Approximate boundaries for all land parcels in New Brunswick. The boundaries are structured as Polygons. The Property Identifier number or PID is included for each parcel.

**Data purpose: **Provide a geo-referenced civic address database in support of government program delivery especially emergency services but also many business applications including package delivery.

**Data description: **Point data with complete civic address attributes that do not include postal code. Street address, civic number and suffix, PID number, community and county name, latitude and longitude coordinates, a unique civic ID for each point and other attributes are included.

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 dataset includes a metadata file providing the following information for each campaign:

date of collection;

start time and end time of collection;

length;

type (walking/driving).

Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.

The dataset contains the following data for NB-IoT:

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

NB-IoT_coverage_C.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);

NB-IoT_RefSig_cir_C.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.

Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by pairs. Each data point provides RF and CIR maximum delay measurements for each unique combination detected at the coordinates of the data point.

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 , the files are:

5G_coverage_C.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);

5G_RefSig_cir_C.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.

Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by pairs. Each data point provides RF and CIR maximum delay measurements for each unique combination detected at the coordinates of the data point.

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 additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:

A campaign-by-campaign linear interpolation (both NB-IoT and 5G);

A bidimensional interpolation on all campaigns combined (NB-IoT only).

A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach 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. DOI: 10.3390/s23094266.

The second interpolation approach is instead introduced and described in:

L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.

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," IEEE Communications Magazine, Volume 62, Issue 5, pp. 44-49, May 2024. DOI: 10.1109/MCOM.011.2200707.

G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: 10.1109/OJCOMS.2024.3499370.

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

This dataset includes data for NB-IoT networks as collected in two cities: Oslo, Norway and Rome, Italy.

Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome.

The dataset contains the following data:

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

In addition, in the case of the Rome data a script to interpolate missing data in the original data is provided, as well as the corresponding interpolated data 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.

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

An Open Context "subjects" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Radiocarbon Sample" record is part of the "Cross-referenced p3k14c" data publication.

Georeferenced Civic Address Data Base (GCADB)

Data purpose: Provide a geo-referenced civic address database in support of government program delivery especially emergency services but also many business applications including package delivery.
Data description: Point data with complete civic address attributes but does not include postal code. PID number is included. Update requirements: Monthly Georeferencing: datum - NAD83(CSRS), map projection - NB Stereographic Double, (EPSG 2953) Data coverage and size: Provincial, 10 mb Responsible Agency: Department of Public Safety

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

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.

In this lesson, you will explore the Parks Canada system that includes Historic Sites, National Parks and National Marine Conservation Areas. These natural and historic features are significant because they represent the natural and cultural heritage of Canada. The Parks Canada System Web GIS Lesson Pack can be found here: http://k12.esri.ca/resourcefinder/data/files/ParksCanada_Web.zipNote: This assignment can be used as a GIS extension exercise for the Parks Canada: Places and Spaces for Everyone Activity 1 – The Parks Canada System. It is part of the Canadian Geographic Education’s Giant Floor Map Program.

Learning Outcomes

By completing this lesson, students will gain, by Province/Territory, Grade and Subject, the following curriculum-focused knowledge:

Identify and describe various characteristics of Canada’s natural environment (AB - Grade 4, 5 Social Studies; BC – Grade 5 Social Studies; MB - Grade 4, 9 Social Studies; ON - Grade 4, 9 Geography; YT – Grade 5 Social Studies; QC – Elementary, Cycle 2 Social Sciences)

Determine the spatial distribution of Parks Canada’s natural and historic features (AB - Grade 4, 5 Social Studies; MB - Grade 4, 9 Social Studies ON - Grade 4, 9 Geography; NB - Grade 4, 9 Social Studies; PE - Grade 4, 8, 10 Social Studies; NS - Grade 4, 9 Social Studies; NL - Grade 4, 9 Social Studies)

 3.  Understand how Parks

Canada’s features are connected to the local ecology and to human characteristics, such as population density and the location of cities (AB -Grade 4, 5 Social Studies; MB - Grade 9 Social Studies; ON - Grade 4 Social Studies, 9 Geography; NB – Grade 4, 9 Social Studies; NL - Grade 4, 9 Social Studies; NS - Grade 4, 9 Social Studies; NWT 4, 5 Social Studies; PE – Grade 4, 8, 10 Social Studies; QC- Elementary, Cycle 3 Social Sciences, Secondary, Cycle 1 Geography)

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.

In this lesson, students will use ArcGIS Online to investigate the human and physical characteristics of the areas where the mining resource of their choice is located.

View the lesson here: http://bit.ly/2qn5k2p

Learning Outcomes

By completing this assignment, students will be able to gain, by Province/Territory, Grade and Subject, the following curriculum-focused knowledge:

1. Use GIS to identify where mines are located in Canada

(British Columbia – Grade 5 Social Studies; Ontario - Grade 9 Geography, Grade 12 Resource management, New Brunswick - Grade 8 Social Studies, Grade 12 Geography; Manitoba - Grade 10 Social Studies; Newfoundland and Labrador -Grade 9 Social Studies; Nova Scotia - Grade 9 Geography; Prince Edward Island - Grade 9 Social Studies; Saskatchewan - Grade 8 Social Studies)

1. Understand the human and physical characteristics of the area where your selected mined resource is located

(British Columbia – Grade 5 Social Studies; Ontario - Grade 9 Geography, Grade 12 Resource management; Manitoba - Grade 10 Social Studies; Newfoundland and Labrador -Grade 9 Social Studies; Nova Scotia - Grade 9 Geography; Prince Edward Island - Grade 9 Social Studies; Quebec- Secondary, Cycle 1 Geography)

1. Describe how landforms influence cultural and economic activities in Canada

(Ontario - Grade 9 Geography, Grade 12 Resource management, New Brunswick - Grade 8 Social Studies, Grade 12 Geography; Manitoba - Grade 10 Social Studies; Newfoundland and Labrador -Grade 9 Social Studies; Nova Scotia - Grade 9 Geography; Prince Edward Island - Grade 9 Social Studies; Saskatchewan - Grade 8 Social Studies)

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.

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 rocky shores of the Musquash Harbour and Mispec Bay, New Brunswick, Canada, using the MBON P2P sampling protocol for rocky shores, 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.