Purpose of data.gov.lv is to gather and to circulate Government institution and Government organization collected data in on place for public use, as this data is valuable for the development of innovations in the state. On this portal datasets can be browsed by category, keywords or institution. The portal is based on open source technologies including CKAN Open data catalogue. Developed add-ons are available at: https://github.com/dpp-dev The Latvian Open Data Portal was created by the European Regional Development Fund co-financed project Nr. 2.2.1.1/16/I/001 "Public Administration Information and Communication Technology Architecture Management System" (PIKTAPS) The Third Action Plan for Open Government Partnership of Latvia is taking an action towards openness, responsibility, publics’ participation and the use of ICT [1] This plan carries out the improvement and implementation of various services in the Internet environment. One of the 12 commitment plans of Latvia is the development of an open-source public data portal. But to fulfill the entire target of 2017-2019 OGP plan Latvia has committed to involve society in the selection of datasets. Consequently, the website has the ability to vote which data should be opened. So far in Latvia many valuable data were not available, since collecting them is a paid service, for that reason, access to re-usable data containing metadata has been difficult. Only a few institutions, on their own initiative, published open data on their websites. In 2013, the European Union (EU) adopted the Directive 2013/37/EU with a view to introducing uniform practices and rules in all Member States for the re-use of public administration information. In Latvia, in 2015, the relevant amendments were incorporated into the Information Disclosure Law. According to the directive, in Latvia the data that is open should be published "on the authority of its own initiative, if it is useful ", which means the voluntary principle in the publication of data and does not promote the general" open by default "compliance with the principle. This portal was opened within the OGP to facilitate the opening of data, of course, with respect to the protection of personal data. Involving society in choosing the datasets to be opened. From this commitment, an open data portal, gains all the groups of the public: Members of the society will not only be able to vote for data sets of interest to them, but also to obtain data without bureaucracy. Government institutions will increase the efficiency of work and improve their image by opening and publishing their data. Entrepreneurs will have more data available that can be used to create new products or services, thus contributing to overall economic growth.

Conversion factors for euro fixed series into euro/ECU - monthly data

Open Data Portal Software Market Outlook

According to our latest research, the global Open Data Portal Software market size reached USD 1.24 billion in 2024, demonstrating robust momentum driven by the increasing demand for data transparency and digital governance initiatives worldwide. The market is projected to expand at a strong CAGR of 16.1% during the forecast period, reaching a forecasted value of USD 3.23 billion by 2033. Key growth factors include the proliferation of open government data initiatives, rapid digital transformation across public and private sectors, and the growing need for centralized, accessible data repositories to fuel innovation and drive operational efficiency.

The Open Data Portal Software market is witnessing exponential growth due to the rising emphasis on data-driven decision-making across both governmental and commercial organizations. Governments worldwide are increasingly adopting open data policies to promote transparency, accountability, and citizen engagement. These policies are compelling public agencies to invest in robust open data portal solutions that can efficiently manage, publish, and share large volumes of structured and unstructured data. Moreover, the integration of advanced analytics, artificial intelligence, and machine learning tools within these portals is unlocking new avenues for value creation, enabling organizations to derive actionable insights from vast datasets. This trend is further amplified by regulatory mandates such as the Open Government Directive in the United States and the European Union’s Open Data Directive, which require public sector bodies to make their data freely available, thus bolstering market demand.

Another significant growth driver for the Open Data Portal Software market is the accelerated pace of digital transformation initiatives across industries. Enterprises are increasingly recognizing the strategic value of open data in fostering innovation, enhancing customer experiences, and driving operational efficiencies. By leveraging open data portals, organizations can break down data silos, facilitate seamless data sharing across departments, and foster collaboration with external stakeholders such as partners, developers, and researchers. The adoption of cloud-based deployment models is further democratizing access to open data portal solutions, enabling small and medium enterprises (SMEs) to participate in the data economy without incurring high upfront infrastructure costs. As digital ecosystems continue to evolve, the demand for scalable, secure, and user-friendly open data portal software is expected to surge, underpinning sustained market growth.

The Open Data Portal Software market is also benefiting from the increasing focus on smart city initiatives and the Internet of Things (IoT). Municipal governments and urban planners are leveraging open data portals to aggregate and disseminate real-time data on transportation, energy consumption, public safety, and environmental metrics. This data-driven approach is empowering cities to optimize resource allocation, enhance service delivery, and engage citizens in urban governance. Furthermore, the growing adoption of open data portals in sectors such as healthcare, education, and transportation is unlocking new opportunities for innovation, research, and public-private partnerships. As organizations strive to harness the full potential of open data, the market for robust, interoperable, and customizable open data portal software is poised for significant expansion over the next decade.

Regionally, North America continues to dominate the Open Data Portal Software market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, has emerged as a frontrunner due to its early adoption of open government data initiatives and strong regulatory support. Europe is witnessing rapid growth, driven by stringent data transparency regulations and the proliferation of digital government programs across member states. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by digital transformation efforts in countries such as China, India, and Australia. Latin America and the Middle East & Africa are also showing promising signs of adoption, albeit at a slower pace, as governments and enterprises in these regions increasingly recognize the value of open data in driving socio-economic development.

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The BMDV open data portal mCLOUD offers a Export interface (REST-API) via the data as RDF according to the DCAT-AP.de specification or can be exported as CSV.

Export as DCAT-AP.de in RDF/XML:

Basic path: https://mcloud.de/export/datasets

Export as CSV:

Basic path: https://mcloud.de/export/csv/datasets

Parameters:

The parameters in the requests are based on the parameters in the portal for a remote search (URL).
At the end of a hit page in the portal, the export is always offered. One possibility is to search normally via the portal and then copy the export URL at the end of a page.

Single data record

A single data record can be retrieved by appending the UUID.
E.g. https://mcloud.de/export/datasets/922e436b- 2f0d-42d7-b3f4-528debab8b87
This export is directly available in the mCLOUD in the data record as a "link to the metadata".

Predefined filters:

All data sets added in the last 24 hours:
filter=newdatasets
https://mcloud.de/export/datasets?filter=newdatasets

All data sets that were changed in the last 24 hours (also includes newly added records):
filter=modifieddatasets
https://mcloud.de/export/datasets?filter=modifieddatasets

Paging (default):

pageSize=10 (number of sentences on one page)
page=1 (display first page)
https://mcloud.de/export/datasets?page=1&pageSize=10

Im DCAT-AP.de export always includes navigation information at the beginning:
itemsPerPage (= pageSize parameter)
totalItems (total number)
firstPage (= first page for page parameter)
lastPage (= last page for page parameter)

Search term:

< i>query=Vehicle
https://mcloud.de/export/ datasets?query=Vehicle

Search facet:

aggs=...
The facet is then specified exactly as in the portal request. Please note the coding:
format%3ACSV = type of access "CSV"
categories%3Aroads = category "road"
format%3ACSV%40%40categories%3Aroads = type of access "CSV" AND category "road"

Together:
aggs=format%3ACSV %40%40categories%3Aroads
https ://mcloud.de/export/datasets?aggs=format%3ACSV%40%40categories%3Aroads

Here is the search in the portal, you can use this as a guide:
https://mcloud .de/web/guest/suche/-/results/filter/auto/format%3ACSV%40%40categories%3Aroads/0
At the end of the page there is also the link (as RDF):
https://mcloud.de/export/datasets?page=1&pageSize=1147&sortOrder=desc&sortField=latest&aggs=format%3ACSV%40%40categories%3Aroads

Sorting field:

No information sorted by ID of the data records
sortField=relevance (relevance)
sortField=latest (current)

Sort order:

sortOrder=asc (ascending, default)
sortOrder=desc (descending)

NASA provides a number of lunar samples for display at museums, planetariums, and scientific expositions around the world. Lunar displays are open to the public. This is a database of every location around the planet displaying returned lunar samples from the Apollo missions.

Comprehensive list of data from the Mekong River Commission. The data dashboard includes time series and map data. Time series parameters include: Water level, rainfall, Nitrate, water temperature, Ammonium, phosphorous, sulphate, total suspended, ph, disolved oxygen, evaporation rates, conductivity, nitrogen, air temperature, relative humidity, sediment concentration, for a range of durations depending on location/station selected. Data can be saved and downloaded via creation of a free user account

In an effort to assist federal, state and local community officials with risk communication efforts and build a support base for hazard mitigation, sustainability, and resilience discussions within their communities, FEMA Region III has developed the Mitigation Mapping Data Portal. This Geographic Portal provides officials with maps, data, tools and resources to easily and effectively discover and then communicate natural hazard risk information to their partners or constituents. FEMA Region III works with a variety of contracted providers and CTP partners who assist in the delivery of Flood Risk Projects or Mapping Projects and other hazard assessment data to Delaware, the District of Columbia, Maryland, Pennsylvania, Virginia, and West Virginia. Integration between Federal, State and Local government leaders increases efficiencies and expands success throughout our project delivery areas.

In order to help contractors and private residents avoid existing utility lines (including gas, electrical, and water lines) when digging, the Chicago Department of Transportation maintains 811 Chicago, a free, 24-hour service to private contractors and homeowners in Chicago. Anyone planning to dig within Chicago must obtain a “dig ticket” from 811 Chicago. 811 Chicago notifies all utilities of the impending excavations. The utility owners then send out staff to mark the location of the underground facilities within 48 hours (excluding emergencies), not counting Saturdays, Sundays, and holidays.

The dataset on which this filtered view is based shows these utility notifications. Since it is common for the same dig ticket to produce multiple notifications, the same dig ticket will appear multiple times and the dataset cannot be used without further refinement to count, map, or analyze unique excavations in Chicago.

This filtered view shows only the columns that should remain constant for a dig ticket and de-duplicates them, Therefore, it should represent a unique list of dig tickets. Because of the technique used, while it is possible to show the LATITUDE and LONGITUDE columns, it is not possible to show the LOCATION column and therefore not possible to create map views directly from this filtered view within the data portal software.

See https://ipi.cityofchicago.org/Digger for more information on the dig ticket system.

For 12 months, air temperature was measured at a height of 2 m above the ground at the upper forest line in the Tatras. The aim of these studies was to show the relationship between terrain forms (concave and convex) on air temperature. The lowest air temperature value was characteristic of concave terrain forms and in them the upper forest line ran lower than on convex terrain forms.

Datasets used in paper: Telesca L. and Z. Czechowski, Fisher–Shannon Investigation of the Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Entropy 2023, 25, 1650.

The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2020 campaign. Between October 2019 and May 2020, data were collected from 454 snow pits at 12 regional locations throughout California, Colorado, Idaho, New Mexico, and Utah, USA. At each of the locations, between 1 and 11 sites covering a range of conditions (terrains, snow depths, etc.) were chosen for weekly snow pit observations. Also available are photos of the field notes and snow pit sites.

For 12 months, air temperature was measured at a height of 2 m above the ground at the upper forest line in the Tatras. The aim of these studies was to show the relationship between terrain forms (concave and convex) on air temperature. The lowest air temperature value was characteristic of concave terrain forms and in them the upper forest line ran lower than on convex terrain forms.

What is expenditure data?

The Queensland Government works closely with all Queenslanders to build safe, caring and connected communities, create jobs, protect our environment and enhance quality of life for present and future generations.

To help achieve this, a number of grant and assistance programs are open to individuals, small community groups and non-government organisations right across the State.

Funding programs cover a range of service areas and interests from sport, arts, and events to education, environment and Advance Queensland research and innovation initiatives.

Expenditure data reflects amounts paid for these grant/funding and frontline service procurement programs. This is a consolidated dataset that can be used by the public, non-government organisations and Government departments to research the investment the Queensland Government makes across grant/funding programs.

The information is correct at the time of reporting however figures and other details may be subject to change. Budgeted and expenditure amounts may also be subject to variation due to changes in project timing, scope or funding reallocations.

Where individuals have received funding, these details have been aggregated within the broader program, funding categories or location, to maintain privacy.

Data available

Please view the Data Dictionary (see Data and resources) to understand the data attributes, quality and limitations of this data which should all be considered before using this data for any purpose.

Due to program name changes, consolidation of funding into other programs, machinery-of-government changes and refinement in the data fields collected overtime, comparison of figures across different years may be ineffective.

Data ownership

This dataset provides a consolidated view of payments made by all Queensland Government agencies responsible for the administration of grant and funding programs. The data contained within this file is not owned by Queensland Treasury.

To view the data as provided by the data owners, please use the link under Related resources. Any questions regarding the data should be directed to the respective Queensland Government agency responsible for the payment. You can contact the responsible agency via the Contact Us option.

Related resources

• Agency Datasets —Queensland Government Investment Portal (QGIP)

The Global Population Count Grid Time Series Estimates provide a back-cast time series of population grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population count grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

The dataset shows, with monthly aggregation, the number of resources modified on the open data portal of the municipality of Milan. Changes to each unique resource are only counted once each month. A resource consists of the representation of the data contained in a dataset, typically exposed in one or more downloadable files (possibly in several different formats).

A public passenger vehicle is a vehicle used for the transportation of passengers for hire by a public chauffeur. This filtered view contains only licensed taxicabs. The dataset of public passenger vehicles on which this view is based includes licensed taxicabs (medallions), liveries, ambulances, medicars, charter-sightseeing buses, horse-drawn carriages, and pedicabs. For more information, please see http://www.cityofchicago.org/city/en/depts/bacp/supp_info/bacppublicvehicles.html.

Each record in this dataset shows information about an arrest executed by the Chicago Police Department (CPD). Source data comes from the CPD Automated Arrest application. This electronic application is part of the CPD CLEAR (Citizen Law Enforcement Analysis and Reporting) system, and is used to process arrests Department-wide.

A more-detailed version of this dataset is available to media by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Arrests Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup. New data fields may be added to this public dataset in the future. Requests for individual arrest reports or any other related data other than access to the more-detailed dataset should be directed to CPD, through contact information on that site or a Freedom of Information Act (FOIA) request.

The data is limited to adult arrests, defined as any arrest where the arrestee was 18 years of age or older on the date of arrest. The data excludes arrest records expunged by CPD pursuant to the Illinois Criminal Identification Act (20 ILCS 2630/5.2).

Department members use charges that appear in Illinois Compiled Statutes or Municipal Code of Chicago. Arrestees may be charged with multiple offenses from these sources. Each record in the dataset includes up to four charges, ordered by severity and with CHARGE1 as the most severe charge. Severity is defined based on charge class and charge type, criteria that are routinely used by Illinois court systems to determine penalties for conviction. In case of a tie, charges are presented in the order that the arresting officer listed the charges on the arrest report. By policy, Department members are provided general instructions to emphasize seriousness of the offense when ordering charges on an arrest report.

Each record has an additional set of columns where a charge characteristic (statute, description, type, or class) for all four charges, or fewer if there were not four charges, is concatenated with the | character. These columns can be used with the Filter function's "Contains" operator to find all records where a value appears, without having to search four separate columns.

Users interested in learning more about CPD arrest processes can review current directives, using the CPD Automated Directives system (http://directives.chicagopolice.org/directives/). Relevant directives include:

• Special Order S06-01-11 – CLEAR Automated Arrest System: describes the application used by Department members to enter arrest data. • Special Order S06-01-04 – Arrestee Identification Process: describes processes related to obtaining and using CB numbers. • Special Order S09-03-04 – Assignment and Processing of Records Division Numbers: describes processes related to obtaining and using RD numbers. • Special Order 06-01 – Processing Persons Under Department Control: describes required tasks associated with arrestee processing, include the requirement that Department members order charges based on severity.

Introduction Daily Demand Statistics (Minimum, Average, Maximum) for Grid Supply Point (GSP). It includes aggregated GSP Data for all the monitored GSPs, and it also includes SGT data if we have used those for the creation of aggregated data. Data includes Voltages, Current, Active Power, and Reactive Power, and is populated from 2021 onwards to the current day. This dataset shows data from a sample of GSPs which account for 1/5th of the total GSPs in our area (we will add more sites across the network in the future). This data is refreshed every day at midnight. The data is published as is from the network.

Methodological Approach The power flow data is streamed from our PI server into an FTP server before being published on the Open Data Portal. To streamline and enable faster data refresh, we have structured our data into a header file which is light.

Quality Control Statement The data is published as is from the network.

Assurance Statement The Open Data Team and DSO Data Science Team worked together to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON) Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: Open Data Portal GlossaryTo view this data please register and login.

The name-bearing types at the RMCA ornithological collection are represented by these unique records: 89 holotypes, 5 lectotypes and 36 syntypes compose the most important part among the 988 type specimens of birds kept in Tervuren. This types’ dataset was based on a digitally updated catalogue (Louette et al. 2010), that was a revision of an earlier detailed inventory (Louette et al. 2002). Thanks to recent comparisons by Louette (2023), we hereby highlight actualised taxonomic findings: - Of 25 bird species, accepted by the International Ornithological Committee (IOC), the AfricaMuseum holds the type specimens, including 12 holotypes and almost all syntypes which are listed in this updated dataset. - A few other birds that have not yet been recognised as distinct species by the IOC (2021), are additionally discussed, of which the three holotypes are indicated here as incipient species in taxon remarks. - Besides, many taxa are listed as accepted subspecies by the IOC for which the RMCA has type material. - Moreover, three birds that were originally considered to be species and once described by holotypes, appear to be melanistic morphs of already known species belonging to other taxa. Paratypes are excluded from this dataset, since only the name-bearing types were photographed and digitised through an earlier project, partially funded by Belspo. Initially, this data mobilisation was situated as a paradigm case in the experimentally Semantic Web-based Thematic European Reference Network Application (STERNA, http://www.sterna-net.eu) that aimed to develop a capacious database on birds according to the objectives of a European Digital Library (EDL) and its portal Europeana (www.europeana.eu). References: Louette, M., D. Meirte, A. Louage & A. Reygel, 2002. Type specimens of birds in the Royal Museum for Central Africa, Tervuren. Doc. Zool. (Mus. R. Afr. Centr.) 26, 105 pp. Louette, M., D. Meirte, A. Louage & A. Reygel, 2010. Type specimens of birds in the Royal Museum for Central Africa, Tervuren. Revised Edition. Zool. Doc. Online Series (R. Mus. Centr. Afr.) 332 pp. Louette, M., 2023. The bird species from the type collection. Royal Museum for Central Africa, Tervuren. Collections of the RMCA. 180 pp. International Ornithological Committee http://www.worldbirdnames.org/ especially IOC 2021 World Bird List 11.2. doi: 10.14344/IOC.ML.11.2

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8