing, Sales, Operations, Finance, HR & Legal), with large enterprises and applications involving sensitive financial data demonstrating particularly high demand for advanced database security solutions. North America currently holds a dominant market share due to early adoption of advanced technologies and a strong regulatory landscape, but the Asia-Pacific region is poised for significant growth, driven by increasing digitalization and a rapidly expanding economy. The competitive landscape is characterized by a mix of established players like Oracle and IBM, alongside specialized security vendors such as Trustwave and McAfee. These companies offer a wide range of solutions, including database activity monitoring, encryption, access control, and vulnerability management. The market is witnessing innovation in areas like AI-powered threat detection and automated security response, which are enhancing the effectiveness and efficiency of database security solutions. However, challenges remain, including the rising complexity of cyber threats, the skills gap in cybersecurity professionals, and the high cost of implementing and maintaining comprehensive database security systems. The continued evolution of cyberattacks and data privacy regulations will be key drivers shaping the future of this dynamic market.

Code and data to generate GloPUT database. Data set description found in "Global pesticide use and trade database (GloPUT): New estimates show pesticide use trends in low-income countries substantially underestimated." (2023) Global Environmental Change.

Link to paper: https://doi.org/10.1016/j.gloenvcha.2023.102693

The NYS Department of Environmental Conservation (DEC) collects and maintains several datasets on the locations, distribution and status of species of plants and animals. Information on distribution by county from the following three databases was extracted and compiled into this dataset. First, the New York Natural Heritage Program biodiversity database: Rare animals, rare plants, and significant natural communities. Significant natural communities are rare or high-quality wetlands, forests, grasslands, ponds, streams, and other types of habitats. Next, the 2nd NYS Breeding Bird Atlas Project database: Birds documented as breeding during the atlas project from 2000-2005. And last, DEC’s NYS Reptile and Amphibian Database: Reptiles and amphibians; most records are from the NYS Amphibian & Reptile Atlas Project (Herp Atlas) from 1990-1999.

Comprehensive dataset of 1 Credit unions in Roi Et, Thailand as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The global Information Technology Application Innovation Databases market is experiencing robust growth, driven by the increasing adoption of cloud computing, big data analytics, and the digital transformation initiatives across various sectors. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. Key drivers include the rising demand for real-time data processing, enhanced data security needs across sectors like Smart Government Affairs and Information Security, and the accelerating digitalization of industries. The market is segmented by database type (RDBMS and NoSQL) and application (Smart Government Affairs, Information Security, Industry Digitalization, Digital Industrialization, and Others). RDBMS currently holds a larger market share due to its established presence and maturity, but NoSQL databases are gaining traction, fueled by the need for scalability and flexibility in handling unstructured data. The strong growth in the Asia-Pacific region, particularly in China and India, is further contributing to the overall market expansion, driven by rapid technological advancements and increasing government investments in digital infrastructure. However, challenges like data privacy concerns, the complexity of database management, and the high initial investment costs act as restraints. The competitive landscape is highly fragmented, with major players including Oracle, IBM, Microsoft, Amazon (AWS), and Google Cloud Platform offering a range of database solutions. These companies are constantly innovating to improve performance, security, and scalability, leading to increased competition and fostering market growth. The shift toward cloud-based database solutions is a prominent trend, offering businesses scalability, cost-effectiveness, and improved accessibility. The convergence of databases with artificial intelligence (AI) and machine learning (ML) is also emerging as a key trend, enabling more intelligent data analysis and decision-making. Future growth will be significantly influenced by the adoption of advanced technologies like blockchain, serverless computing, and edge computing within database management systems. Continued investment in research and development will be crucial for companies to maintain their competitive edge in this rapidly evolving market.

Database Security Solution Market Outlook

The global database security solution market was valued at USD 4.5 billion in 2023 and is projected to reach USD 11.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2032. This remarkable growth can be attributed to the increasing volume of data generated and stored by organizations, rising cyber threats, regulatory compliance requirements, and the growing adoption of cloud-based services across various industries.

One of the primary growth factors for the database security solution market is the exponential increase in data generation and storage. With the advent of big data, IoT, and advanced analytics, organizations are producing vast amounts of data that need to be securely stored and managed to prevent unauthorized access and data breaches. As a result, there is a growing demand for robust database security solutions that can protect sensitive information across diverse databases and platforms, ensuring data privacy and integrity.

Another significant growth driver is the rising number of cyber threats and data breaches. Organizations face sophisticated cyber-attacks that target confidential and high-value data, leading to financial losses, reputational damage, and regulatory penalties. This has necessitated the implementation of advanced database security solutions that offer real-time threat detection, encryption, access control, and audit capabilities to safeguard critical data and maintain business continuity.

Compliance with stringent regulatory frameworks is also propelling the growth of the database security solution market. Regulations such as GDPR, HIPAA, and CCPA mandate the protection of personal and sensitive information, compelling organizations to adopt comprehensive database security measures. Businesses are investing heavily in database security solutions to meet these regulatory requirements, avoid hefty fines, and build customer trust by ensuring data confidentiality and compliance.

The advent of Big Data Security has become a pivotal aspect in the realm of database security solutions. As organizations increasingly rely on big data analytics to drive business insights, the security of this data becomes paramount. Big Data Security involves implementing comprehensive measures to protect large volumes of data from unauthorized access and breaches. It encompasses various strategies, including encryption, access controls, and real-time monitoring, to ensure that sensitive data remains protected throughout its lifecycle. As the volume and complexity of data continue to grow, the demand for advanced Big Data Security solutions is expected to rise, driving further innovation and investment in this area.

Regionally, the database security solution market is witnessing significant growth, with North America leading the charge due to its advanced technological infrastructure, early adoption of innovative security solutions, and stringent data protection laws. Europe is also experiencing substantial growth driven by the enforcement of GDPR and increasing awareness of data privacy issues. The Asia Pacific region is projected to witness the highest CAGR during the forecast period, fueled by the rapid digital transformation, rising cyber threats, and growing government initiatives to enhance cybersecurity.

Component Analysis

The database security solution market can be segmented by component into software, hardware, and services. The software segment holds the largest market share, driven by the extensive use of database security software to protect data against unauthorized access, malware, and other cyber threats. These software solutions offer various functionalities such as encryption, access control, auditing, and monitoring, making them indispensable for organizations looking to secure their databases effectively.

The hardware segment, although smaller compared to software, plays a crucial role in enhancing database security. Hardware-based security solutions, such as hardware security modules (HSMs), are used for cryptographic key management and secure storage of sensitive data. These solutions provide an additional layer of security by ensuring that cryptographic operations are performed in a tamper-resistant environment, thus preventing unauthorized access and key compromise.

The services segment is also witnessing significant growth, driven by the increasing demand for m

This data set is a Microsoft Access database containing detailed station data (station name number, date, location, depth, time, and bottom temperature) as well as species, fish counts and measurements, and habitat data derived from the raw video and still images and from chevron fish traps.

• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467). * Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312) * Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries). * Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

GC skew denotes the relative excess of G nucleotides over C nucleotides on the leading versus the lagging replication strand of eubacteria. While the effect is small, typically around 2.5%, it is robust and pervasive. GC skew and the analogous TA skew are a localized deviation from Chargaff’s second parity rule, which states that G and C, and T and A occur with (mostly) equal frequency even within a strand.

Most bacteria also show the analogous TA skew. Different phyla show different kinds of skew and differing relations between TA and GC skew. This article introduces an open access database (https://skewdb.org) of GC and 10 other skews for over 28,000 chromosomes and plasmids. Further details like codon bias, strand bias, strand lengths and taxonomic data are also included.

The SkewDB database can be used to generate or verify hypotheses. Since the origins of both the second parity rule, as well as GC skew itself, are not yet satisfactorily explained, such a database may enhance our understanding of microbial DNA.

Methods The SkewDB analysis relies exclusively on the tens of thousands of FASTA and GFF3 files available through the NCBI download service, which covers both GenBank and RefSeq. The database includes bacteria, archaea and their plasmids. Furthermore, to ease analysis, the NCBI Taxonomy database is sourced and merged so output data can quickly be related to (super)phyla or specific species. No other data is used, which greatly simplifies processing. Data is read directly in the compressed format provided by NCBI.

All results are emitted as standard CSV files. In the first step of the analysis, for each organism the FASTA sequence and the GFF3 annotation file are parsed. Every chromosome in the FASTA file is traversed from beginning to end, while a running total is kept for cumulative GC and TA skew. In addition, within protein coding genes, such totals are also kept separately for these skews on the first, second and third codon position. Furthermore, separate totals are kept for regions which do not code for proteins. In addition, to enable strand bias measurements, a cumulative count is maintained of nucleotides that are part of a positive or negative sense gene. The counter is increased for positive sense nucleotides, decreased for negative sense nucleotides, and left alone for non-genic regions.

A separate counter is kept for non-genic nucleotides. Finally, G and C nucleotides are counted, regardless of if they are part of a gene or not. These running totals are emitted at 4096 nucleotide intervals, a resolution suitable for determining skews and shifts. In addition, one line summaries are stored for each chromosome. These line includes the RefSeq identifier of the chromosome, the full name mentioned in the FASTA file, plus counts of A, C, G and T nucleotides. Finally five levels of taxonomic data are stored.

Chromosomes and plasmids of fewer than 100 thousand nucleotides are ignored, as these are too noisy to model faithfully. Plasmids are clearly marked in the database, enabling researchers to focus on chromosomes if so desired. Fitting Once the genomes have been summarised at 4096-nucleotide resolution, the skews are fitted to a simple model. The fits are based on four parameters. Alpha1 and alpha2 denote the relative excess of G over C on the leading and lagging strands. If alpha1 is 0.046, this means that for every 1000 nucleotides on the leading strand, the cumulative count of G excess increases by 46. The third parameter is div and it describes how the chromosome is divided over leading and lagging strands. If this number is 0.557, the leading replication strand is modeled to make up 55.7% of the chromosome. The final parameter is shift (the dotted vertical line), and denotes the offset of the origin of replication compared to the DNA FASTA file. This parameter has no biological meaning of itself, and is an artifact of the DNA assembly process.

The goodness-of-fit number consists of the root mean squared error of the fit, divided by the absolute mean skew. This latter correction is made to not penalize good fits for bacteria showing significant skew. GC skew tends to be defined very strongly, and it is therefore used to pick the div and shift parameters of the DNA sequence, which are then kept as a fixed constraint for all the other skews, which might not be present as clearly. The fitting process itself is a downhill simplex method optimization over the three dimensions, seeded with the average observed skew over the whole genome, and assuming there is no shift, and that the leading and lagging strands are evenly distributed. The simplex optimization is tuned so that it takes sufficiently large steps so it can reach the optimum even if some initial assumptions are off.

Ethiopia ET: General Government: Net Debt: % of GDP data was reported at 46.976 % in 2023. This records a decrease from the previous number of 53.235 % for 2022. Ethiopia ET: General Government: Net Debt: % of GDP data is updated yearly, averaging 55.254 % from Dec 1992 (Median) to 2023, with 32 observations. The data reached an all-time high of 129.201 % in 1994 and a record low of 29.189 % in 2009. Ethiopia ET: General Government: Net Debt: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Ethiopia – Table ET.IMF.FM: Government Finance Statistics.

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. To characterize the climate conditions across the study period, we use the Standardized Precipitation Evapotranspiration Index (SPEI). The SPEI is a water balance index which includes both precipitation and evapotranspiration in its calculation. Conditions from the prior n months, generally ranging from 1 to 60, are compared to the same respective period over the prior years to identify the index value (Vicente-Serrano et al., 2010). Values generally range from -3 to 3, where values less than 0 suggest drought conditions while values greater than 0 su ...

United States Export Price Index: Mfg: MA: ET: Others data was reported at 117.800 Dec2019=100 in Mar 2025. This stayed constant from the previous number of 117.800 Dec2019=100 for Feb 2025. United States Export Price Index: Mfg: MA: ET: Others data is updated monthly, averaging 110.650 Dec2019=100 from Dec 2019 (Median) to Mar 2025, with 64 observations. The data reached an all-time high of 118.200 Dec2019=100 in Aug 2024 and a record low of 100.000 Dec2019=100 in Dec 2019. United States Export Price Index: Mfg: MA: ET: Others data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.JA118: Export Price Index: by NAICS.

Ethiopia ET: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data was reported at 51.260 % in 2016. This records an increase from the previous number of 50.454 % for 2015. Ethiopia ET: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data is updated yearly, averaging 10.870 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 51.260 % in 2016 and a record low of 2.040 % in 1985. Ethiopia ET: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank: Imports. Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree. The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013). The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.

This is version v3.3.0.2022f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20230101_v3.3.1.2022f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note. Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

The Database Automation Systems market is experiencing robust growth, driven by the increasing complexity of databases, the rising demand for improved operational efficiency, and the need for enhanced data security. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key trends, including the widespread adoption of cloud-based database solutions, the growing popularity of DevOps methodologies, and the increasing focus on automation across various industries. The on-premise segment currently holds a significant market share, but cloud-based solutions are rapidly gaining traction due to their scalability, cost-effectiveness, and accessibility. The IT & Telecom sector is a major driver of market growth, followed by the Government and Transportation sectors. However, challenges such as the high initial investment costs associated with implementing database automation systems and the need for skilled professionals to manage these systems act as restraints. The geographical distribution of the market reflects a strong presence in North America, followed by Europe and Asia Pacific. North America’s dominance is attributed to early adoption of advanced technologies and a robust IT infrastructure. However, emerging economies in Asia Pacific, particularly India and China, are showing significant growth potential due to increasing digitalization and investments in IT infrastructure. The competitive landscape is characterized by a mix of established players like Oracle, IBM, and Microsoft, alongside specialized database automation vendors like Datavail and Quest Software. These companies are continuously innovating to enhance their offerings, focusing on AI-powered automation, improved integration with other IT tools, and enhanced security features to maintain their competitive edge.

The French database is implemented as a relational database that integrates multiple interconnected data frames. It is organized around four main components:

• Thesis Metadata: This table contains the core information for each dissertation. Each entry corresponds to a single thesis and includes details such as the title, defense date, abstract, and other relevant metadata.
• Edges Data: This table captures the connections between the other three tables, linking individuals, institutions, and theses. It associates each thesis with the individuals and institutions involved in its production, thereby enabling a synthetized view of these relationships. The edges data are provided in two formats: (1) a ready-to-use format with cleaned and standardized information; and (2) a more extensive format that allows for comparison between the original collected data and the results of the cleaning process.
• Institutions Data: This table includes information on universities, laboratories, doctoral schools, and other institutions associated with the dissertations. Each entry corresponds to a single institution.
• Individual Data: This table contains information on the individuals involved in the dissertations, including authors, supervisors, and jury members. Each entry corresponds to a single individual.

The data used in this project comes from three mains sources:

• Theses.fr: https://theses.fr/
• Sudoc: https://www.sudoc.fr/
• IdRef: https://www.idref.fr/

If you use our data or scripts, please cite the following reference: “Delcey Thomas, and Aurélien Goutsmedt. (2024). Becoming an Economist: A Database of French Economics PhDs. Zenodo. https://doi.org/10.5281/zenodo.14541427”

Ethiopia ET: Trade Balance: Advanced Economies: Germany data was reported at -5.815 USD mn in May 2018. This records a decrease from the previous number of -0.172 USD mn for Apr 2018. Ethiopia ET: Trade Balance: Advanced Economies: Germany data is updated monthly, averaging -1.662 USD mn from Feb 1967 (Median) to May 2018, with 504 observations. The data reached an all-time high of 30.500 USD mn in Nov 2012 and a record low of -44.003 USD mn in Dec 2015. Ethiopia ET: Trade Balance: Advanced Economies: Germany data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Ethiopia – Table ET.IMF.DOT: Trade Balance: by Country: Monthly.