This web page leads to a database of images and information about the 150 major impact craters on Ganymede and is updated semi-regularly based on continuing analysis of Voyager 2 images.

A database for the entire Markarian (First Byurakan Spectral Sky Survey or FBS) Catalog is presented that combines extensive new measurements of their optical parameters with a literature and database search. The measurements were made using images extracted from the STScI Digitized Sky Survey (DSS) of F_pg (red) and J_pg (blue) band photographic sky survey plates obtained by the Palomar and UK Schmidt telescopes. The authors provide accurate coordinates, morphological type, spectral and activity classes, red and blue apparent magnitudes, apparent diameters, axial ratios, and position angles, as well as number counts of neighboring objects in a circle of radius 50 kpc. Special attention was paid to the individual descriptions of the galaxies in the original Markarian lists, which clarified many cases of misidentifications of the objects, particularly among interacting systems, larger galaxies with knots of star formation, possible stars, and cases of stars projected on galaxies. The total number of individual Markarian objects in the database is now 1544. The authors also have included redshifts which are now available for 1524 of the objectswith UV-excess radiation, as well as Galactic color excess E(B-V) values and their 2MASS or DENIS infrared magnitudes. The table also includes extensive notes that summarize information about the membership of Markarian galaxies in different systems of galaxies and about new and revised activity classes and redshifts. The new optical information on Markarian galaxies was obtained from images extracted from the STScI Digitized Sky Survey (DSS) of F_pg (red) and J_pg (blue) band photographic sky survey plates obtained by the Palomar and UK Schmidt telescopes. This table was created by the HEASARC in November 2009 based on the electronic version of the optical database of Markarian galaxies which was obtained from the CDS (their catalog J/ApJS/170/33 file table1.dat). This is a service provided by NASA HEASARC .

This web page leads to a database of images and information about the 150 major impact craters on Callisto and is updated semi-regularly based on continuing analysis of Voyager images.

To the contents of the Central Bank's data openness action plan revised in December 2019, it is an open ODT file.

Database used to store client data both Identity and customer relationship management.

This is a database of pile load test information that has been built as part of the Engineering and Physical Sciences Research Council (EPSRC) funded project EP/P020933/1: Databases to INterrogate Geotechnical Observations (DINGO) which ran between 1 July 2017 and 9 June 2019. The database is populated with data digitised from the literature as well as datasets supplied by contributors from the geotechnical engineering industry in the United Kingdom. Contributors have agreed in writing for their data to be shared via the DINGO Database and are cited as personal communication. v1.1 is a minor revision of v1.0 with some error corrections. v1.0 can be found at https://doi.org/10.5523/bris.3r14qbdhv648b2p83gjqby2fl8. N.b. these data have been superseded by The DINGO Database, v1.2 (https://doi.org/10.5523/bris.1jraem68g7ara21p2oi6hv4z22).

National Land Cover Database 2011 (NLCD 2011) is the most recent national land cover product created by the Multi-Resolution Land Characteristics (MRLC) Consortium. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data. This dataset is associated with the following publication: Homer, C., J. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N. Herold, J. Wickham , and K. Megown. Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING. American Society for Photogrammetry and Remote Sensing, Bethesda, MD, USA, 81(0): 345-354, (2015).

The Learning Resources Database is a catalog of interactive tutorials, videos, online classes, finding aids, and other instructional resources on National Library of Medicine (NLM) products and services. Resources may be available for immediate use via a browser or downloadable for use in course management systems

Dataset Description

It contains 520 rows and 13 variables as listed below - - Resource ID : Alphanumeric identifier - Resource Name : Title of the resource - Resource URL : Link of the resource - Description : Brief explanation on the reource - Archived : Flagged as False for all data points - Format : Format of the resource ex. HTML, PDF, MP4 video , MS Word, Powerpoint etc. - Type : Type of the resource ex Webinar, document, tutorial, slides etc. - Runtime : Runtime of the resource - Subject Areas : Topic covered in reource - Authoring Organization : Name of the Authoring Organization - Intended Audiences : Profile of the intended audience - Record Modified : Timestamp info on record last modification - Resource Revised : Timestamp info on resource last modified

The ARG Database is a huge collection of labeled and unlabeled graphs realized by the MIVIA Group. The aim of this collection is to provide the graph research community with a standard test ground for the benchmarking of graph matching algorithms.

Open-Source Database Software Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 35.83 Billion by 2032, growing at a CAGR of 20% during the forecast period 2026-2032.

Global Open-Source Database Software Market Drivers

The market drivers for the Open-Source Database Software Market can be influenced by various factors. These may include:

Cost-Effectiveness: Compared to proprietary systems, open-source databases frequently have lower initial expenses, which attracts organizations—especially startups and small to medium-sized enterprises (SMEs) with tight budgets. Flexibility and Customisation: Open-source databases provide more possibilities for customization and flexibility, enabling businesses to modify the database to suit their unique needs and grow as necessary. Collaboration and Community Support: Active developer communities that share best practices, support, and contribute to the continued development of open-source databases are beneficial. This cooperative setting can promote quicker problem solving and innovation. Performance and Scalability: A lot of open-source databases are made to scale horizontally across several nodes, which helps businesses manage expanding data volumes and keep up performance levels as their requirements change. Data Security and Sovereignty: Open-source databases provide businesses more control over their data and allow them to decide where to store and use it, which helps to allay worries about compliance and data sovereignty. Furthermore, open-source code openness can improve security by making it simpler to find and fix problems. Compatibility with Contemporary Technologies: Open-source databases are well-suited for contemporary application development and deployment techniques like microservices, containers, and cloud-native architectures since they frequently support a broad range of programming languages, frameworks, and platforms. Growing Cloud Computing Adoption: Open-source databases offer a flexible and affordable solution for managing data in cloud environments, whether through self-managed deployments or via managed database services provided by cloud providers. This is because more and more organizations are moving their workloads to the cloud. Escalating Need for Real-Time Insights and Analytics: Organizations are increasingly adopting open-source databases with integrated analytics capabilities, like NoSQL and NewSQL databases, as a means of instantly obtaining actionable insights from their data.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

This data set describes select global soil parameters from the Harmonized World Soil Database (HWSD) v1.2, including additional calculated parameters such as area weighted soil organic carbon (kg C per m2), as high resolution NetCDF files. These data were regridded and upscaled from the Harmonized World Soil Database v1.2 The HWSD provides information for addressing emerging problems of land competition for food production, bio-energy demand and threats to biodiversity and can be used as input to model global carbon cycles. The data are presented as a series of 27 NetCDF v3/v4 (*.nc4) files at 0.05-degree spatial resolution, and one NetCDF file regridded to the Community Land Model (CLM) grid cell resolution (0.9 degree x 1.25 degree) for the nominal year of 2000.

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Water Bodies Database (ASTWBD) Version 1 data product provides global coverage of water bodies larger than 0.2 square kilometers at a spatial resolution of 1 arc second (approximately 30 meters) at the equator, along with associated elevation information. The ASTWBD data product was created in conjunction with the ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 data product by the Sensor Information Laboratory Corporation (SILC) in Tokyo. The ASTER GDEM Version 3 data product was generated using ASTER Level 1A scenes acquired between March 1, 2000, and November 30, 2013. The ASTWBD data product was then generated to correct elevation values of water body surfaces.To generate the ASTWBD data product, water bodies were separated from land areas and then classified into three categories: ocean, river, or lake. Oceans and lakes have a flattened, constant elevation value. The effects of sea ice were manually removed from areas classified as oceans to better delineate ocean shorelines in high latitude areas. For lake water bodies, the elevation for each lake was calculated from the perimeter elevation data using the mosaic image that covers the entire area of the lake. Rivers presented a unique challenge given that their elevations gradually step down from upstream to downstream; therefore, visual inspection and other manual detection methods were required. The geographic coverage of the ASTWBD extends from 83°N to 83°S. Each tile is distributed in GeoTIFF format and referenced to the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. Each data product is provided as a zipped file that contains an attribute file with the water body classification information and a DEM file, which provides elevation information in meters.

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

The size of the Open Source Time Series Database market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

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.

A database that contains sequences built from the existing primary sequence data in GenBank. The sequences and corresponding annotations are experimentally supported and have been published in a peer-reviewed scientific journal.

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable. Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets. Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary. The reach of the WDPA is further enhanced in services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.

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).