In 2015, the second of several Regional Stream Quality Assessments (RSQA) was done in the southeastern United States. The Southeast Stream Quality Assessment (SESQA) was a study by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA) project. One of the objectives of the RSQA, and thus the SESQA, is to characterize the relationships between water-quality stressors and stream ecology and subsequently determine the relative effects of these stressors on aquatic biota within the streams (Van Metre and Journey, 2014). To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. This dataset represents the 115 water-chemistry sites sampled for the SESQA, and is one of the four fundamental geospatial data layers that were developed for the Southeast study.

The Open Government Data portals (OGD) thanks to the presence of thousands of geo-referenced datasets, containing spatial information, are of extreme interest for any analysis or process relating to the territory. For this to happen, users must be enabled to access these datasets and reuse them. An element often considered hindering the full dissemination of OGD data is the quality of their metadata. Starting from an experimental investigation conducted on over 160,000 geospatial datasets belonging to six national and international OGD portals, this work has as its first objective to provide an overview of the usage of these portals measured in terms of datasets views and downloads. Furthermore, to assess the possible influence of the quality of the metadata on the use of geospatial datasets, an assessment of the metadata for each dataset was carried out, and the correlation between these two variables was measured. The results obtained showed a significant underutilization of geospatial datasets and a generally poor quality of their metadata. Besides, a weak correlation was found between the use and quality of the metadata, not such as to assert with certainty that the latter is a determining factor of the former.

The dataset consists of six zipped CSV files, containing the collected datasets' usage data, full metadata, and computed quality values, for about 160,000 geospatial datasets belonging to the three national and three international portals considered in the study, i.e. US (catalog.data.gov), Colombia (datos.gov.co), Ireland (data.gov.ie), HDX (data.humdata.org), EUODP (data.europa.eu), and NASA (data.nasa.gov).

Data collection occurred in the period: 2019-12-19 -- 2019-12-23.

The header for each CSV file is:

[ ,portalid,id,downloaddate,metadata,overallq,qvalues,assessdate,dviews,downloads,engine,admindomain]

where for each row (a portal's dataset) the following fields are defined as follows:

• portalid: portal identifier
• id: dataset identifier
• downloaddate: date of data collection
• overallq: overall quality values computed by applying the methodology presented in [1]
• qvalues: json object containing the quality values computed for the 17 metrics presented in [1]
• assessdate: date of quality assessment
• dviews: number of total views for the dataset
• downloads: number of total downloads for the dataset (made available only by the Colombia, HDX, and NASA portals)
• engine: identifier of the supporting portal platform: 1(CKAN), 2 (Socrata)
• admindomain: 1 (national), 3 (international)
• metadata: the overall dataset's metadata downloaded via API from the portal according to the supporting platform schema

[1] Neumaier, S.; Umbrich, J.; Polleres, A. Automated Quality Assessment of Metadata Across Open Data Portals.J. Data and Information Quality2016,8, 2:1–2:29. doi:10.1145/2964909

Under the Open Government Action Plan, and related National Action Plan, the FGP is required to report on its commitments related to: supporting a user-friendly open government platform; improving the quality of open data available on open.canada.ca; and reviewing additional geospatial datasets to assess their quality. This report summarizes the FGP’s action on meeting these commitments.

This dataset provides indicators for 249 public green spaces (at least 0.25 ha) in the city of Leipzig, Germany, and total scores for natural elements, built elements (infrastructure) and context, as well as an total quality score on the individual park level representing the potential supply of recreational ecosystem services. The quality score is also depicted in regular hexagons of 0.25 square kilometers. All data is supplement to the original linked publication.

List of data and content

• Parks_Indicators: vector files (*.shp, *.geojson), field description (*.csv)
• Parks_Scores: vector files (*.shp, *.geojson), field description (*.csv)
• Parks_Scores_RGB: raster file (*.tif), readme file (*.txt)
• QualityScore_Hexagons: vector files (*.shp, *.geojson), readme file (*.txt)

Spatial reference
All data is projected in ETRS 1989 UTM Zone 33N (EPSG:25833)

Web-GIS
View data and explore interactively using this online application.

Data sources and processing
For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1

Acknowledgments
We thank Terra Concordia and the City of Leipzig for providing data. We greatly acknowledge OpenStreetMap and contributers for providing important parts of the data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A).

Related publication
Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). https://doi.org/10.5751/ES-12485-260245

Dataset Title: OpenStreetMap Quality Assurance Dataset

Dataset Description: This dataset comprises OpenStreetMap (OSM) data obtained from the Dublin area in 2023, specifically for quality assurance purposes. The dataset contains a diverse range of geospatial information, meticulously sourced from OSM through the Overpass API.

Data Source: The primary source of this dataset is OpenStreetMap, accessed via the Overpass API. It encompasses a wide array of geospatial features and attributes contributed by the OSM community.

Data Format: The dataset is formatted in GeoJSON, a widely used and versatile format for representing geospatial data.

Data Size: The dataset encompasses 471 individual records, collectively forming a comprehensive representation of the Dublin area within the scope of the year 2023.

Data License: The dataset is released under the Open Database License (ODbL), ensuring openness and accessibility to users while respecting OSM's data sharing principles.

Temporal and Spatial Coverage: The dataset captures geospatial information within the vibrant city of Dublin, offering a snapshot of the region during the year 2023. It provides valuable insights into the dynamic nature of the city's geographical data.

This dataset serves as a valuable resource for quality assurance and evaluation of geospatial data within the Dublin area. Researchers, GIS professionals, and the broader OSM community can utilize it for a variety of spatial analysis and data quality assessment tasks.

In 2011, the U.S. Geological Survey, in cooperation with the Town of Newfield and the Tompkins County Planning Department, began a study of the stratified-drift aquifers in the West Branch Cayuga Inlet and Fish Kill valleys in the Town of Newfield, Tompkins County, New York. The objective of this study was to characterize the hydrogeology and water quality of the stratified-drift aquifers in the West Branch Cayuga Inlet and Fish Kill valleys and produce a summary report of the findings. This dataset contains locations geologic cross sections used in the study in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York.

GIS quality control checks are intended to identify issues in the source data that may impact a variety of9-1-1 end use systems.The primary goal of the initial CalOES NG9-1-1 implementation is to facilitate 9-1-1 call routing. Thesecondary goal is to use the data for telephone record validation through the LVF and the GIS-derivedMSAG.With these goals in mind, the GIS QC checks, and the impact of errors found by them are categorized asfollows in this document:Provisioning Failure Errors: GIS data issues resulting in ingest failures (results in no provisioning of one or more layers)Tier 1 Critical errors: Impact on initial 9-1-1 call routing and discrepancy reportingTier 2 Critical errors: Transition to GIS derived MSAGTier 3 Warning-level errors: Impact on routing of call transfersTier 4 Other errors: Impact on PSAP mapping and CAD systemsGeoComm's GIS Data Hub is configurable to stop GIS data that exceeds certain quality control check error thresholdsfrom provisioning to the SI (Spatial Interface) and ultimately to the ECRFs, LVFs and the GIS derivedMSAG.

Summary: Database of confusion matrices retrieved from scientific literature. Suitable for research on the creation and explotation of the confusion matrix that remain as interestint topics, such as new tools, sampling design, indices derived from the matrix, proposals in testing statistical hypotheses and so on.Format: Microsoft Access

In 2013, the first of several Regional Stream Quality Assessments (RSQA) was done in the Midwest United States. The Midwest Stream Quality Assessment (MSQA) was a collaborative study by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA), the USGS Columbia Environmental Research Center, and the U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). One of the objectives of the RSQA, and thus the MSQA, is to characterize the relationships between water-quality stressors and stream ecology and to determine the relative effects of these stressors on aquatic biota within the streams (U.S. Geological Survey, 2012). To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. This dataset defines the geographic extent of the MSQA, and is one of the four fundamental geospatial data layers that were developed for the Midwest study.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The global GIS data collector market is experiencing robust growth, driven by increasing adoption of precision agriculture, expanding infrastructure development projects, and the rising demand for accurate geospatial data across various industries. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $4.2 billion by 2033. Key drivers include the increasing availability of affordable and high-precision GPS technology, coupled with advancements in data processing and cloud-based solutions. The integration of GIS data collectors with other technologies, such as drones and IoT sensors, is further fueling market expansion. The demand for high-precision GIS data collectors is particularly strong in sectors like surveying, mapping, and construction, where accuracy is paramount. While the market faces challenges such as high initial investment costs and the need for specialized expertise, the overall growth trajectory remains positive. The market is segmented by application (agriculture, industrial, forestry, and others) and by type (general precision and high precision). North America and Europe currently hold significant market shares, but the Asia-Pacific region is anticipated to experience rapid growth in the coming years due to substantial infrastructure development and increasing government investments in geospatial technologies. The competitive landscape is characterized by both established players like Trimble, Garmin, and Hexagon (Leica Geosystems) and emerging companies offering innovative solutions. These companies are constantly innovating, integrating advanced technologies like AI and machine learning to enhance data collection and analysis capabilities. This competition is driving down prices and improving product quality, benefiting end-users. The increasing use of mobile GIS and cloud-based data management solutions is also transforming the industry, making data collection and analysis more accessible and efficient. Future growth will be largely influenced by the advancement of 5G networks, enabling faster data transmission and real-time applications, and the increasing adoption of automation and AI in data processing workflows. Furthermore, government regulations promoting the use of accurate geospatial data for sustainable development and environmental monitoring are creating new opportunities for the market’s expansion.

The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.

The U.S. Geological Survey in cooperation with the New York State Department of Environmental Conservation, the Tug Hill Commission, the Jefferson County Soil and Water Conservation District, the Oswego County Soil and Water Conservation District, and the Tug Hill Land Trust studied the northern and central parts of the Tug Hill glacial aquifer to better understand and explain the dynamics of the aquifer to help communities make sound policy decisions about groundwater use. This dataset includes aquifer boundaries, discharge and water quality sites, geologic sections, records of selected wells, surface water temperature sites, generalized surficial geology, and water level contours for the northern and central parts of the Tug Hill aquifer. The southern part of the aquifer was beyond the scope of the study.

This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Geospatial Data insights are sourced through partnerships with tier-1 app developers and a unique data collection method. The low-latency delivery ensures real-time insights, setting us apart and providing unparalleled benefits and use cases for Location Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. Clear and compliant privacy notices accompany our data collection process. Opt-in/out management empowers users over data distribution.

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Geospatial data about Data quality info for GDD. Export to CAD, GIS, PDF, CSV and access via API.

This dataset and the accompanying Data Series report was created to assist in analysis and interpretation of water-quality data provided by the U.S. Geological Survey, National Stream Quality Accounting Network (NASQAN) and the National Monitoring Network (NMN). The report describes the methods used to develop the geospatial data which was primarily derived from the National Watershed Boundary Dataset (WBD12). The geospatial data contains polygon shapefiles of basin boundaries for 33 NASQAN and 5 NMN stations. In addition, 30 polygon shapefiles of the closed and noncontributing basins contained within the NASQAN or NMN boundaries where applicable are included. Also included is a point shapefile of the NASQAN and NMN gaging stations and associated basin and station attributes. The basin boundaries included in this dataset are for those sites implemented under the October 2007 design for the 5-years from 2008-2013 (http://water.usgs.gov/nasqan/).

ODS (Open Document Spreadsheet) which only contains numeric data from a set of confusion matrices (one sheet per matrix).It is the same quantitative data stored in a field of a table in the database. Only is provided as a complement to the database in order to access to the quantitative data in a more convenient format.

From 2013 to 2018, the U.S. Geological Survey, in cooperation with the Town of Enfield and the Tompkins County Planning Department, studied the unconsolidated aquifers in the Enfield Creek Valley in the town of Enfield, Tompkins County, New York. The objective of this study was to characterize the hydrogeology and water quality of the unconsolidated aquifers in the Enfield Creek valley and produce a summary report of the findings. The spatial extent and hydrogeologic framework of these unconsolidated aquifers were delineated using existing data, including soils maps, well records, geologic logs, topographic data, and published reports. An interactive ArcGIS Online web map of the geospatial datasets is available here: https://usgs.maps.arcgis.com/home/webmap/viewer.html?webmap=b53518b0b6b74694932605c4578c00c3. These geospatial datasets support U.S. Geological Survey Scientific Investigations Report 2019-5136, "Geohydrology and Water Quality of the Unconsolidated Aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York."

This Data Release contains various types of hydrologic and geologic data from the Pecos River Basin during 1900–2015, including water-quality data compiled and synthesized from various sources (including data from water-quality samples collected by the USGS in 2015 from 26 sites), streamflow gain-loss data collected by the USGS in 2015 and historical streamflow gain-loss data compiled from the literature, the horizontal extent of and depth to the base of the 16 geologic and hydrogeologic units that underlie the study area, and geospatial data collected by the USGS. The data were used to complete a detailed salinity assessment of the Pecos River from Santa Rosa Lake, New Mexico to the Confluence of the Pecos River and the Rio Grande, Texas.