This map contains multibeam sonar survey data collected during the 2021 field project. This file supports the New Technology and the Search for Historic Shipwrecks StoryMap created by the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) and Office of National Marine Sanctuaries (ONMS). The StoryMap can be viewed here. The StoryMap was funded through NOAA's Office of Ocean Exploration and Research. More information on the project can be found here. All project files are stored in the NOAA National Centers for Environmental Information.

This web map is a component of the CrowdMag Visualization App.NOAA's CrowdMag is a crowdsourced data collection project that uses a mobile app to collect geomagnetic data from the magnetometers that modern smartphones use as part of their navigation systems. NCEI collects these data from citizen scientists around the world and provides quality control services before making them available through a series of aggregated maps and charts. These data have the potential to provide a high resolution alternative to geomagnetic satellite data, as well as near real-time information about changes in the magnetic field.This map shows data collected from phones around the world! Displayed are the Crowdsourced magnetic data within a tolerance level of prediction by World Magnetic Model. We have added some uncertainty to each data point shown to ensure the privacy of our contributors. The data points are grouped together (or "aggregated") into small areas , and we display the median data value across all the readings for each point.

This map is updated every day. Layers are available for Median Intensity, Median Horizontal Component (Y), and Median Vertical Component (Z).


Use the time slider to select the date range. Select the different layers under the "Crowdmag Observations" menu. View a color scale using the legend tool. Zoom to your location using the "Find my Location" tool. Click or tap on a data point to view a popup containing more information.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Santa Cruz map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Santa Cruz map area data layers. Data layers are symbolized as shown on the associated map sheets.

Presentation Date: Friday, March 15, 2019. Location: Barnstable, MA. Abstract: A presentation to a crowd of Barnstable High "AstroJunkies," about how we use physics, statistics, and visualizations to turn information from large, public, astronomical data sets, across many wavelengths into a better understanding of the structure of the Milky Way.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of location-based services (LBS) and geographic information systems (GIS) across industries like real estate, tourism, logistics, and urban planning is a major catalyst. Businesses are increasingly leveraging interactive maps to enhance customer engagement, improve operational efficiency, and gain valuable insights from geospatial data. Furthermore, advancements in mapping technologies, including the integration of AI and machine learning for improved data analysis and visualization, are contributing to market growth. The accessibility of user-friendly tools, coupled with the decreasing cost of cloud-based solutions, is also making interactive map creation more accessible to a wider range of users, from individuals to large corporations. However, the market also faces certain challenges. Data security and privacy concerns surrounding the use of location data are paramount. The need for specialized skills and expertise to effectively utilize advanced mapping technologies may also hinder broader adoption, particularly among smaller businesses. Competition among established players like Mapbox, ArcGIS StoryMaps, and Google, alongside emerging innovative solutions, necessitates constant innovation and differentiation. Nevertheless, the overall market outlook remains positive, with continued technological advancements and rising demand for data visualization expected to propel growth in the coming years. Specific market segmentation data, while unavailable, can be reasonably inferred from existing market trends, suggesting a strong dominance of enterprise-grade solutions, but with substantial growth expected from simpler, more user-friendly tools designed for individuals and small businesses.

The dataset includes cartographic visualization data and software designed, implemented, and published for the ARCHITRAVE research project website. The research focused on the edition, executed in German and French, of six travelogues by German travelers of the Baroque period who visited Paris and Versailles. The edited texts are published in the Textgrid repository. For all further information on the content and objectives of the research, please refer to the website (https://architrave.eu/) and given literature. Three visualizations were created for the website: the travel stops of five of the travelers on their way to Paris and Versailles the sites in Europe mentioned in the six travelogues the sites in Paris described by the six travelers The visualizations were implemented with Leaflet.js. The dataset contains scripts for data crunching processed geodata scripts for leaflet.js License README

The global data visualization market, valued at $9.84 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 10.95% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data generated across various industries necessitates effective visualization tools for insightful analysis and decision-making. Furthermore, the rising adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, driving market growth. Advances in artificial intelligence (AI) and machine learning (ML) are integrating seamlessly with data visualization platforms, enhancing automation and predictive capabilities, further stimulating market demand. The BFSI (Banking, Financial Services, and Insurance) sector, along with IT and Telecommunications, are major adopters, leveraging data visualization for risk management, fraud detection, customer relationship management, and network optimization. However, challenges remain, including the need for skilled professionals to effectively utilize these tools and concerns regarding data security and privacy. The market segmentation reveals a strong presence of executive management and marketing departments across organizations, highlighting the strategic importance of data visualization in business operations. The market's competitive landscape is characterized by established players like SAS Institute, IBM, Microsoft, and Salesforce (Tableau), along with emerging innovative companies. This competition fosters innovation and drives down costs, making data visualization solutions more accessible to a broader range of businesses and organizations. Regional variations in market penetration are expected, with North America and Europe currently holding significant shares, but Asia Pacific is poised for substantial growth, driven by rapid digitalization and technological advancements in the region. The on-premise deployment mode still holds a considerable market share, though the cloud/on-demand segment is experiencing faster growth due to its inherent advantages. The ongoing trend towards self-service business intelligence (BI) tools is empowering end-users to access and analyze data independently, increasing the overall market demand for user-friendly and intuitive data visualization platforms. Future growth will depend on continued technological advancements, expanding applications across diverse industries, and addressing the existing challenges related to data skills gaps and security concerns. This report provides a comprehensive analysis of the Data Visualization Market, projecting robust growth from $XX Billion in 2025 to $YY Billion by 2033. It covers the period from 2019 to 2033, with a focus on the forecast period 2025-2033 and a base year of 2025. This in-depth study examines key market segments, competitive landscapes, and emerging trends influencing this rapidly evolving industry. The report is designed for executives, investors, and market analysts seeking actionable insights into the future of data visualization. Recent developments include: September 2022: KPI 360, an AI-driven solution that uses real-time data monitoring and prediction to assist manufacturing organizations in seeing various operational data sources through a single, comprehensive industrial intelligence dashboard that sets up in hours, was recently unveiled by SymphonyAI Industrial., January 2022: The most recent version of the IVAAP platform for ubiquitous subsurface visualization and analytics applications was released by INT, a top supplier of data visualization software. IVAAP allows exploring, visualizing, and computing energy data by providing full OSDU Data Platform compatibility. With the new edition, IVAAP's map-based search, data discovery, and data selection are expanded to include 3D seismic volume intersection, 2D seismic overlays, reservoir, and base map widgets for cloud-based visualization of all forms of energy data.. Key drivers for this market are: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Potential restraints include: Lack of Tech Savvy and Skilled Workforce/Inability. Notable trends are: Retail Segment to Witness Significant Growth.

Additional file 1. Perl script used for converting a contact map into an adjacency matrix based on the graphrepresentation in Fig. 1a.

Biogeoclimatic Ecosystem Classification (BEC) system is the ecosystem classification adopted in the forest management within British Columbia based on vegetation, soil, and climate characteristics whereas Site Series is the smallest unit of the system. The Ministry of Forests, Lands, Natural Resource Operations and Rural Development held under the Government of British Columbia (“the Ministry”) developed a web-based tool known as BEC Map for maintaining and sharing the information of the BEC system, but the Site Series information was not included in the tool due to its quantity and complexity. In order to allow users to explore and interact with the information, this project aimed to develop a web-based tool with high data quality and flexibility to users for the Site Series classes using the “Shiny” and “Leaflet” packages in R. The project started with data classification and pre-processing of the raster images and attribute tables through identification of client requirements, spatial database design and data cleaning. After data transformation was conducted, spatial relationships among these data were developed for code development. The code development included the setting-up of web map and interactive tools for facilitating user friendliness and flexibility. The codes were further tested and enhanced to meet the requirements of the Ministry. The web-based tool provided an efficient and effective platform to present the complicated Site Series features with the use of Web Mapping System (WMS) in map rendering. Four interactive tools were developed to allow users to examine and interact with the information. The study also found that the mode filter performed well in data preservation and noise minimization but suffered from long processing time and creation of tiny sliver polygons.

NDS is an interactive, web-based system, for the visualization of multidimensional neighborhood dynamics across the 50 largest US Metropolitan Statistical Areas (MSAs) from 1980 to 2010 (http://neighborhooddynamics.dreamhosters.com). Four different visualization tools are developed: (1) an interactive time slider to show neighborhood classification changes for different years; (2) multiple interactive bar charts for each variables of each neighborhood; (3) an animated neighborhood’s trajectory and sequence cluster on a self-organizing map (SOM) output space; and (4) a synchronized visualization tool showing maps for four time stamps at once. The development of this interactive online platform for visualizing dynamics overcomes many of the challenges associated with communicating changes for multiple variables, across multiple time stamps, and for a large geographic area when relying upon static maps. The system enables users to select and dive into details on particular neighborhoods and explore their changes over time.

Visual map at kumu.io/access2perspectives/covid19-resources

Data set doi: 10.5281/zenodo.3732377 // available in different formats (pdf, xls, ods, csv,)

Correspondence: (JH) info@access2perspectives.com

Objectives

Provide citizens with crucial and reliable information

Encourage and facilitate South South collaboration

Bridging language barriers

Provide local governments and cities with lessons learned about COVID-19 crisis response

Facilitate global cooperation and immediate response on all societal levels

Enable LMICs to collaborate and innovate across distances and leverage locally available and context-relevant resources

Methodology

The data feeding the map at kumu.io was compiled from online resources and information shared in various community communication channels.

Kumu.io is a visualization platform for mapping complex systems and to provide a deeper understanding of their intrinsic relationships. It provides blended systems thinking, stakeholder mapping, and social network analysis.

Explore the map // https://kumu.io/access2perspectives/covid19-resources#global

Click on individual nodes and view the information by country

info hotlines

governmental informational websites, Twitter feeds & Facebook pages

fact checking online resources

language indicator

DIY resources

clinical staff capacity building

etc.

With the navigation buttons to the right, you can zoom in and out, select and focus on specific elements.

If you have comments, questions or suggestions for improvements on this map email us at info@access2perspectives.com

Contribute

Please add data to the spreadsheet at https://tinyurl.com/COVID19-global-response

you can add additional information on country, city or neighbourhood level (see e.g. the Cape Town entry)

Related documents

Google Doc: tinyurl.com/COVID19-Africa-Response

Dataset

This dataset was created by David Maillie

Released under CC BY-SA 4.0

Contents

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

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

The Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (smis_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

Description

This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation methods. Each project has been classified according to visualisation and interaction techniques, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.

Classification schema: categories and columns

The project_id column contains unique internal identifiers assigned to each project. Meanwhile, the last_access column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the url column.
The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:

Narrativity. It reports the presence of narratives employing information visualisation techniques. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is determined by user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:

• non_narrative (boolean)

• narrative (boolean)

Domain. The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:

• domain (categorical):

• History and archaeology

• Art and art history

• Language and literature

• Music and musicology

• Multimedia and performing arts

• Philosophy and religion

• Other: both extra-list domains and cases of collections without a unique or specific thematic focus.

Visualisation of uncertainty and interpretation. Buiding upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise methods explicitly represent quantifiable uncertainty such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction techniques (usually filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:

• uncertainty_interpretation (categorical):

• Interactive distinction

• Visual distinction

• Ambiguation

• Interpretative metrics

Critical adaptation. We identify projects in which, with regards to at least a visualisation, the following criteria are fulfilled: 1) avoid repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid simplifications to embrace and depict complexity, promoting time-consuming visualisation-based inquiry. Column:

• critical_adaptation (boolean)

Non-temporal visualisation techniques. We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:

• plot (boolean): visual representations that map data points onto a two-dimensional coordinate system.

• cluster_or_set (bool): sets or cluster-based visualisations used to unveil possible inter-object similarities.

• map (boolean): geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.

• network (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.

• hierarchical_diagram (boolean): tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.

• treemap (boolean): still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.

• word_cloud (boolean): clouds of words, where each instance’s size is proportional to its frequency in a related context

• bars (boolean): includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.

• line_chart (boolean): the display of information as sequential data points connected by straight-line segments.

• area_chart (boolean): similar to a line chart but with a filled area below the segments. It also includes density plots.

• pie_chart (boolean): circular graphs divided into slices which can also use multi-level solutions.

• plot_3d (boolean): plots that use a third dimension to encode an additional variable.

• proportional_area (boolean): representations used to compare values through area size. Typically, using circle- or square-like shapes.

• other (boolean): it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.

Temporal visualisations and encodings. In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:

• timeline (boolean): the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [7], we also include structured solutions resembling Gantt chart layouts.

• temporal_dimension (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.

• animation (boolean): temporality is perceived through an animation changing the visualisation according to time flow.

• visual_variable (boolean): another visual encoding strategy is used to represent any temporality-related variable (e.g., colour).

Interaction techniques. A set of categories to assess affordable interaction techniques based on the concept of user intent [8] and user-allowed data actions [9]. The following categories roughly match the “processing”, “mapping”, and “presentation” actions from [9] and the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the visual representation or the aspect of data points, symbols, and glyphs are taken into consideration. Columns:

• basic_selection (boolean): the demarcation of an element either for the duration of the interaction or more permanently until the occurrence of another selection.

• advanced_selection (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.

• navigation (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view but only when applied to the visualisation and not to the web page. It also includes “drill” interactions (to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original) and “expand” interactions generating new perspectives on data by expanding and collapsing nodes.

• arrangement (boolean): methods to organise visualisation elements (symbols, glyphs, etc.) or multi-visualisation

The Data Asset Map System market is experiencing robust growth, driven by the increasing need for organizations to gain a comprehensive understanding of their data assets. This allows for better data governance, improved data quality, and more efficient utilization of data for informed decision-making. The market is segmented by application (Data Governance and Intelligent Analytics Engine) and deployment type (Cloud-based and Local Deployment). The cloud-based segment is witnessing faster adoption due to its scalability, cost-effectiveness, and ease of access. Intelligent analytics engines are becoming increasingly important as organizations seek to derive actionable insights from their data assets, driving demand for sophisticated mapping systems. Key players in the market include Primeton, Bynder, and WoodWing, along with several other prominent vendors. The market's growth is fueled by the rising volume and complexity of data, coupled with stricter data regulations and the growing adoption of digital transformation initiatives. The North American market currently holds a significant share due to the region's advanced technological infrastructure and high adoption rates, followed by Europe and Asia Pacific. The market's expansion is projected to continue at a healthy CAGR (let's assume a conservative 15% based on typical growth rates in the data management sector) throughout the forecast period (2025-2033). However, challenges remain, including the initial investment costs associated with implementing such systems and the need for skilled professionals to manage and interpret the generated data maps. Despite these restraints, the long-term outlook for the Data Asset Map System market remains positive, driven by ongoing technological advancements and the increasing strategic importance of data within organizations. This includes the development of more user-friendly interfaces and integration with existing business intelligence tools, making the systems more accessible and valuable to a wider range of businesses.

This data contains Q and A, Key Influencers, Map, Matrix, Dashboard

In 2012, GreyNet published a page on its website and made accessible the first edition of IDGL, International Directory of Organizations in Grey Literature . The latest update of this PDF publication was in August 2016, providing a list of some 280 organizations in 40 countries worldwide that have contact with the Grey Literature Network Service. The listing appears by country followed by the names of the organizations in alphabetical order, which are then linked to a URL. This year GreyNet International marks its Twenty Fifth Anniversary and seeks to more fully showcase organizations, whose involvement in grey literature is in one or more ways linked to GreyNet.org. Examples of which include: members, partners, conference hosts, sponsors, authors, service providers, committee members, associate editors, etc. This revised and updated edition of IDGL will benefit from the use of visualization software mapping the cities in which GreyNet’s contacts are located. Behind each point of contact are a number of fields that can be grouped and cross-tabulated for further data analysis. Such fields include the source, name of organization, acronym, affiliate’s job title, sector of information, subject/discipline, city, state, country, ISO code, continent, and URL. Eight of the twelve fields require input, while the other four fields do not. The population of the study was derived by extracting records from GreyNet’s in-house, administrative file. Only recipients on GreyNet’s Distribution List as of February 2017 were included. The records were then further filtered and only those that allowed for completion of the required fields remained. This set of records was then converted to Excel format, duplications were removed, and further normalization of field entries took place. In fine, 510 records form the corpus of this study. In the coming months, an in-depth analysis of the data will be carried out - the results of which will be recorded and made visually accessible. The expected outcome of the project will not only produce a revised, expanded, and updated publication of IDGL, but will also provide a visual overview of GreyNet as an international organization serving diverse communities with shared interests in grey literature. It will be a demonstration of GreyNet’s commitment to research, publication, open access, education, and public awareness in this field of library and information science. Finally, this study will serve to pinpoint geographic and subject based areas currently within as well as outside of GreyNet’s catchment.