The vertical land change activity focuses on the detection, analysis, and explanation of topographic change. These detection techniques include both quantitative methods, for example, using difference metrics derived from multi-temporal topographic digital elevation models (DEMs), such as, light detection and ranging (lidar), National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and Interferometric Synthetic Aperture Radar (IFSAR), and qualitative methods, for example, using multi-temporal aerial photography to visualize topographic change. The geographic study area of this activity is Perry County, Kentucky. Available multi-temporal lidar, NED, SRTM, IFSAR, and other topographic elevation datasets, as well as aerial photography and multi-spectral image data were identified and downloaded for this study area county. Available mine maps and mine portal locations were obtained from the Kentucky Mine Mapping Information System, Division of Mine Safety, 300 Sower Boulevard, Frankfort, KY 40601 at http://minemaps.ky.gov/Default.aspx?Src=Downloads. These features were used to spatially locate the study areas within Perry County. Previously developed differencing methods (Gesch, 2006) were used to develop difference raster datasets of NED/SRTM (1950-2000 date range) and SRTM/IFSAR (2000-2008 date range). The difference rasters were evaluated to exclude difference values that were below a specified vertical change threshold, which was applied spatially by National Land Cover Dataset (NLCD) 1992 and 2006 land cover type, respectively. This spatial application of the vertical change threshold values improved the overall ability to detect vertical change because threshold values in bare earth areas were distinguished from threshold values in heavily vegetated areas. Lidar high-resolution (1.5 m) DEMs were acquired for Perry County, Kentucky from U.S. Department of Agriculture, Natural Resources Conservation Service Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGOrder.aspx#. ESRI Mosaic Datasets were generated from lidar point-cloud data and available topographic DEMs for the specified study area. These data were analyzed to estimate volumetric changes on the land surface at three different periods with lidar acquisitions collected for Perry County, KY on 3/29/12 to 4/6/12. A recent difference raster dataset time span (2008-2012 date range) was analyzed by differencing the Perry County lidar-derived DEM and an IFSAR-derived dataset. The IFSAR-derived data were resampled to the resolution of the lidar DEM (approximately 1-m resolution) and compared with the lidar-derived DEM. Land cover based threshold values were applied spatially to detect vertical change using the lidar/IFSAR difference dataset. Perry County lidar metadata reported that the acquisition required lidar to be collected with an average of 0.68 m point spacing or better and vertical accuracy of 15 cm root mean square error (RMSE) or better. References: Gesch, Dean B., 2006, An inventory and assessment of significant topographic changes in the United States Brookings, S. Dak., South Dakota State University, Ph.D. dissertation, 234 p, at https://topotools.cr.usgs.gov/pdfs/DGesch_dissertation_Nov2006.pdf.

This dataset contains the material of a user survey presented in the paper " More is Less - Adding Zoom Levels in Multi-Scale Maps to Reduce the Need for Zooming Interactions". There are several files in the dataset: the Tiles.zip contained all the tiles of the 5 multi-scale maps used in the experiments. The route.shp shapefiles contains the routes the participants were asked to follow during each trial instructions.doc is the reproduction of the instructions given to each participant prior to the survey. The document is in French. FormulaireConsentement.docx is the consent form the participant had to sign before the survey. It is also in French. script.r contains the R scripts run to analyse the results. qualitative_results.xlsx contains the qualitative information collected during the survey. Most of the information stored in the file is in French. quantitative_results.xlsx contains the quantitative results (completion time, nb of errors, etc.) for each participant and each trial.

We recruited 414 college students to participate in the experiment. Through the experiment, we collected their visual data and arranged them according to different visual indicators. Then we process our data through qualitative and quantitative analysis to get the final result.

This data set summarized biological and environmental sampling data from Reef Visual Census (RVC) surveys in southern Florida in conjunction with remote-sensed, high-resolution mapping data to take significant strides in moving from qualitative to quantitative habitat characterization of the RVC coral reef sampling frame. The data set contains two GIS shape files, one for the Dry Tortugas region and one for the Florida Keys, of survey sampling grids with habitat-depths quantitatively characterized to a 50 x 50 m resolution. Each sampling grid has region code, grid number, average depth (m), habitat code, zone code indicating onshore-offshore, MPA-code indicating whether inside or outside a protected area, depth strata code, rugosity strata code, fish strata code, and coral strata code. There is a dictionary file which describes the details of each habitat code categories. The refined sampling grid will have significant improvements to the accuracy, precision, and cost-effectiveness of RVC surveys in the Florida Keys and Tortugas regions. The study findings suggest some clear mapping priorities for fully characterizing the RVC sampling grid for the Florida Keys and Tortugas regions.

To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: (i) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. (ii) The inclusion probabilities must be: (a) knowable for nonsampled units and (b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne Very High Resolution (VHR) images, where: (I) an original Categorical Variable Pair Similarity Index (CVPSI, proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and (II) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability sampling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic MapperTM (SIAMTM) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAMTM by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAMTM pre-classification maps proposed in this contribution, together with OQIs claimed for SIAMTM by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAMTM software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems (GEOSS) initiative and the QA4EO international guidelines. Overlapping area matrices between:(A) the QuickBird-like Satellite Image Automatic MapperTM (Q-SIAMTM) preliminary classification maps at fine, intermediate, coarse semantic granularity (52, 28 and 12 spectral categories) generated from three very high resolution (VHR) test images: WorldView-2 T1, WorldView-2 T2, QuickBird-2 and(B) Reference thematic samples belonging to 7 land cover classes, selected by Michael Humber in the three VHR test images.Quality indicators of an Overlapping area matrix: Overall accuracy, Producer's accuracy, User's accuracy, Categorical Variable Pair Similarity Index.Files contain three test maps each: Q-SIAMTM at fine, intermediate, coarse granularity; One reference land cover class set: 7

The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).

This project is a collaborative effort between NOVA Southeastern University (Principal Investigator Brian Walker) and FWC FWRI. The primary objectives of this cooperative project are 1) Map West Florida continental shelf colonized hard bottom features as well as the offshore extent of seagrass in optically shallow waters using satellite imagery, object based image analysis and photo-interpretation techniques; 2) Conduct qualitative benthic surveys to validate the map and associate community information to the classification; 3) Conduct and analyze quantitative benthic surveys to characterize the hard bottom communities throughout the mapped space; 4) Compare and integrate acoustic-based and satellite imagery based seafloor maps; and 5) Develop recommendations as to how habitat data collected through satellite imagery can best be utilized to improve overall survey efficiency and the utility of data collected.

The dataset of this paper originated from quantitative survey data and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent wetlands governance for each of the ten cases. Collaboration networks of actors form a first level of the multi-level networks. The collaboration network is connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration network; quantitative survey data exists for 71% of them. A unique feature of the collaboration network is that it differentiates between positive and negative forms of collaboration depending on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are also elicited in the survey and connected to the collaboration network. The dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (2-4 experts per case).

Multivariate statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been widely used to summarize the structure of human genetic variation, often in easily visualized two-dimensional maps. Many recent studies have reported similarity between geographic maps of population locations and MDS or PCA maps of genetic variation inferred from single-nucleotide polymorphisms (SNPs). However, this similarity has been evident primarily in a qualitative sense; and, because different multivariate techniques and marker sets have been used in different studies, it has not been possible to formally compare genetic variation datasets in terms of their levels of similarity with geography. In this study, using genome-wide SNP data from 128 populations worldwide, we perform a systematic analysis to quantitatively evaluate the similarity of genes and geography in different geographic regions. For each of a series of regions, we apply a Procrustes analysis approach to find an optimal transformation that maximizes the similarity between PCA maps of genetic variation and geographic maps of population locations. We consider examples in Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, as well as in a worldwide sample, finding that significant similarity between genes and geography exists in general at different geographic levels. The similarity is highest in our examples for Asia and, once highly distinctive populations have been removed, Sub-Saharan Africa. Our results provide a quantitative assessment of the geographic structure of human genetic variation worldwide, supporting the view that geography plays a strong role in giving rise to human population structure.

In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Las Marias municipality in central-western Puerto Rico was severely impacted by landslides., Landslide density in this region was mapped as greater than 25 landslides/km2 (Bessette-Kirton et al., 2019). In order to better understand the controlling variables of landslide occurrence and runout in this region, two 2.5-km2 study areas were selected and all landslides within were manually mapped in detail using remote-sensing data. Included in the data release are five separate shapefiles: geographic areas representing the mapping extent of the four distinct areas (map areas, filename: map_areas), initiation location polygons (source areas, filename: SourceArea), polygons of the entire impacted area consisting of source, transport, and deposition (affected areas, filename: AffectArea), points on the furthest upslope extent of the landslide source areas (headscarp point, filename: HSPoint), and lines reflecting the approximate travel paths from the furthest upslope extent to the furthest downslope extent of the landslides (runout lines, filename: RunoutLine). These shapefiles contain qualitative attributes interpreted from the aerial imagery (such as geomorphic setting and impact of human activity) and qualitative attributes extracted from the geospatial data (such as source area length, width, and depth), as well as attributes extracted from other sources (such as geology and soil properties). A table detailing each attribute, attribute abbreviations, the possible choices for each attribute, and a short description of each attribute is provided as a table in the file labeled AttributeDescription.docx. The headscarp point shapefile attribute tables contain closest distance between headscarp and paved road (road_d_m; road data from U.S. Census Bureau, 2015). The runout line shapefile attribute table reflects if the landslide was considered independently unmappable past a road or river (term_drain), the horizontal length of the runout (length_m), the fall height from the headscarp to termination (h_m), the ratio of fall height to runout length (hlratio), distance to nearest paved road (road_d_m), and the watershed area upslope from the upper end of the runout line (wtrshd_m2). All quantitative metrics were calculated using tools available in ESRI ArcMap v. 10.6. The source area shapefile attribute table reflects general source area vegetation (vegetat) and land use (land_use), whether the slide significantly disaggregated during movement (flow), the failure mode (failmode), if the slide was a reactivation of a previous one (reactivate), if the landslide directly impacted the occurrence of another slide (ls_complex), the proportion of source material that left the source area (sourc_evac), the state of the remaining material (remaining), the curvature of the source area (sourc_curv), potential human impact on landslide occurrence (human_caus), potential landslide impact on human society (human_effc), if a building exists within 10 meters of the source area (buildng10m), if a road exists within 50 meters of the source area (road50m), the planimetric area of the source area (area_m2), the dimension of the source area perpendicular to the direction of motion (width_m), the dimension of the source area parallel to the direction of motion (length_m), the geologic formation of the source area (FMATN; from Bawiec, W.J., 1998), the soil type of the source area (MUNAME; from Acevido, G., 2020), the root-zone (0-100 cm deep) soil moisture estimated by the NASA SMAP mission for 9:30 am Atlantic Standard Time on 21 September 2017 (the day after Hurricane MarÃa) (smap; NASA, 2017), the average precipitation amount in the source area for the duration of the hurricane (pptn_mm; from Ramos-Scharrón, C.E., and Arima, E., 2019), the source area mean slope (mn_slp_d), the source area median slope (mdn_slp_d), the average depth change of material from the source area after the landslide (mn_dpth_m), the median depth change of material from the source area after the landslide (mdn_dpt_m), the sum of the volumetric change of material in the source area after the landslide (ldr_sm_m3), the major geomorphic landform of the source (maj_ldfrm), and the landcover of the source area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). The affected area shapefile attribute table reflects the general affected area vegetation type (vegetat), the major geomorphic landform on which the landslide occurred (maj_ldfrm), whether the slide disaggregated during movement (flow), the general land use (land_use), the planimetric area of the affected area (area_m2), the dominant geologic formation of the affected area (FMATN; from Bawiec, W.J., 1998), the dominant soil type of the affected area (MUNAME; from Acevido, G., 2020), the sum of the volumetric change of material in all the contributing source areas for the affected area (Sum_ldr_sm), the average volumetric change of material in all the contributing source areas for the affected area (Avg_ldr_sm), if the landslide was considered independently unmappable past a road or river (term_drain), the number of contributing source areas to the affected area (num_srce), and the dominant landcover of the affected area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). Mapping was conducted using aerial imagery collected between 9-15 October 2017 at 25-cm resolution (Quantum Spatial, Inc., 2017), a 1-m-resolution pre-event lidar digital elevation model (DEM) (U.S. Geological Survey, 2018), and a 1-m-resolution post-event lidar DEM (U.S. Geological Survey, 2020). In order to accurately determine the extent of the mapped landslides and to verify the georeferencing of the aerial imagery, aerial photographs were overlain with each DEM as well as a pre- and post-event lidar difference (2016-2018), and corrections were made as needed. Additional data sources described in the AttributeDescription document and metadata were used to extract spatial data once mapping was complete and results were appended to the shapefile attribute tables. Data in this release are provided as ArcGIS point (HSPoint), line (RunoutLine), and polygon (AffectArea and SourceArea) feature class files. Bessette-Kirton, E.K., Cerovski-Darriau, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W, Thomas, M.A., and Hughes, K. Stephen, 2019, Landslides Triggered by Hurricane Maria: Assessment of an Extreme Event in Puerto Rico: GSA Today, v. 29, doi:10.1130/GSATG383A.1 U.S. Census Bureau, 2015, 2015 TIGER/Line Shapefiles, State, Puerto Rico, primary and secondary roads State-based Shapefile: United States Census Bureau, accessed September 12, 2019, at http://www2.census.gov/geo/tiger/TIGER2015/ PRISECROADS/tl_2015_72_prisecroads.zip. Bawiec, W.J., 1998, Geology, geochemistry, geophysics, mineral occurrences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey Open-File Report 98-38, https://pubs.usgs.gov/of/1998/of98-038/ (accessed May 2020). Acevido, G., 2020, Soil Survey of Arecibo Area of Norther Puerto Rico: United States Department of Agriculture, Soil Conservation Service. National Aeronautics and Space Administration [NASA], 2017, SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 4: National Snow & Ice Data Center web page, accessed September 12, 2019, at https://nsidc.org/data/SPL4SMAU/versions/4. Ramos-Scharrón, C.E., and Arima, E., 2019, Hurricane MarÃa’s precipitation signature in Puerto Rico—A conceivable presage of rains to come: Scientific Reports, v. 9, no. 1, article no. 15612, accessed February 28, 2020, at https://doi.org/10.1038/ s41598-019-52198-2. Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004, Development of a 2001 National Landcover Database for the United States: Photogrammetric Engineering and Remote Sensing, Vol. 70, No. 7, July 2004, pp. 829-840. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018). U.S. Geological Survey, 2020, USGS NED Original Product Resolution PR Puerto Rico 2018: http://nationalmap.gov/elevation.html (accessed June 2020). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government

The Water Supply and Management Schema (SAGE) is a collective planning document, for a coherent hydrographic perimetre, setting general objectives for the use, development, quantitative and qualitative protection of the water resource. It must be compatible with the Schema Water Supply and Management Managers (SDAGE).The perimetre and the time frame in which it is developed are determined by the SDAGE. In default, it shall be stopped by the prefets of the department, the case being decided on the proposal of the territorial collectivites of interest. The SAGE is established by a Commission Locale de l’Eau (CLE) representing the various actors in the territory, subject to a public investigation and is approved by the Prefet. He has a legal framework: the Regulation and its cartographic documents are enforceable against third parties and decisions in the field of water must be compatible or made compatible with the plan for the supply and sustainable management of the water resource. Planning documents (scheme of territorial coherence, local planning plan and municipal map) must be compatible with the objectives of protection defined by the SAGE. The schema departemental of the carrieres must be compatible with the provisions of the SAGE. The reference texts are Articles L.212-3 to L.212-11 of the Environmental Code and circular DE/SDATDCP/BDCP/No. 10 of 21 April 2008.

Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexities involved in the end-to-end Raman analytics pipeline: baseline removal, cosmic noise elimination, and extraction of trusted spectral signatures and abundance maps. Elimination of cosmic noise is the bottleneck in the entire Raman analytics pipeline. Unless this issue is addressed, the realization of autonomous Raman analytics is impractical. Here, we present a learner-predictor strategy-based “automated hyperspectral Raman analysis framework” to rapidly fingerprint the molecular variations in the hyperspectral 2D/3D Raman dataset. We introduce the spectrum angle mapper (SAM) technique to eradicate the cosmic noise from the hyperspectral Raman dataset. The learner-predictor strategy eludes the necessity of human inference, and analytics can be done in autonomous mode. The learner owns the ability to learn; it automatically eliminates the baseline and cosmic noise from the Raman dataset, extracts the predominant spectral signatures, and renders the respective abundance maps. In a nutshell, the learner precisely learned the spectral features space during the hyperspectral Raman analysis. Afterward, the learned spectral features space was translated into a neural network (LNN) model. In the predictor, machine-learned intelligence (LNN) is utilized to predict the alternate batch specimen’s abundance maps in real time. The qualitative/quantitative evaluation of abundance maps implicitly lays the foundation for monitoring the offline/inline industrial qualitative/quantitative quality control (QA/QC) process. The present strategy is best suited for 2D/3D/four-dimensional (4D) hyperspectral Raman spectroscopic techniques. The proposed ML framework is intuitive because it obviates human intelligence, sophisticated computational hardware, and solely a personal computer is enough for the end-to-end pipeline.

These primary data are taken from a mixture of quantitative and qualitative methods employed over two years in three countries in East Africa: Kenya, Sudan and Uganda. In each of the three countries research teams worked in four sites which were split across two districts (Uganda), counties (Kenya) or states (Sudan). The methodology progressed from inductive qualitative tools from which hypotheses were developed to deductive qualitative and quantitative investigation. Inductive methods included key informant interviews (not included in these data due to issues with confidentiality), agricultural timelines, innovation histories and communication maps – a mixture of textual and visual data. Inductive methods included quantitative tools: a household survey of more than 400 households in each country and participatory budgets and qualitative tools: innovation behaviour case studies, wealth ranking and local economy chain analysis (protocols for all methods are included with this submission). This project’s aim is to understand how different institutional arrangements for supporting smallholder farmers affect the innovation activity and livelihoods of female and male farmers in Kenya, Sudan and Uganda, and the impact of this innovation on growth in the local economy. From interviews with key informants and a document review the research team will build up a detailed picture of organisations and institutions that support and provide services to smallholder farmers in Kenya, Sudan and Uganda. They will then carry out a detailed investigation of recent innovation activity in four sites in each country and of the factors that have constrained and those that have supported innovation. Participatory research tools will include innovation histories, communication maps, value chain analysis and timelines. The analysis will generate hypotheses linking institutional arrangements, innovation activity, and changes in farm output, livelihoods, and incomes. These hypotheses will be tested using data from further participatory research and a sample survey in each of the research sites. The team will develop evidence-based conclusions on the potential and limitations for enhancing support for smallholder farmers’ innovation through new institutional arrangements and different ways of implementing support programmes at local level. These data are a mixture of quantitative and qualitative. Collected using a range of methods, the data are split between three folders, objective 2 data, objective 3 quantitative data and objective 3 qualitative data. Objective 2 data were collected using qualitative tools which included agricultural timelines, innovation histories and communication mapping (the protocols for each of these activities are included in the objective 2 data folder). Objective 3 quantitative data were collected through a household survey. Sampling strategy for each country is outlined in the objective 3 quantitative data folder. Objective 3 qualitative data were collected using a range of tools which included participatory budgets, livelihood maps, wealth ranking, local economy chain analysis and innovation behaviour case studies. The protocols for each of these activities are included in the objective 3 qualitative data folder.

This dataset represents a comprehensive effort to capture the global trajectory of research on social media in entrepreneurial startups over the past decade and a half. It covers bibliometric records collected from 2009 to 2024, with modeling projections extending into 2025–2034 to anticipate future scholarly developments. The collection draws exclusively from peer-reviewed documents indexed in Scopus, comprising 462 publications authored by 1,195 scholars from 72 countries and disseminated across 365 distinct sources. Each record was selected based on rigorous inclusion criteria, ensuring relevance to the topic, complete coverage within the annual timespan, peer-review status, and accessibility for analysis.The dataset provides rich bibliometric metadata, including authors, titles, abstracts, keywords, affiliations, DOIs, funding details, and references, enabling both quantitative mapping and qualitative insights. Analytical outputs are organized into multiple formats: CSV files for raw bibliometric data; PNG images for thematic maps, trend topic visualizations, and research flowcharts; and CSV and PNG outputs for annual publication trajectories and polynomial regression-based modeling projections. Visualization and analysis were conducted using Microsoft Excel for summary statistics, R Biblioshiny for thematic and trend mapping, and Python for projection modeling.By combining bibliometric mapping with predictive modeling, this dataset offers a unique foundation for studying knowledge production, international collaboration, and research dynamics in the intersection of social media and startup entrepreneurship. It is designed as an open data resource to facilitate replication, encourage comparative studies across domains, and support evidence-based decision-making for scholars, practitioners, and policymakers engaging with the evolving digital entrepreneurship ecosystem.

This child page contains qualitative multi-element compositional data from scanning electron microscopy-energy dispersive spectrometry. The data are in the form of element maps and normalized compositional data from spots or areas. These qualitative data are accompanied by backscattered electron images and, in some cases, corresponding optical images. Quantitative measurement of features in a subset of the optical images is also presented. Data collected after July 22, 2019 are subject to secondary data review as required by the Energy and Minerals Mission Area Quality Management System (EMMA QMS). As this process is not yet complete, data collected after July 22, 2019 must be considered preliminary or provisional (subject to revision). They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS or the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. When secondary data review is completed, this provisional statement will be removed.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Classification was based on 50 quantitative field plots, which were placed across WICR in a stratified random manner based on qualitative field observation points, consideration of biophysical setting, and viewing of air photos. Mapping was based on a combination of image object generation and heads-up digitization of air photos on-screen. Accuracy assessment points obtained during 2012 verified that the map is 81.3% accurate across all classes. GIS Database 2011 - 2012: Wilson’s Creek National Battlefield = 1,975 acres (799 hectares) Base Imagery used for mapping (acquired by MoRAP): 2007-2009, Greene County, MO, leaf-off, true color, 2 ft 2009, Greene County, MO, leaf-on, Color Infrared, 1 m 2009, Greene County, MO, leaf-on, true color, 1 m Additional Imagery acquired and viewed by MoRAP: 1941 Panchromatic Aerial Photo 1936 Panchromatic Aerial Photo Minimum Mapping Unit = 0.5 hectares (ha) Total Size = 379 Polygons Average Polygon Size = 5.21 acres (2.1 ha) Overall Thematic Accuracy = 81.3% Project Completion Date: 11/2012

Data and knowledge of spatial-temporal dynamics of multiple types of water body are significant for water resources management but remain very limited. Using the Landsat satellite data and weakly supervised deep learning techniques for long term mapping, we report annual maps of multiple types of inland water bodies on urban agglomeration scale in the middle reaches of the Yangtze River(MRYR) during 1990–2021 at 30m spatial resolution. Accuracy assessment from 14000 validation points in seven years indicates an overall accuracy of 94.50%. Quantitative and qualitative comparison with other water related mapping products further demonstrates the superiority on long time span, refined classification system, and the high precision. The Water-MRYR could support the government and the public in the challenging water resources management and wetland conservation.

Thematic accuracy assessment of land cover/use products requires reliable reference data that enable their qualitative and quantitative evaluation. Such dataset with up-to-date information on a predefined class composition and spatial distribution is rarely available and its preparation requires an appropriate methodological approach adjusted to a specific product.Development of a new pan-European land cover/use map, generated from Copernicus Sentinel-2 data 2017 within the Sentinel-2 Global Land Cover (S2GLC) project carried out under a programme of and funded by the European Space Agency, provided an opportunity to design and develop an unique dataset dedicated to validation of this product. The dataset was prepared by twofold stratified random sampling. The first selection designated validation sites represented by Sentinel-2 image tiles and was performed on a country level with county borders used as a stratum. In the second selection validation samples were chosen randomly within the validation sites with stratification based on classes of the CORINE Land Cover database.The final dataset composed of samples visually checked by experienced image interpreters consists of a total number of 52,024 samples spread over the European countries. The samples represent 13 land cover/use classes including artificial surfaces, natural material surfaces (consolidated and un-consolidated), broadleaf tree cover, coniferous tree cover, herbaceous vegetation, moors and heathland, sclerophyllous vegetation, cultivated areas, vineyards, marshes, peatbogs, water bodies and permanent snow cover. Each sample provides information about the occurrence of one of the predefined land cover or land use classes within an area of 100 m² represented by a single pixel (10 m size) of Sentinel-2 imagery for the year 2017. The described dataset was used for the accuracy assessment process of the product Land Cover Map of Europe 2017 resulting from the S2GLC project and provided an estimate of the overall accuracy at the level of 86.1%. S2GLC - Land Cover Map of Europe 2017 reference dataCBK PAN, http://s2glc.cbk.waw.pl/extensionAttribute table fields:'S2GLC' – a land cover/use class symbol according to the S2GLC classification system'TILE' – a symbol of the Sentinel-2 granule (a tile of the Military Grid Reference System)'NAME_ENG' – a country English name (valid for inland and coastal areas). Data source of country names and administrative boundaries: 'Countries, 2020 - Administrative Units - Dataset' of European Commission, Eurostat (ESTAT), GISCO, https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/countriesClassification system:111 - Artificial surfaces211 - Cultivated areas221 - Vineyards231 - Herbaceous vegetation311 - Broadleaf tree cover312 - Coniferous tree cover322 - Moors and heathland323 - Sclerophyllous vegetation331 - Natural material surfaces335 - Permanent snow cover411 - Marshes412 - Peatbogs511 - Water bodiesData projection: Lambert Azimuthal Equal Area (LAEA)EPSG: 3035For more technical information on this dataset please refer to Malinowski et al. (2020).

PurposeTo evaluate the diagnostic performance of cerebral blood flow (CBF) by using arterial spin labeling (ASL) perfusion magnetic resonance (MR) imaging to differentiate glioblastoma (GBM) from brain metastasis.Materials and MethodsThe institutional review board of our hospital approved this retrospective study. The study population consisted of 128 consecutive patients who underwent surgical resection and were diagnosed as either GBM (n = 89) or brain metastasis (n = 39). All participants underwent preoperative MR imaging including ASL. For qualitative analysis, the tumors were visually graded into five categories based on ASL-CBF maps by two blinded reviewers. For quantitative analysis, the reviewers drew regions of interest (ROIs) on ASL-CBF maps upon the most hyperperfused portion within the tumor and upon peritumoral T2 hyperintensity area. Signal intensities of intratumoral and peritumoral ROIs for each subject were normalized by dividing the values by those of contralateral normal gray matter (nCBFintratumoral and nCBFperitumoral, respectively). Visual grading scales and quantitative parameters between GBM and brain metastasis were compared. In addition, the area under the receiver-operating characteristic curve was used to evaluate the diagnostic performance of ASL-driven CBF to differentiate GBM from brain metastasis.ResultsFor qualitative analysis, GBM group showed significantly higher grade compared to metastasis group (p = 0.001). For quantitative analysis, both nCBFintratumoral and nCBFperitumoral in GBM were significantly higher than those in metastasis (both p < 0.001). The areas under the curve were 0.677, 0.714, and 0.835 for visual grading, nCBFintratumoral, and nCBFperitumoral, respectively (all p < 0.001).ConclusionASL perfusion MR imaging can aid in the differentiation of GBM from brain metastasis.