Research data and scientific software related to: Schröder W. Nickel S, Jenssen M, Riediger J 2015. Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: a pilot study in Germany. Science of the Total Environment 521-522:108-122

Datasets contain original and revised Index of Watershed Integrity (IWI) and Index of Catchment Integrity (ICI), as well as sub-components that were used to develop the indices and water quality data used to revise and/or evaluate the indices. This dataset is associated with the following publication: Johnson, Z.C., S. Leibowitz, and R.A. Hill. Revising the index of watershed integrity national maps. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 651: 2615-2630, (2018).

These are the discrete sampling locations brought out of the Water Quality Sampling Data dataset [https://data.austintexas.gov/Environmental/Water-Quality-Sampling-Data/5tye-7ray] for ease of mapping. SampleSiteNo in this table maps to SAMPLE_SITE_NO in the larger dataset. Note that not all samples in the larger dataset have a match in this table ... this table only contains sampling locations with valid latitude/longitude values. Reasons for samples not having a valid physical location: the data represents a non-spatial object like a product or a lab standard or blank; the data was collected at a protected karst feature; the data was collected prior to GIS or GPS and the information never existed or was lost.

Community engagement and involvement have been increasingly recognized as an ethical and valuable component of health science research over the past two decades. Progress has been accompanied by emerging standards that emphasize participation, two-way communication, inclusion, empowerment, and ownership. Although these are important and noble benchmarks, they can represent a challenge for research conducted in marginalized contexts. This community case study reports on the methods, outcomes, constraints and learning from an NGO-led community engagement project called Bucket Loads of Health, implemented in the Western Cape province of South Africa. The independent project team used multiple participatory visual methods to foster two-way communication between members of two disenfranchised communities, Enkanini and Delft, and a group of water microbiologists at Stellenbosch University who were conducting research in Enkanini. The project was carried out during the 2018 Western Cape water crisis, under the growing threat of “Day Zero”. The resulting visual outputs illustrated the negative impacts of water shortage on health and wellbeing in these community settings and showcased scientific endeavors seeking to address them. Engagement included knowledge exchange combining body maps, role play performances and films created by the community members, with hand maps, posters and presentations produced by the scientists. Whereas these engagement tools enabled reciprocal listening between all groups, their ability to respond to the issues raised was hindered by constraints in resources and capacity beyond their control. An additional core objective of the project was to bring the impacts of water shortage in participating communities, and the work of the research team, to the attention of local government. The case study demonstrates the challenges that politically ambitious community engagement faces in being acknowledged by government representatives. We further the argument that research institutions and funders need to match professed commitments to engagement with training and resources to support researchers and community members in responding to the needs and aspirations surfaced through engagement processes. We introduce the concept of engagement integrity to capture the gap between recommended standards of community engagement and what is realistically achievable in projects that are constrained by funding, time, and political interest.

This record presents the data underlying Skills4EOSC Deliverable D6.1 Mapping of existing professional networks and relevant documentation of the search string.

This EnviroAtlas dataset is a point feature class showing the locations of stream confluences, with attributes showing indices of ecological integrity in the upstream catchments and watersheds of stream confluences and the results of a cluster analysis of these indices. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Watershed Integrity MethodologySee also: https://www.cmap.illinois.gov/2050/maps/watershed

The HD Map market for autonomous driving is experiencing explosive growth, projected to reach $2035.9 million by 2035, fueled by a remarkable Compound Annual Growth Rate (CAGR) of 50.9%. This surge is driven by the increasing demand for safer and more efficient autonomous vehicles. Key factors contributing to this growth include advancements in sensor technology (LiDAR, radar, cameras), the development of sophisticated mapping algorithms, and the rising investments from both established automotive players and technology giants. The market's expansion is further propelled by the increasing adoption of Level 3 and above autonomous driving features, requiring highly accurate and detailed HD maps for reliable vehicle navigation and decision-making. Competition is fierce, with established players like TomTom, Google, and Baidu vying for market share alongside emerging companies specializing in high-definition mapping solutions. The market is segmented by map type (lane-level, point cloud), data update frequency, and application (passenger vehicles, commercial vehicles). Geographic expansion is expected across North America, Europe, and Asia, with regions experiencing rapid technological advancements and robust autonomous vehicle adoption leading the way. The restraints on market growth primarily include the high cost of data acquisition and map production, the challenges of maintaining map accuracy in dynamic environments (e.g., construction zones, weather changes), and the need for robust cybersecurity measures to protect the integrity of the mapping data. However, the long-term potential of this market is substantial, as the widespread adoption of autonomous driving technology becomes increasingly inevitable. Continuous improvements in mapping technologies, coupled with decreasing costs, are expected to alleviate some of these current constraints, paving the way for even more rapid market expansion in the coming years. The collaborative efforts between mapping companies, automotive manufacturers, and technology providers will be crucial in shaping the future trajectory of this dynamic market.

Math equations for tag generation.

Data Mapping Tool Market Outlook

The global data mapping tool market size was valued at $1.5 billion in 2023 and is projected to reach $4.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.4% during the forecast period. The market is driven by the increasing need for efficient data management and integration solutions across various industries, coupled with the rising volume of data generated globally.

One of the primary growth factors for the data mapping tool market is the surge in data generation across various sectors, including healthcare, finance, and retail. Organizations are increasingly recognizing the importance of data accuracy and integrity for decision-making processes, driving the demand for advanced data mapping tools. These tools help in transforming and integrating data from different sources into a unified structure, ensuring seamless data flow and enhanced analytical capabilities. Furthermore, the rapid adoption of cloud-based solutions has simplified the deployment and scalability of data mapping tools, making them more accessible to businesses of all sizes.

Another significant driver of the market is the growing emphasis on regulatory compliance and data governance. Governments and regulatory bodies across the globe are implementing stringent data protection and privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations necessitate accurate data mapping to ensure compliance, thereby fueling the demand for data mapping tools. Additionally, industries like BFSI and healthcare, which handle sensitive and critical data, are increasingly investing in data mapping solutions to maintain data integrity and comply with regulatory standards.

The proliferation of big data and the rising adoption of advanced analytics and business intelligence (BI) tools are also contributing to the growth of the data mapping tool market. As organizations strive to leverage data for strategic insights and competitive advantage, the need for robust data integration and mapping solutions becomes imperative. Data mapping tools play a crucial role in preparing and structuring data for analysis, enabling organizations to derive actionable insights and drive business growth. Moreover, the advent of artificial intelligence (AI) and machine learning (ML) technologies is further enhancing the capabilities of data mapping tools, making them more efficient and intelligent.

In the evolving landscape of data management, Data Virtualization Tools have emerged as a pivotal solution for organizations seeking to streamline their data integration processes. These tools offer a unified view of data from disparate sources without the need for physical data movement, thus enhancing the efficiency of data mapping solutions. By leveraging data virtualization, businesses can access and manipulate data in real-time, enabling more agile and informed decision-making. This capability is particularly beneficial in industries with complex data environments, such as finance and healthcare, where timely insights are crucial. As organizations continue to embrace digital transformation, the integration of data virtualization tools with data mapping solutions is expected to drive further innovation and growth in the market.

Regionally, North America holds a significant share of the data mapping tool market, driven by the presence of major technology companies and early adopters of advanced data solutions. The region's robust IT infrastructure, coupled with high investments in data management technologies, is propelling market growth. Europe follows closely, with a strong focus on data privacy and regulatory compliance driving the adoption of data mapping tools. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digital transformation and increasing adoption of cloud-based solutions in countries like China and India.

Component Analysis

The data mapping tool market is segmented by component into software and services. The software segment is anticipated to dominate the market throughout the forecast period, driven by the increasing demand for advanced data integration and transformation solutions. Data mapping software enables organizations to efficiently map, transform, and integrate data from multiple sources, ensuring data consistency and accuracy. The rising adoption of cloud-based solutions is f

These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577).

The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16) navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.

The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.

In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.

Acknowledgements

This research was funded by The Wilderness Society.

Contact

Further queries regarding these datasets can be directed to Ruben Venegas (r.venegas@uq.edu.au) and James Watson (james.watson@uq.edu.au).

Protein-Protein, Genetic, and Chemical Interactions for Tahirovic S (2003):Role for lipid signaling and the cell integrity MAP kinase cascade in yeast septum biogenesis. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Polarized deposition of chitin at the bud neck is essential for cell separation in yeast. Chitin septum biogenesis is catalyzed by two distinct chitin synthase activities encoded by the CHS2 and CHS3 genes. The phosphoinositide phosphatase Sac1p is required for proper trafficking of the Chs3p chitin synthase. sac1 mutants also display a severe synthetic growth defect, with mutations in the SLT2 gene which encodes a MAP kinase involved in cell integrity. We characterized the defect that underlies this genetic interaction and found that sac1 Delta slt2 Delta cells arrest as large-budded cells because they fail to separate at the end of mitosis. This inability to complete cell division appears to be caused by an increased deposition of chitin at the septum area and correlates with a mislocalized accumulation of the Chs2p chitin synthase at the cell periphery. Our data therefore indicate that Sac1p and Slt2p have synergistic roles in regulating chitin septum biogenesis.

This dataset contains key characteristics about the data described in the Data Descriptor Global humid tropics forest structural condition and forest structural integrity maps. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 


      Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

Soil and landscape mapping was collated from Western Australia, South Australia, Victoria, and New South Wales, in combination with latest Digital Soil Mapping products for Australia (Soil and Landscape Grid of Australia) as the basis for a new sandy soils map. A staged map compilation process was undertaken to combine all these available datasets into one uniform map that retains integrity of legacy contextual mapping information.

The key steps undertaken in the mapping of sandy soils include: 1. Define an agricultural region area of interest for this study; 2. Collate available soil-landscape mapping datasets across Australia (including state and national); 3. Assemble and edit existing mapping to form a new sandy soil map for agricultural regions of the study area; 4. Review and revise this mapping in response to feedback from NCST members including state/territory experts.

Maps were revised and updated with input from members of the Digital Soil Assessment Working Group and members of the National Committee on Soil and Terrain. While efforts were made to include these suggestions, it was not possible to refine the map indefinitely, and therefore editing ceased on the 23^rd of February 2021. Due to the variations in scale, mapping techniques, representation, and attribution across Australia, the use of these maps for such purposes as mapping sandy soils across southern Australia proved difficult.

From the new sandy soils map we were able to identify agricultural areas of sandy soils: (Western Australia - 10.611Mha; South Australia - 2.479Mha; New South Wales - 1.867Mha; Victoria - 0.864Mha and Tasmania - 0.215Mha). Nationally there were 16.039Mha of sandy soil identified which is considerably higher than the 11Mha from previous estimates.

This research is funded by the CRC for High Performance Soils and supported by the Cooperative Research Centres program, an Australian Government initiative.

Additional funding and in-kind support are provided by: Murdoch University, PIRSA, Federation University Australia, West Midlands Group and AORA. Contributions from Richard Bell, Amanda Schapel and David Davenport have been critical in shaping the logic and key considerations in mapping sandy soils and benefits of amelioration. James Hall is also thanked for providing insights into sandy soils for South Australia and the formation of the new Arenosol soil order for Australia.

We would also like to acknowledge the contributions of the Digital Soil Assessment Working Group and members of the National Committee on Soil and Terrain that provided valuable feedback on the approach used to map sandy soils.

Administrative and structural details on data files:

• A shapefile (Sandy_soil_map_aglands.shp) is provided for use in a Geographic Information System (GIS). This should be useable in commercial (e.g. ArcGIS) and open source software packages (e.g. QGIS). The shapefile data coordinate system is WGS1984 geographic.
• A RTF file is also provided which includes information on the data fields and content of the shapefile for users. Note that abbreviations for the Australian Soil Classification Order and Suborder fields (as 2021) were used.

Associated publication:

Robinson N, Pope R, Liddicoat C, Holmes K, Griffin E, Kidd D, Jenkins B, Rees D, Searle R. (2021) Sandy Soils: Organic and clay amendments to improve the productivity of sandy soils. Detailed plan for mapping and grouping of sands. Soil CRC Project 3.3.003. Cooperative Research Centre for High Performance Soils.

The New Jersey Department of Environmental Protection (NJDEP) Bureau of Freshwater and Biological Monitoring (BFBM) performs monitoring on non-tidal freshwater streams and rivers throughout the state using fish as biological indicators of stream health. This data is used for a wide variety of purposes, including the evaluation of aquatic life use assessment for the federally required NJ Integrated Water Quality Assessment Report and the designation of Category One antidegradation classification based on exceptional ecological significance. BFBM has established fish bioassessment protocols for three different stream types in New Jersey. The Bureau initiated Fish Index of Biotic Integrity (IBI) monitoring in 2000 following the development of the Northern Fish IBI by U.S. EPA Region 2 which was based on the EPA’s Rapid Bioassessment Protocols (RBP; USEPA 1999). This, the longest fish monitoring program in the NJDEP Division of Water Monitoring and Standards (DWMS), monitors resident fish assemblages in wadable streams larger than 4-square miles in drainage area. The Southern Fish IBI was developed by BFBM in 2012 for low gradient streams in the Inner Coastal Plain eco-region of NJ. Lastly, after several years of research and analysis by the Philadelphia Academy of Natural Sciences of Drexel University and BFBM, the Headwaters IBI was completed in 2014. This program is used to monitor small first and second order streams less than 4 square miles in drainage area within the same eco-regions of Northern New Jersey as the Northern Fish IBI. The two northern programs differ not only in the size of stream monitored, but also in the assemblages monitored. The Northern Fish IBI is solely a fish-based index, whereas the Headwaters IBI uses fish, crayfish, and streamside amphibians as bio-indicators.

The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

Systematic mapping of academic integrity literature and Artificial Intelligence.

Details of assurance activities, mapping integrity checks, seedpoint checks and quality of correspondence checks.

Comparing the DIA-MTTP with existing works against the efficiency requirements in section.

Number of encrypted data blocks and their associated tags: With/without the data deduplication approach.