ATOM service for downloading information related to Theme 10 - Demography, of Annex III of the INSPIRE Directive, in the geographical area of Catalonia. The service allows the download of demographic data of Catalonia, according the Directive requirements.

Success.ai is dedicated to providing advanced consumer insights that empower businesses to understand and predict consumer behaviors effectively. Our datasets are crafted from diverse online interactions, including keyword searches, sentiment analysis, and web activity, paired with detailed geodemographic data to offer a holistic view of consumer trends.

Utilize Our Consumer Insights to Enhance Your Business Strategies:

• Keyword Data Analysis: Understand what your potential customers are searching for with detailed keyword data. This information is crucial for optimizing SEO strategies and aligning your content with consumer interests.
• Sentiment Analysis: Gauge public opinion and sentiment trends across various demographics to tailor your marketing messages or product features.
• Web Activity Insights: Track how consumers interact online to refine your online marketing strategies and improve user engagement.
• Geodemographic Profiling: Employ detailed demographic and geographic data to segment your marketing campaigns and personalize outreach efforts.
• Consumer Behavior Reports: Analyze consumer purchasing patterns and preferences to forecast future trends and adjust your business approach accordingly.

Why Success.ai Stands Out:

• Tailored Data Solutions: Our data solutions are customized to meet specific industry needs, ensuring relevancy and applicability.
• Real-Time Data Processing: We offer the latest insights with continuous updates, keeping your business ahead of the curve.
• Precision and Compliance: Our data collection methods are not only precise but also strictly adhere to global privacy standards, ensuring ethical usage and data reliability.
• Affordable Pricing: We provide competitive pricing models that guarantee the best value for extensive data insights.

Empower Your Business With Data-Driven Decisions:

• Email Marketing: Utilize our data to craft targeted email campaigns that resonate with specific consumer segments.
• Online Marketing: Enhance your digital presence by aligning your strategies with real-time consumer data insights.
• B2B Lead Generation: Identify and engage potential business clients by understanding their industry-specific behaviors and needs.
• Sales Data Enrichment: Enrich your sales strategies with comprehensive consumer data to boost conversion rates.
• Competitive Intelligence: Stay ahead of the competition by leveraging detailed insights into consumer behaviors and market trends.

With Success.ai, transform vast data into actionable insights that drive business growth and strategic innovation. Connect with us today to learn how our Consumer Insights Data can revolutionize your approach to market analysis and consumer engagement.

Experience the competitive edge with Success.ai, where we don't just offer data; we deliver market leadership.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Abstract: This rainforest tree demographic data package comprises recruitment, growth and mortality census data for rainforest trees Davies Creek Plot in Dinden National Park, (25 km south west of Cairns), Queensland for 1963-2013. This plot consists of one 1.7 hectare plot in tropical rainforest, established in 1963. Rainforest tree attributes recorded comprise the size (height or girth) of tagged and mapped, free-standing stems of shrub and tree species. Sampling has been undertaken at intervals of 1-6 years. The Davies Creek Plot was incorporated over an existing 0.4 ha plot established by the Queensland Department of Forestry in 1951 (Nicholson et al. 1988), so the central part of the Davies Creek Plot has records extending back more than a decade prior to 1963.

This data package forms part of the collection of vegetation data undertaken at plots situated in both Lamington National Park and Davies Creek initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963.

A synopsis of related data packages which have been collected as part of the Connell Rainforest Plot Network’s full program is provided at https://doi.org/10.25911/5c13444388e1b

Sampling method: The Dinden National Park Plot is a 1.7 hectare plot. The plot was selected by Prof. Joseph H. Connell in 1963 on the advice of his CSIRO collaborators Dr Len Webb and Mr Geoff Tracey, and was chosen for three reasons; it was accessible, it was unlogged, and a smaller 0.4 ha plot belonging to the Queensland Department of Forestry had already been established there in 1951.

This plot is one of two plots established by Connell in 1963 – the other is in subtropical rainforest near O’Reilly’s Guesthouse in Lamington National Park, 65 km south of Brisbane. The same sampling methods are employed at both plots, at intervals of 1-6 years.

Project funding: The National Science Foundation was the sole funder of this research between 1963 and 2003.

Between 2012 and 2018 this project was part of, and funded through the Long Term Ecological Research Network (LTERN) a facility within the Terrestrial Ecosystem Research Network (TERN) and supported by the Australian Government through the National Collaborative Research Infrastructure Strategy.

• Population density 2023 (inhabitants per km²), Lorraine: 2021 - Territorial entities: arrondissements (Lorraine, Wallonie), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) - Statistical data sources: Destatis, INSEE, Statbel, STATEC. Harmonization: IBA / OIE 2024 - Geodata sources: GeoBasis-DE / BKG, IGN France, NGI-Belgium, ACT Luxembourg. Harmonization: SIG-GR / GIS-GR 2024

• Population density 2013 (inhabitants per km²) - Territorial entities: arrondissements (Wallonie), zones d'emploi (Lorraine), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) - Statistical data sources: INSEE Lorraine; SPF Economie; STATEC; Statistisches Landesamt Rheinland-Pfalz; Statistisches Amt Saarland. Harmonization: IBA / OIE 2014 - Geodata sources: EuroGeographics EuroRegionalMap v3.0 - 2010. Harmonization: SIG-GR / GIS-GR 2014

Species distribution models (SDMs) are widely used to infer species-environment relationships, predict spatial distributions, and characterise species’ environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors, and the calibration of stage-specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on..., We conducted a keyword-based search in the Web of Science to quantify how often temporal components related to phenology and demographic stages are explicitly considered in plant SDMs. A full list of keywords is provided in the Supporting Information Table S1. We used a nested set of keywords to identify all studies that mentioned SDMs (or common synonyms), were focused on plants, and were listing relevant keywords related to phenology or to demographic stages, respectively. The search was carried out on 5-Oct-2023 and was restricted to English-language journal articles in the period 1945-2022 (no studies using SDMs were published before that start year). Overall, we found more than 40,000 articles mentioning SDM and over 10,000 articles in our refined search for plant SDMs, with a strong increase in the number of articles over time. Among these, phenology (or related search terms) was mentioned in 970 articles and demographic stages (or related terms) in 1188 articles, each averaging c..., , # The niche through time: considering phenology and demographic stages in plant distribution models

https://doi.org/10.5061/dryad.sn02v6xct

Description of the data and file structure

Columns from WoS (Web of Science) search – these are identical in both excel sheets

These columns are the standard columns provided as WoS search output. If the entries contain "n/a", then no information was provided by WoS because those items are not applicable. For example, a journal article does not have any entries for book authors.

According to Google's search data from 2024, the most common queries by Indians were ****** and ********. These common search queries provide an insight into the Indian content consumption patterns. Video content was sought actively, also evident by the fact that the video-sharing platform had the highest share of monthly social network users in the country that same year. Optimization of video streaming Not surprisingly, video content is witnessing exponential growth in recent years. It is evident in the fact that video streaming accounts for a major share of online mobile traffic across the nation. Recent trends suggest an increase in consumption of video over graphic or text content. Hence, a sound implementation of SEO in videos has become a necessity for a successful content creating channel. One of the major optimization strategies is to cater to the demographic of the nation, which incorporates efficient description, headline, and tag implementation. Keyword search trends Searches related to local preferences are gaining momentum, rendering local SEO invaluable to promoting visibility of the content. Phrases like “near me” and “close to me” have witnessed a significant increase in their frequency of appearances in queries. Since the coronavirus (COVID-19) outbreak, the latter part of 2020 has seen a significant rise in the usage of queries related to the pandemic. This is testament to the influence of recent events on keywords and optimized phrases for improved channel visibility.

Projection of total population 2024-2050 Territorial entities: arrondissements (Wallonie), départements (Lorraine), Grand-Duché (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, Eurostat, Statbel, STATEC, Statistisches Amt Saarland, Statistisches Landesamt Rheinland-Pfalz. Calculations: OIE/IBA 2024 Geodata sources: ACT Luxembourg, IGN France, GeoBasis-DE / BKG, NGI-Belgium. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2425&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/05879c75-1c5f-4eea-be23-53e27662fb16 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Population_projection_WMS/guest with layer name(s): -Projection_20_64_years_2024_2050

Abstract: This rainforest tree data package comprises stand structure data for rainforest trees at the O'Reilly's Connell Rainforest Plot, Lamington National Park (84 km south of Brisbane), Queensland for 2013. The O'Reilly's Plot consists of two 1.0 hectare plots spaced 600 m apart in sub-tropical rainforest, established in 1963. They have always been treated as a single unit for the purpose of analysis. Rainforest tree attributes recorded comprise the size (height or girth) of tagged and mapped, free-standing stems of shrub and tree species. Sampling has been undertaken at intervals of 1-6 years since 1963, and this data package is from the most recent recensus of the plot in July 2013. It essentially provides a snapshot of stand structure on the site. This data package forms part of the collection of vegetation data undertaken at plots situated in both Lamington National Park and Davies Creek initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963.

A synopsis of related data packages which have been collected as part of the Connell Rainforest Plot Network's full program is provided at https://doi.org/10.25911/5c13444388e1b.

Sampling method: The O'Reilly's Plot consists of two 1.0 hectare plots spaced 600 m apart, which have always been treated as a single unit for the purpose of analysis. This data package forms part of the collection of vegetation data undertaken at plots in Lamington National Park which were initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963. The same sampling methods are employed in a related data package focussing on tropical rainforest plots at Davies Creek, Dinden National Park (1.7 ha, 25 km south-west of Cairns). Sampling has been undertaken at intervals of 1-6 years.

Project abstract: This group conducts research in the rainforest investigating tree demographics.

Project funding: The National Science Foundation was the sole funder of this research between 1963 and 2003.

Between 2012 and 2018 this project was solely funded through the Long Term Ecological Research Network (LTERN) a facility within the Terrestrial Ecosystem Research Network (TERN) and supported by the Australian Government through the National Collaborative Research Infrastructure Strategy.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Abstract: This rainforest tree data package comprises stand structure data for rainforest trees at the Davies Creek Plot in Dinden National Park, (25 km south west of Cairns), Queensland for 2013. This plot consists of one 1.7 hectare plot in tropical rainforest, established in 1963. Rainforest tree attributes recorded comprise the size (height or girth) of tagged and mapped, free-standing stems of shrub and tree species. Sampling has been undertaken at intervals of 1-6 years since 1963; this data package is from the most recent full re-census of the plot in October 2013, and essentially provides a snapshot of stand structure on the site. This data package forms part of the collection of vegetation data undertaken at plots situated in both Lamington National Park and Davies Creek initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963. A synopsis of related data packages which have been collected as part of the Connell Rainforest Plot Network’s full program is provided at https://doi.org/10.25911/5c13444388e1b.

Sampling method: The Dinden National Park Plot is a 1.7 hectare plot. The plot was selected by Prof. Joseph H. Connell in 1963 on the advice of his CSIRO collaborators Dr Len Webb and Mr Geoff Tracey, and was chosen for three reasons; it was accessible, it was unlogged, and a smaller 0.4 ha plot belonging to the Queensland Department of Forestry had already been established there in 1951. This plot is one of two plots established by Connell in 1963 – the other is in subtropical rainforest near O’Reilly’s Guesthouse in Lamington National Park, 65 km south of Brisbane. The same sampling methods are employed at both plots, at intervals of 1-6 years. See Connell Rainforest Plot Network’s full program provided at https://doi.org/10.25911/5c13444388e1b.

Study extent: None

Project abstract: This group conducts research in the rainforest looking at tree demographics.

Project funding: The National Science Foundation was the sole funder of this research between 1963 and 2003. Between 2012 and 2018 this project was soley funded through the Long Term Ecological Research Network (LTERN) a facility within the Terrestrial Ecosystem Research Network (TERN) and supported by the Australian Government through the National Collaborative Research Infrastructure Strategy.

table>

QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community. This dataset contains global Population Distribution (1990), Terrestrial Area and Country Name Information on a One by One Degree Grid Cell Basis.

• Population density 2021 (inhabitants per km²), Lorraine: 2019 - Territorial entities: arrondissements (Lorraine, Wallonie), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) - Statistical data sources: Destatis, INSEE, Statbel, STATEC. Harmonization: IBA / OIE 2022 - Geodata sources: GeoBasis-DE / BKG 2017, IGN France 2017, NGI-Belgium 2017, ACT Luxembourg 2017. Harmonization: SIG-GR / GIS-GR 2022

Projection of working age population (20-64 years) 2024-2050 Territorial entities: arrondissements (Wallonie), départements (Lorraine), Grand-Duché (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, Eurostat, Statbel, STATEC, Statistisches Amt Saarland, Statistisches Landesamt Rheinland-Pfalz. Calculations: OIE/IBA 2024 Geodata sources: ACT Luxembourg, IGN France, GeoBasis-DE / BKG, NGI-Belgium. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2426&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/1fc2dadc-cabf-41cb-a900-37c78c3fb7a7 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Population_projection_WMS/guest with layer name(s): -Projection_20_64_years_2024_2050

Abstract: This rainforest tree demographic data package comprises recruitment, growth and mortality census data for rainforest trees at the O'Reilly's Plot, Lamington National Park (84km south of Brisbane), Queensland for 1963–2013. The O’Reilly’s Plot consists of two 1.0 hectare plots spaced 600 m apart in sub-tropical rainforest, which have always been treated as a single unit for the purpose of analysis. Rainforest tree attributes recorded comprise the size (height or girth) of tagged and mapped, free-standing stems of shrub and tree species. Sampling has been undertaken at intervals of 1-6 years. This data package forms part of the collection of vegetation data undertaken at plots situated in both Lamington National Park and Davies Creek initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963.

A synopsis of related data packages which have been collected as part of the Connell Rainforest Plot Network’s full program is provided at https://doi.org/10.25911/5c13444388e1b.

Sampling method: The O'Reilly's Plot consists of two 1.0 hectare plots spaced 600 m apart, which have always been treated as a single unit for the purpose of analysis. This data package forms part of the collection of vegetation data undertaken at plots in Lamington National Park which were initiated by Professor Joseph H. Connell (University of California, Santa Barbara) in 1963. The same sampling methods are employed in a related data package focussing on tropical rainforest plots at Davies Creek, Dinden National Park (1.7 ha, 25 km south-west of Cairns). Sampling has been undertaken at intervals of 1-6 years.

Project funding: The National Science Foundation was the sole funder of this research between 1963 and 2003.

Between 2012 and 2018 this project was solely funded through the Long Term Ecological Research Network (LTERN) a facility within the Terrestrial Ecosystem Research Network (TERN) and supported by the Australian Government through the National Collaborative Research Infrastructure Strategy.

The FGGD rural population density map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel classified as rural by the urban area boundaries map contains the number of persons per square kilometre, aggregated from the 30 arc-second datalayer. All remaining pixels contain no data. The method used by FAO to generate this datalayer is described in FAO, 2005, Mapping global urban and rural population distributions, by M. Salvatore, et. al.

Populated places including urbanized areas and villages within the Okavango Basin. Source: Digital Chart of the World (DCW). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project†document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

Inhabitants of Sant Cugat del Vallès is a set of geographical information referring to the demographic data of the municipality. The data is represented in an interactive viewer that offers the demographic figures of Sant Cugat broken down by districts and census sections. Finally, the viewer allows you to search for streets and addresses.

Boundaries identifying similar behavioural ecotypes and sub-populations of Grizzly bears. This dataset contains versions from multiple years. From 2018 on, NatureServe conservation concern ranking categories (e.g., Very Low, Low, Moderate, High, Extreme Concern) supersede the pre-2018 population status categories (e.g., Viable, Threatened, Extirpated) contained in the field STATUS. NatureServe conservation concern ranking categories reflect population size and trend, genetic and demographic isolation, as well as threats to bears and their habitats. The NatureServe conservation concern ranking fields are named CONSERVATION_CONCERN_RANK and CONSERVATION_CONCERN_DESC. Please view the attached PDF file for a summary of changes to this dataset from 2012 onward. To download only the 2018 units, in the link below, select the "Export" tab, then select the "Provincial Layer Download" button: https://maps.gov.bc.ca/ess/hm/imap4m/?catalogLayers=7744,7745 Grizzly Bear Conservation Ranking results table is available here: https://catalogue.data.gov.bc.ca/dataset/e08876a1-3f9c-46bf-b69a-3d88de1da725 Grizzly Bear population estimates from various years are available here: https://catalogue.data.gov.bc.ca/dataset/2bf91935-9158-4f77-9c2c-4310480e6c29 Grizzly Bear reports are available here: https://www2.gov.bc.ca/gov/content/environment/plants-animals-ecosystems/wildlife/wildlife-conservation/grizzly-bear