This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).

Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements

The present dataset contains the following data for NB-IoT:

Raw data for each campaign, stored in two .csv files. For a generic campaign

NB-IoT_coverage_C

NB-IoT_RefSig_cir_C

Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by

Estimated positions of eNodeBs, stored in a csv file for each city;

A matlab script and a function to extract and generate processed data from the raw data for each city.

The dataset contains the following data for 5G:

Raw data for each campaign, stored in two .xslx files. For a generic campaign

5G_coverage_C

5G_RefSig_cir_C

Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by

A matlab script and a supporting function to extract and generate processed data from the raw data.

In addition, in the case of the Rome data a function to interpolate missing data in the original data is provided for each technology, as well as the corresponding interpolated data, stored in a second matlab workspace. The interpolation rationale and procedure is detailed in:

L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023.

Positioning using the 5G data was furthermore in investigated in:

K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," submitted to IEEE Communications Magazine, 2023

Please refer to the above publications when using and citing the dataset.

Monthly indexes and percentage changes for major components, selected sub-groups and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse and Yellowknife. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

The Marine Biodiversity Observation Network Pole to Pole (MBON P2P) effort seeks to develop a framework for the collection, use and sharing of marine biodiversity data in a coordinated, standardized manner leveraging on existing infrastructure managed by the Global Ocean Observing System (GOOS; IOC-UNESCO), the GEO Biodiversity Observation Network (GEO BON), and the Ocean Biogeographic Information System (OBIS). The MBON Pole to Pole aims to become a key resource for decision-making and management of living resource across countries in the Americas for reporting requirements under the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Aichi Targets of the Convention of Biological Diversity (CBD), and the UN 2030 Agenda for Sustainable Development Goals (SDGs). This collection corresponds to the species registered on sandy beaches of the Musquash Harbour, Mispec Bay, and New River Beach, New Brunswick, Canada, using the MBON P2P sampling protocol for sandy beaches, with funding from the Government of Canada's Coastal Environmental Baseline Program. Citation: Reinhart B (2024). MBON POLE TO POLE: SANDY BEACH BIODIVERSITY OF SOUTHWEST NEW BRUNSWICK, CANADA. Version 1.5. Caribbean OBIS Node. Samplingevent dataset. https://ipt.iobis.org/mbon/resource?r=sandybeachesbayoffundynb&v=1.5

Landscape intactness has been defined as a quantifiable estimate of naturalness measured on a gradient of anthropogenic influence. We developed a multiscale index of landscape intactness for the Bureau of Land Managements (BLM) landscape approach, which requires multiple scales of information to quantify the cumulative effects of land use. The multiscale index of landscape intactness represents a gradient of anthropogenic influence as represented by development levels at two analysis scales. To create the index, we first mapped the surface disturbance footprint of development, for the western U.S., by compiling and combining spatial data for transportation1, energy extraction and transport1,2, mineral extraction3, agriculture4, and urban5 development. All linear features and points were buffered to create a surface disturbance footprint. Buffered footprints and polygonal data were rasterized at 15-meter (m), aggregated to 30-m, and then combined with the existing 30-meter inputs for urban development and cultivated croplands. The footprint area was represented as a proportion of the cell and was summed using a raster calculator. To reduce processing time, the 30-m disturbance footprint was aggregated to 90-m. The 90-m resolution surface disturbance footprint is retained as a separate raster in this data release. We used a circular moving window to create a terrestrial development index for two scales of analysis, 2.5- and 20-kilometers (km), by calculating the percent of the surface disturbance footprint at each scale. The terrestrial development index at both the 2.5-km and 20-km were retained as separate rasters in this data release. The terrestrial development indexes at two analysis scales were ranked and combined to quantify landscape intactness level. To identify intact areas, we focused on terrestrial development index scores less than or equal to 3 percent, which represented relatively low levels of development on multiple-use lands managed by the BLM and other land management agencies. The multiscale index of landscape intactness was designed to be flexible, transparent, defensible, and applicable across multiple spatial scales, ecological boundaries, and jurisdictions. To foster transparency and facilitate interpretation, the multiscale index of landscape intactness data release retains four component data sets to enable users to interpret the multiscale index of landscape intactness: the surface disturbance footprint, the terrestrial development index summarized at two scales (2.5-km and 20-km circular moving windows), and the overall landscape intactness index. The multiscale index is a proposed core indicator to quantify landscape integrity for the BLM Assessment, Inventory, and Monitoring program and is intended to be used in conjunction with additional regional- or local-level information not available at national levels (such as invasive species occurrence) necessary to evaluate ecological integrity for the BLM landscape approach. 1 Roads, Railroads, and utility lines were mapped using Topologically Integrated Geographic Encoding and Referencing (TIGER) (https://www.census.gov/geo/maps-data/data/tiger.html) 2 Oil and gas wells were mapped using IHS Enerdeq Database (https://www.ihs.com/products/oil-gas-tools-enerdeq-browser.html); Solar energy was mapped using Surface area of solar arrays in the conterminous United States (https://www.sciencebase.gov/catalog/item/57a25271e4b006cb45553efa); Wind energy was mapped using Onshore industrial wind turbine locations for the U.S. http://energy.usgs.gov/OtherEnergy/WindEnergy.aspx#4312358-data); Oil and gas pipelines were mapped using the National Pipeline Mapping System (https://www.npms.phmsa.dot.gov/) 3 Surface mines and quarries were mapped using National Gap Analysis Program, Level 3 data (http://gapanalysis.usgs.gov/gaplandcover/data/download/) 4 Cultivated croplands were mapped using National Agriculture Statistical Service Cultivated Crop Layer (http://www.nass.usda.gov/research/Cropland/Release/index.htm) 5 Urban development was mapped using National Land Cover Dataset Impervious surface (Homer and others, 2015). References Carr, N.B., Leinwand, I.I.F. and Wood, D.J.A., In review. A multiscale index of landscape intactness for management of public lands in Carter, S.K., Carr, N.B., and Wood, eds, Developing multiscale tools and guidance for a landscape approach to resource management for the Bureau of Land Management. USGS Circular XXX Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354.

U.S. Census Bureau QuickFacts statistics for New Brunswick city, New Jersey. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

The Marine Biodiversity Observation Network Pole to Pole (MBON P2P) effort seeks to develop a framework for the collection, use and sharing of marine biodiversity data in a coordinated, standardized manner leveraging on existing infrastructure managed by the Global Ocean Observing System (GOOS; IOC-UNESCO), the GEO Biodiversity Observation Network (GEO BON), and the Ocean Biogeographic Information System (OBIS). The MBON Pole to Pole aims to become a key resource for decision-making and management of living resource across countries in the Americas for reporting requirements under the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Aichi Targets of the Convention of Biological Diversity (CBD), and the UN 2030 Agenda for Sustainable Development Goals (SDGs). This collection corresponds to the species registered on sandy beaches of the Musquash Harbour, Mispec Bay, and New River Beach, New Brunswick, Canada, using the MBON P2P sampling protocol for sandy beaches, with funding from the Government of Canada's Coastal Environmental Baseline Program. Citation: Reinhart B (2024). MBON POLE TO POLE: SANDY BEACH BIODIVERSITY OF SOUTHWEST NEW BRUNSWICK, CANADA. Version 1.5. Caribbean OBIS Node. Samplingevent dataset. https://ipt.iobis.org/mbon/resource?r=sandybeachesbayoffundynb&v=1.5

The MBON Pole to Pole effort seeks to develop a framework for the collection, use and sharing of marine biodiversity data in a coordinated, standardized manner leveraging on existing infrastructure managed by the Global Ocean Observing System (GOOS; IOC-UNESCO), the GEO Biodiversity Observation Network (GEO BON), and the Ocean Biogeographic Information System (OBIS). The MBON Pole to Pole aims to become a key resource for decision-making and management of living resource across countries in the Americas for reporting requirements under the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Aichi Targets of the Convention of Biological Diversity (CBD), and the UN 2030 Agenda for Sustainable Development Goals (SDGs). This collection corresponds to the species registered on sandy beaches of the Musquash Harbour, Mispec Bay, and New River Beach, New Brunswick, Canada, using the MBON P2P sampling protocol for sandy beaches, with funding from the Government of Canada's Coastal Environmental Baseline Program.

This dataset displays the geographic areas within which critical habitat for terrestrial species at risk, listed on Schedule 1 of the federal Species at Risk Act (SARA), occurs in Atlantic Canada: Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland and Labrador. Note that this includes only terrestrial species and species for which Environment and Climate Change Canada is the lead. However, not all of the area within these boundaries is necessarily critical habitat. To precisely define what constitutes critical habitat for a particular species it is essential that this geo-spatial information be considered in conjunction with the information provided in a species’ recovery document. Recovery documents are available from the Species at Risk (SAR) Public Registry (http://www.sararegistry.gc.ca). The recovery documents contain important information about the interpretation of the geo-spatial information, especially regarding the biological and environmental features (“biophysical attributes”) that complete the definition of a species’ critical habitat.

Each species’ dataset is part of a larger collection of critical habitat data for all terrestrial species in Atlantic Canada that is available for download. The collection includes critical habitat as it is depicted in final recovery documents. It is important to note that recovery documents, and therefore critical habitat, may be amended from time to time. Also, new species can be added to Schedule 1 of SARA and thus new critical habitat described when additional recovery documents are posted on the SAR Public Registry. As critical habitat is amended, this dataset will be updated; however the SAR Public Registry (http://www.sararegistry.gc.ca) should always be considered the definitive source for critical habitat information.

In cases where the data is sensitive (e.g. some turtle species), the geographic area within which critical habitat occurs may be represented as “grid squares”. These are coarse (1, 10, 50 or 100 km2) squares based on a UTM grid that serve as a flag to review the associated species’ recovery document. To reiterate, not all of the area within these boundaries is necessarily critical habitat.

Critical habitat is defined in the federal Species at Risk Act (SARA) as “the habitat that is necessary for the survival or recovery of a listed wildlife species and that is identified as the species’ critical habitat in the recovery strategy or action plan for the species”. Critical habitat identification alone is not an automatic “protection” designation. Federal or non-federal laws or bylaws may be in place to provide protection.

This dataset displays the geographic areas within which critical habitat for terrestrial species at risk, listed on Schedule 1 of the federal Species at Risk Act (SARA), occurs in Atlantic Canada: Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland and Labrador. Note that this includes only terrestrial species and species for which Environment and Climate Change Canada is the lead. However, not all of the area within these boundaries is necessarily critical habitat. To precisely define what constitutes critical habitat for a particular species it is essential that this geo-spatial information be considered in conjunction with the information provided in a species’ recovery document. Recovery documents are available from the Species at Risk (SAR) Public Registry (http://www.sararegistry.gc.ca). The recovery documents contain important information about the interpretation of the geo-spatial information, especially regarding the biological and environmental features (“biophysical attributes”) that complete the definition of a species’ critical habitat. Each species’ dataset is part of a larger collection of critical habitat data for all terrestrial species in Atlantic Canada that is available for download. The collection includes critical habitat as it is depicted in final recovery documents. It is important to note that recovery documents, and therefore critical habitat, may be amended from time to time. Also, new species can be added to Schedule 1 of SARA and thus new critical habitat described when additional recovery documents are posted on the SAR Public Registry. As critical habitat is amended, this dataset will be updated; however the SAR Public Registry (http://www.sararegistry.gc.ca) should always be considered the definitive source for critical habitat information. In cases where the data is sensitive (e.g. some turtle species), the geographic area within which critical habitat occurs may be represented as “grid squares”. These are coarse (1, 10, 50 or 100 km2) squares based on a UTM grid that serve as a flag to review the associated species’ recovery document. To reiterate, not all of the area within these boundaries is necessarily critical habitat. Critical habitat is defined in the federal Species at Risk Act (SARA) as “the habitat that is necessary for the survival or recovery of a listed wildlife species and that is identified as the species’ critical habitat in the recovery strategy or action plan for the species”. Critical habitat identification alone is not an automatic “protection” designation. Federal or non-federal laws or bylaws may be in place to provide protection.

This is a lookup file between electoral wards/divisions and local authority districts in the United Kingdom as at 31st December 2021. (File Size - 294 KB) Field Names - WD21CD, WD21NM, LAD21CD, LAD21NM, FIDField Types - Text, Text, Text, TextField Lengths - 9, 53, 9, 36FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. NB: Electoral Change Orders operative on 7th May 2020 postponed until May 2021

This dataset displays the geographic areas within which critical habitat for terrestrial species at risk, listed on Schedule 1 of the federal Species at Risk Act (SARA), occurs in Atlantic Canada: Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland and Labrador. Note that this includes only terrestrial species and species for which Environment and Climate Change Canada is the lead. However, not all of the area within these boundaries is necessarily critical habitat. To precisely define what constitutes critical habitat for a particular species it is essential that this geo-spatial information be considered in conjunction with the information provided in a species' recovery document. Recovery documents are available from the Species at Risk (SAR) Public Registry (http://www.sararegistry.gc.ca). The recovery documents contain important information about the interpretation of the geo-spatial information, especially regarding the biological and environmental features ("biophysical attributes") that complete the definition of a species' critical habitat.

Each species' dataset is part of a larger collection of critical habitat data for all terrestrial species in Atlantic Canada that is available for download. The collection includes critical habitat as it is depicted in final recovery documents. It is important to note that recovery documents, and therefore critical habitat, may be amended from time to time. Also, new species can be added to Schedule 1 of SARA and thus new critical habitat described when additional recovery documents are posted on the SAR Public Registry. As critical habitat is amended, this dataset will be updated; however the SAR Public Registry (http://www.sararegistry.gc.ca) should always be considered the definitive source for critical habitat information.

In cases where the data is sensitive (e.g. some turtle species), the geographic area within which critical habitat occurs may be represented as "grid squares". These are coarse (1, 10, 50 or 100 km2) squares based on a UTM grid that serve as a flag to review the associated species' recovery document. To reiterate, not all of the area within these boundaries is necessarily critical habitat.

Critical habitat is defined in the federal Species at Risk Act (SARA) as "the habitat that is necessary for the survival or recovery of a listed wildlife species and that is identified as the species' critical habitat in the recovery strategy or action plan for the species". Critical habitat identification alone is not an automatic "protection" designation. Federal or non-federal laws or bylaws may be in place to provide protection. For more information, visit: https://open.canada.ca/data/en/dataset/b4631909-c7fd-474c-8246-c2d37847c107

The map title is Canada. Tactile map scale. 2.8 centimetres = 500 kilometres North arrow pointing to the top of the page. Provincial and Political borders, shown as dashed and solid lines. The Oceans and Lakes, shown with a wavy symbol to indicate water. Labels for Yukon Territory abbreviated to YT. Northwest Territories abbreviated NT. Nunavut abbreviated to NU. British Columbia abbreviated to BC. Saskatchewan abbreviated to SK. Alberta abbreviated to AB. Manitoba abbreviated to MB. Ontario abbreviated to ON. Quebec abbreviated to QC. Newfoundland and Labrador abbreviated to NF. Prince Edward Island abbreviated to PE. Nova Scotia abbreviated to NS. New Brunswick abbreviated to NB. Greenland United States of America abbreviated to USA. Alaska abbreviated to AK. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

BackgroundNeuroblastoma (NB) is the most common extracranial solid tumor in children, and the AURKA gene encodes a protein kinase involved in cell cycle regulation that plays an oncogenic role in a variety of human cancers. The aim of this study was to validate the biological function and prognostic significance of AURKA in NB using basic experiments and bioinformatics.MethodsData obtained from Target and GEO databases were analyzed using various bioinformatic techniques. The expression of AURKA in 77 NB samples was detected by immunohistochemistry (IHC) method. The lentiviral RNA interference technique was employed to downregulate AURKA gene expression in NB cell lines. Additionally, cell counting kit-8 and flow cytometry analysis were conducted to investigate the impact of AURKA expression on cell proliferation, cell cycle progression, and apoptosis.ResultsA bioinformatic analysis showed that patients with NB in the AURKA-high-expression group had shorter OS (Overall Survival). Immune cell infiltration analysis showed that only activated CD4 T cell and type 2 T helper cell infiltration levels were higher in the AURKA-high-expression group than in the AURKA-low-expression group, with the infiltration levels of most other immune cells and cytokines lower in the high-expression group. Furthermore, the enhanced infiltration of activated CD4 T cells was associated with worse OS in patients with NB.IHC results showed that the AURKA expression was correlated with MYCN status and INSS stage. Log-rank test showed that pathological type, MYCN status, INSS stage, COG risk group, and AURKA expression was related to PFS (Progression-free survival) of NB patients, but COX regression analysis showed that none of the above factors were independently prognostic for PFS.In vitro, shRNA delivered via an AURKA-specific lentivirus significantly and consistently silenced endogenous AURKA expression in the human NB cell line SK-N-AS. This inhibited tumor cell proliferation, induced apoptosis, and caused G2/M-phase cell cycle arrest. Moreover, western blot assay showed significant reductions in the levels of mTOR, p70S6K, and 4E-BP1 phosphorylation in the AURKA-knockdown group. I found in subsequent experiments that NFYB can bind to the AURKA promoter and thus promote AURKA expression.ConclusionsHigh-level AURKA expression in NB is associated with poor patient prognosis. Silencing AURKA inhibited tumor cell proliferation, induced tumor cell apoptosis, and led to cell cycle arrest in the G2/M phase. Mechanistically, AURKA knockdown inhibited the phosphorylation and the activation of the mTOR1/p70S6K/4E-BP1 signaling pathway. In addition, AURKA was observed to regulate the infiltration levels of various immune cells in the NB tumor microenvironment, resulting in remodeling of the immunosuppressive tumor microenvironment.