This set contains data from the United Nations Treaty Series between 1945 and 2016. The data contains 50040 treaties and is taken from the World Legal Information Institute. It might be incomplete in comparison to United Nations direct sources. The rows contain the treaties, and the columns the attributes, such as the title of the treaty, its year of signature, and each party that is part of the treaty. The dataset also contains columns for parties that are not countries but international organizations and several columns on treaty characteristics, such as year, number of parties, the share of parties that are international organizations, and others. All column names are self-explanatory.

The Database Platform as a Service (DBPaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for data analytics. The market's expansion is fueled by businesses migrating legacy on-premise databases to cloud-based alternatives, seeking enhanced agility, and leveraging the advantages of pay-as-you-go models. Major players like Amazon Web Services, Microsoft Azure, and Google Cloud Platform dominate the market, offering a wide range of DBPaaS options catering to diverse needs, from relational databases to NoSQL solutions. The market is segmented by deployment model (public cloud, private cloud, hybrid cloud), database type (SQL, NoSQL, NewSQL), and industry vertical (BFSI, healthcare, retail, etc.). Competition is fierce, with established players constantly innovating and new entrants emerging to challenge the status quo. Factors like data security concerns and integration complexities pose some challenges to market growth. However, advancements in serverless computing and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are expected to drive further expansion. The forecast period (2025-2033) is projected to witness substantial growth, driven by ongoing digital transformation initiatives across various industries. The increasing adoption of cloud-native applications and microservices architectures further necessitates robust and scalable DBPaaS solutions. While the initial investment in migrating to the cloud can be significant, the long-term cost savings and improved efficiency make DBPaaS an attractive option. The market's growth is expected to be particularly strong in regions with high cloud adoption rates and robust digital infrastructure. The competitive landscape will likely remain dynamic, with mergers and acquisitions, strategic partnerships, and continuous product innovation shaping the market's trajectory. Overall, the DBPaaS market is poised for substantial growth, driven by a confluence of technological advancements and evolving business needs. Assuming a conservative CAGR of 20% (a reasonable estimate considering the high growth sectors involved), and a 2025 market size of $50 Billion, we can project substantial future growth.

Nepal Net Official Flows from UN Agencies: UNWOMEN data was reported at 0.802 USD mn in 2022. This records an increase from the previous number of 0.769 USD mn for 2021. Nepal Net Official Flows from UN Agencies: UNWOMEN data is updated yearly, averaging 0.785 USD mn from Dec 2021 (Median) to 2022, with 2 observations. The data reached an all-time high of 0.802 USD mn in 2022 and a record low of 0.769 USD mn in 2021. Nepal Net Official Flows from UN Agencies: UNWOMEN data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank.WDI: Defense and Official Development Assistance. Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.;Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: https://data-explorer.oecd.org/.;Sum;

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).

The Swedish Contextual Database provides a large number of longitudinal and regional macro-level indicators primarily assembled to facilitate research on the effects of contextual factors on family and fertility behavior. It can be linked to the individual-level data of the Swedish GGS as well as to data of other surveys. It can also be used for other types of research and for teaching. The comparative data will also be integrated into the international Contextual Database of the GGP. The contextual data are available open-access through the GGP webpage: www.ggp-i.org and through the webpage of Stockholm University Demography Unit www.suda.su.se

Purpose:

The Swedish contextual database (CDB) was established to accompany the Swedish Generations and Gender Survey (GGS) and to complement the contextual database of the international Generations and Gender Programme (GGP).

The Swedish Contextual Data Collection is available in xls format. In addition to that, the internationally comparative data will be integrated into the Contextual Database (CDB) of the GGP in 2018. These data can be exported in other formats, as well (e.g. CSV, XML). The indicators can also be accessed in a single file in STATA or SPSS format. The data can be matched with the Swedish GGS. International regional coding schemes are also supported, such as NUTS, OECD.

IMOPE is the reference database for buildings at national level. To date and on a daily basis, it supports nearly 20,000 public and private actors and more than 800 territories (in operational context: fight against unworthy housing, fight against vacancy, energy renovation, OPAH-RU, PIG, VOC,...) wanting to know and transform the French building sector.

Resulting from public research conducted at Mines Saint-Etienne (Institut Mines Télécom), this breakthrough innovation, the methods of which have been patented by the Ministry of the Economy, Industrial and Digital Sovereignty, brings together all the data of interest (+ 250 items of information) on each of the 20 million existing buildings.

⁇ Consult the news of the ONB and the national IMOPE database ⁇ ACTU ONB/IMOPE

IMOPE has been co-built, since its creation in 2016, with and for the actors of the territories (ALEC, operators ANAH, ADIL, DDT, ADEME, EPCI, urban planning agencies ...) in order to meet the multiple challenges of the building sector. Issues on which we can cite:energy renovation, combating vacancy, precariousness and unsanitary conditions, attrition of housing, home support, adaptation to climate change, etc.

The sourcing of merged and reprocessed data: A single and multiple sourcing to increase knowledge and merging in particular: - Open Data: BAN, BDTOPO, DVF, DPE (ADEME), consumption data (ENEDIS, GRDF), RPLS, QPV, Georisks, permanent equipment base, SITADEL, socio-economic data (RP, FiLoSoFi, INSEE), OPAH, ... - "Conventional" data: Land files enriched by Cerema (source DGFiP DGALN), LOVAC, non-anonymised data of owners, RNIC (ANAH) - Local or business data: devices, FSL, LHI, orders, procedures, reporting, planning permission, rental permit, ANAH aid, ... - "Enriched" data: Machine Learning and Deep Learning (DVF, DPE, power source and heating type predictions)

A strong commitment to the commons: U.R.B.S, spin-off of Mines Saint-Etienne, maintains, develops and improves on a clean background and since 2019 the IMOPE database. With a view to mutualisation and openness, U.R.B.S. invites the entire building community (architects, public decision-makers, insurers, artisans, diagnosticians, researchers, citizens, design offices, etc.) to disseminate and reuse widely internally as well as externally, natively or with post-processing, the data contained in the IMOPE database.

It is driven by this philosophy of sharing that we have deployed the**National Building Observatory** (ONB). The**ONB** is a citizen geo-common. As a decision-making tool providing knowledge of the building stock, it makes it easier for everyone to access the information contained in the national IMOPE database.

Convinced that together we will go further, the ONB and IMOPE are initiatives led by civil society. Civil society of which we are part and which, we are convinced, is the keystone for achieving the energy, climate and social objectives of the building sector.

⁇ For more information: https://www.urbs.fr ⁇ To contact us: contact@urbs.fr ⁇ To access the ONB: https://app.urbs.fr/onb/connection

⁇ To access the data catalogue, click here

In addition to respondents’ highest educational qualification, some surveys also collect data on their main field of education. Current measurement practice involves either a closed question with highly aggregated response categories, which are difficult to use for respondents, or an open question, requiring expensive post-coding. Therefore, a measurement tool for fields of education was developed in the SERISS-project in work package 8, Task 8.3. In deliverable D8.9 we provide a database of fields of education and training in 34 languages, including the definition of a search tree interface to facilitate navigation of categories for respondents. All 120 standard categories and classification codes are taken from UNESCO's International Standard Classification of Education for Fields of Education and Training (ISCED-F). For most languages, detailed 3-digit information is available. The database, including a live search feature, is available at the surveycodings website at https://surveycodings.org/articles/codings/fields-of-education. The search tree can be used for respondents’ self-identification of fields of education and training in computer-assisted surveys. The live search feature can also be used for post-coding open answers in already collected data.

Sudan Net Official Flows from UN Agencies: SDGFUND data was reported at 0.244 USD mn in 2022. Sudan Net Official Flows from UN Agencies: SDGFUND data is updated yearly, averaging 0.244 USD mn from Dec 2022 (Median) to 2022, with 1 observations. The data reached an all-time high of 0.244 USD mn in 2022 and a record low of 0.244 USD mn in 2022. Sudan Net Official Flows from UN Agencies: SDGFUND data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sudan – Table SD.World Bank.WDI: Defense and Official Development Assistance. Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.;Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: https://data-explorer.oecd.org/.;Sum;

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service (https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do) and the Integrated Taxonomic Information System (https://www.itis.gov/) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system. The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP. Contributing organizations: ACWI The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs. ARS The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service. EPA The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse. NWQMC The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community. USGS The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources. Resources in this dataset:Resource Title: Website Pointer for Water Quality Portal. File Name: Web Page, url: https://www.waterqualitydata.us/ The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.

The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.

This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. This subset, for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America, retains all five arrays at the 1-degree resolution but only for the area of interest (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N). The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats.The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate.The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov].

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The CDKN2A Database presents the germline and somatic variants of the CDKN2A tumor suppressor gene recorded in human disease through June 2003, annotated with evolutionary, structural, and functional information, in a format that allows the user to either download it or manipulate it for their purposes online. The goal is to provide a database that can be used as a resource by researchers and geneticists and that aids in the interpretation of CDKN2A missense variants. Most online mutation databases present flat files that cannot be manipulated, are often incomplete, and have varying degrees of annotation that may or may not help to interpret the data. They hope to use CDKN2A as a prototype for integrating computational and laboratory data to help interpret variants in other cancer-related genes and other single nucleotide polymorphisms (SNPs) found throughout the genome. Another goal of the lab is to interpret the functional and disease significance of missense variants in cancer susceptibility genes. Eventually, these results will be relevant to the interpretation of single nucleotide polymorphisms (SNPs) in general. The CDKN2A locus is a valuable model for assessing relationships among variation, structure, function, and disease because: Variants of this gene are associated with hereditary cancer: Familial Melanoma (and related syndromes); somatic alterations play a role in carcinogenesis; allelic variants occur whose functional consequences are unknown; reliable functional assays exist; and crystal structure is known. All variants in the database are recorded according to the nomenclature guidelines as outlined by the Human Genome Variation Society. This database is currently designed for research purposes only and is not yet recommended as a clinical resource. Many of the mutations reported here have not been tested for disease association and may represent normal, non-disease causing polymorphisms.

Net official flows from UN agencies, WHO (current US$) in Afghanistan was reported at 2086291 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Afghanistan - Net official flows from UN agencies; WHO - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Net official flows from UN agencies, UNHCR (current US$) in Mexico was reported at 4933700 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Mexico - Net official flows from UN agencies, UNHCR - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

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.

Cracids are among the most vulnerable groups of Neotropical birds. Almost half of the species of this family are included in a conservation risk category. Twelve taxa occur in Mexico, six of which are considered at risk at national level and two are globally endangered. Therefore, it is imperative that high quality, comprehensive, and high-resolution spatial data on the occurrence of these taxa are made available as a valuable tool in the process of defining appropriate management strategies for conservation at a local and global level. We constructed the CracidMex1 database by collating global records of all cracid taxa that occur in Mexico from available electronic databases, museum specimens, publications, “grey literature”, and unpublished records. We generated a database with 23,896 clean, validated, and standardized geographic records. Database quality control was an iterative process that commenced with the consolidation and elimination of duplicate records, followed by the geo-referencing of records when necessary, and their taxonomic and geographic validation using GIS tools and expert knowledge. We followed the geo-referencing protocol proposed by the Mexican National Commission for the Use and Conservation of Biodiversity. We could not estimate the geographic coordinates of 981 records due to inconsistencies or lack of sufficient information in the description of the locality. Given that current records for most of the taxa have some degree of distributional bias, with redundancies at different spatial scales, the CracidMex1 database has allowed us to detect areas where more sampling effort is required to have a better representation of the global spatial occurrence of these cracids. We also found that particular attention needs to be given to taxa identification in those areas where congeners or conspecifics co-occur in order to avoid taxonomic uncertainty. The construction of the CracidMex1 database represents the first comprehensive research effort to compile current, available global geographic records for a group of cracids. The database can now be improved by continuous revision and addition of new records. The CracidMex1 database will provide high quality input data that could be used to generate species distribution models, to assess temporal changes in species distributions, to identify priority areas for research and conservation, and in the definition of management strategies for this bird group. This compilation exercise could be replicated for other cracid groups or regions to attain a better knowledge of the global occurrences of the species in this vulnerable bird family.

BPS is a database of RNA base pairs with quantitative information on the spatial arrangements of interacting bases, including higher-order base associations, and the context of these interactions in high-resolution crystal structures. The structures are taken from the Nucleic Acid Database (NDB), and the base pairs are identified and characterized with the 3DNA software package. The interactions are classified in terms of residue identities, base-pair positioning, and hydrogen-bonding patterns and related to the structural context in which they occur. A user can browse the atlas of base-pair patterns and carry out searches for patterns of specific types or from specific structures.

A novel subject classification scheme should often be applied to a preclassified bibliographic database for the research evaluation task. Generally, adopting a new subject classification scheme is labor intensive and time consuming, and an effective and efficient approach is necessary. Hence, we propose an approach to apply a new subject classification scheme for a subject-classified database using a data-driven correspondence between the new and present ones. In this paper, we define a subject classification model of the bibliographic database comprising a topological space. Then, we show our approach based on this model, wherein forming a compact topological space is required for a novel subject classification scheme. To form the space, a correspondence between two subject classification schemes using a research project database is utilized as data. As a case study, we applied our approach to a practical example. It is a tool used as world proprietary benchmarking for research evaluation based on a citation database. We tried to add a novel subject classification of a research project database.

A database containing details of water-related researchers located in Irish academic institutions