The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Parking citations with latitude / longitude (XY) in US Feet coordinates according to the NAD_1983_StatePlane_California_V_FIPS_0405_Feet projection.

New Parking Citations dataset here: https://data.lacity.org/Transportation/Parking-Citations/4f5p-udkv/about_data ---Archived as of September 2023--- Parking citations with latitude / longitude (XY) in US Feet coordinates according to the California State Plane Coordinate System - Zone 5 (https://www.conservation.ca.gov/cgs/rgm/state-plane-coordinate-system). For more information on Geographic vs Projected coordinate systems, read here: https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/gcs_vs_pcs/ For information on how to change map projections, read here: https://learn.arcgis.com/en/projects/make-a-web-map-without-web-mercator/

This data set provides a means of identifying an x-y coordinate for the approximate center (centroid) of landnet units based on the corresponding standardized PLSS description (e.g., for PLSS Section this is DTRS -- Direction, Township, Range, and Section codes). This process is sometimes referred to as "protraction". The Landnet centroid shapefile includes coordinates in WTM83/91 and latitude/longitude expressed as decimal degrees or degrees, minutes and seconds.

Loudoun County Parcel X,Y coordinates table. Available in Latitude and Longitude decimal degrees and Virginia State Plane North.

• Data Set Overview
• =================

This Data Set contains Voyager 1 Magnetometer Data from the Interplanetary Cruise averaged to 48 s Samples in Heliographic Coordinates.

• Data Processing
• ===============

The high resolution Data submitted to the NSSDC has its origins in the original MVS 'Summary' Data Sets formally produced on the NSSC IBM MVS Mainframe System. The original Data Sets contain a Mix of Engineering, Electron, Magnetic Field and Plasma Data. The Voyager Magnetometer (MAG) Experiment now resides on a dedicated Workstation where only MAG Data are processed.

All Voyager MAG data submitted to the NSSDC consists wholly of high resolution LFM Averages. These Files consist of a Set of Averages applied across all of the differing Telemetry Modes of the Mission. In the Case of the Magnetometer Experiment, the Records contain both 1.92 s, 9.6 s and 48 s Averages. 1.92 s Averages are created from the detail Detail Data, 9.6 s Averages are created from 1.92 s Averages and 48 s Averages are created from the 9.6 s Averages.

All Data in this NSSDC Data Set are Interplanetary and in Heliographic Coordinates (see below). An ASCII formated Data Set containing Key Components of the 48 s Magnetic Field Data and Ephemeris Data has been created allowing more convenient access to high resolution Voyager Magnetometer Data.

• Coordinate System
• =================

Interplanetary Magnetic Field studies make use of two important Coordinate Systems, the Inertial Heliographic (IHG) Coordinate System and the Heliographic (HG) Coordinate System.

The IHG Coordinate System is used to define the Position of the Spacecraft. The IHG System is defined with its Origin at the Sun. There are three Orthogonal Axes, X(IHG), Y(IHG), and Z(IHG). The Z(IHG) Axis points northward along the Spin Axis of the Sun. The XY Plane of the IHG System lays in the Solar Equatorial Plane. The Intersection of the Solar Equatorial Plane with the Ecliptic Plane defines a Line, the Longitude of the Ascending Node, which is taken to be the X(IHG) Axis. The X(IHG) Axis drifts slowly with Time, approximately one degree per 72 years.

The Magnetic Field Orientation is defined in relation to the Spacecraft. Drawing a Line from the Center of the Sun, which is the Origin of the IHG System, to the Spacecraft defines the X Axis of the HG Coordinate System. The HG Coordinate System is defined with its Origin centered at the Spacecraft. Three orthogonal Axes are defined, X(HG), Y(HG), and Z(HG). The X(HG) Axis points radially away from the Sun and the Y(HG) Axis is parallel to the Solar Equatorial Plane and therefore parallel to the X(IHG)-Y(IHG) Plane as well. The Z(HG) Axis is chosen to complete the Orthonormal Triad.

An excellent Reference Guide with Diagrams explaining the IHG and HG Systems may be found in Space and Science Reviews, Volume 39 (1984), 255-316, MHD Processes in the Outer Heliosphere, L. F. Burlaga.

• Data Format
• ===========

+---------------------------------------------------------------------+

| Num | Field | Description for Data before 1990 |

| 1 | Spacecraft ID | FLT1=Voyager 1, FLT2=Voyager 2 | | 2 | Coordinate System | Heliographic, HG | | 3 | Time (UTC) | Format: YY DDD HH MM SS MSS | | 4 | Field Magnitude | in nT | | 5 | Field Component 1 | in nT, HG Coordinates | | 6 | Field Component 2 | in nT, HG Coordinates | | 7 | Field Component 3 | in nT, HG Coordinates | | 8 | Spacecraft Radial Distance | in AU | | 9 | Spacecraft X Position | in AU, IHG Coordinates | | 10 | Spacecraft Y Position | in AU, IHG Coordinates | | 11 | Spacecraft Z Position | in AU, IHG Coordinates | +---------------------------------------------------------------------+

Time Format Definitions: YY=Year, DDD=Day of Year, HH=Hour, MM=Minute, SS=Second, MSS=Millisecond

+-------------------------------------------------------------------------------------------------+

| Num | Field | Description for Termination Shock Data |

| 1 | Spacecraft ID | 1=Voyager 1, 2=Voyager 2 | | 2 | Time (UTC) | Format: YYYY DDD.DDDD, YY=Year, DDD.DDDD=Decimal Day of Year | | 3 | F1 Average Field Magnitude | in nT | | 4 | Br Field Component | in nT, HG Coordinates | | 5 | Bt Field Component | in nT, HG Coordinates | | 6 | Bn Field Component | in nT, HG Coordinates | | 7 | F1 1-σ Error | in nT | | 8 | Br 1-σ Error | in nT, HG Coordinates | | 9 | Bt 1-σ Error | in nT, HG Coordinates | | 10 | Bn 1-σ Error | in nT, HG Coordinates | +-------------------------------------------------------------------------------------------------+

The GeoPinpoint Suite software attaches geographic coordinates to records in a client database by means of matching certain database fields against a DMTI proprietary geo-reference database. The geo- reference database is comprised of digital street geometry, street address ranges, postal coordinates, point of interest and other reference databases to ensure that data is “geocoded” as accurately as possible. When data is “geocoded”, co-ordinates can be transferred into a Geographic Information Systems (GIS) such as MapInfo, ArcInfo, ArcView and other software systems that support the importation of geographic co-ordinate locations. GeoPinpointTM Suite positions your data using a powerful and innovative geo-location process called geocoding. GeoPinpoint Suite attaches X and Y coordinates to your facility, customer or prospect address data for map visualization, analysis or location based applications. The GeoPinpoint Suite takes advantage of a new modular design that allows the software to encompass future module enhancements without jeopardizing its performance or usability. Based on the nationwide precision and the robust street address content of CanMap® Streetfiles, GeoPinpoint Suite has been engineered to geocode your data with a high degree of accuracy.

Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) File name "eGAdeposit_233CaboVerde_GEOcoordFULL_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file Column 2-3 corresponds to X-Y GPS coordinates of individual's interview location in Cabo Verde Column 4-5 corresponds to X-Y GPS coordinates of individual's self-reported residence location at the time of the interview Column 6-7 corresponds to X-Y GPS coordinates of individual's self-reported birth-place location Column 8-9 corresponds to X-Y GPS coordinates of individual's self-reported paternal birth-place location Column 10-11 corresponds to X-Y GPS coordinates of individual's self-reported maternal birth-place location

This dataset contains all basic primary school details including address, longitude/latitude, geo (XY) coordinates and eircode information

Deze dataset kan gebruikt worden om te testen of de transformatie van Lambert 72 naar Lambert 2008 coördinaten verloopt volgens EPSG:8369 'BD72 to ETRS89 (3) 0.01m accuracy'. EPSG:8369 is de meest precieze transformatiemethode beschikbaar is in GIS-toepassingen en gebruikt hiervoor het NTV2 transformatiegrid van Nicolas SIMON van SPW. De dataset bevat 177K punten met een geometrie volgens het Lambert 72 (EPSG:31370) coördinaatreferentiesysteem (CRS) én xy-coordinaten volgens het Lambert 2008 (EPSG:3812) CRS omgezet met de normatieve cConvert-toepassing van het Nationaal Geografisch Instituut (NGI). De testprocedure verloopt in 3 stappen: 1. Transformeer geom van LB72 naar L08; 2. Bereken de afstand met cConvert-coördinaten; en 3. Verifieer dat de afstand =< 0.011m. Zie ook de slides 'OIS Kennisuitwisseling Lambert 2008' op https://www.milieuinfo.be/confluence/pages/viewpage.action?pageId=256574974

This metadata record describes the 3-Dimensional coordinates of individual krill obtained from the ‘Swarm study’ at-sea deployment of stereo-cameras. An array of 10 Gopro Hero-8 cameras (2 cameras on each lateral side of a cubic frame, and a pair on the bottom) was deployed into a krill swarm during the 2021 TEMPO (Trends in Euphausiids off Mawson, Predators, and Oceanography) voyage onboard the RV Investigator (see Kelly et al. 2021 for more details on the voyage). The primary goal of TEMPO was to collect data to estimate krill biomass with a view to update the precautionary catch limit for krill in CCAMLR’s Division 58.4.2-East. The aim of the Swarm study was to replicate the methods of Burns et al., 2023, in order to examine the individual level behaviour of krill in swarms in natural conditions. The TEMPO voyage ran from February to March 2021. The Swarm study system was deployed when a krill swarm was identified from the ships echosounder and weather and sea conditions allowed for filming (i.e. minimal to no wind or swell, full sun).

This data product contains coordinates of individual krill in three dimensions from a pair of stereo cameras. There are 2 datasheets: TEMPO_Orientation TEMPO_Tracks TEMPO_Orientation: this dataset contains the head and tail coordinate of 305 individual krill obtained from videos deployed into a krill swarm date: date video obtained deployment: deployment no. of swarm study panel: panel cameras attached to (for video ID) left/rightcam: for ID camera_dist: horizontal distance between the two cameras left/rightvid: video filename framediff: difference in frames between left and right camera still: still photo reference number that orientation data was obtained from frame (leftvid): corresponding frame for still pointref: point of krill where coordinate (head or tail) id: individual krill x1/y1: xy coordinates from left camera x2/y2: xy coordinates from right camera convertedXYZ: converted coordinates into 3Dimensional TEMPO_Tracks date: date video obtained deployment: deployment no. of swarm study panel: panel cameras attached to (for video ID) left/rightcam: for ID camera_dist: horizontal distance between the two cameras left/rightvid: video filename framediff: difference in frames between left and right camera fps: video frame rate per second clip: clip reference number frame: clip frame reference pointref: point of krill where coordinate (head or tail) x1/y1: xy coordinates from left camera x2/y2: xy coordinates from right camera on separate tabs: clip#: matched pairs of XY coordinates from the 2 cameras clip#XYZ: corresponding converted XYZ coordinates

References Burns, A.L., Schaerf, T.M., Lizier, J., Kawaguchi, S., Cox, M., King, R., Krause, J. and Ward, A.J., 2022. Self-organization and information transfer in Antarctic krill swarms. Proceedings of the Royal Society B, 289(1969), p.20212361.

Kelly, N., Bestley, S., Burns, A., Clarke, L., Collins, K., Cox, M., Hamer, D., King, R., Kitchener, J., Macaulay, G., Maschette, D., Melvin, J., Miller, B., Smith, A., Suter, L., Westwood, K., Wotherspoon, S. and Kawaguchi, S. (2021). An overview of the ecosystem survey to quantify krill abundance for krill monitoring and management in Eastern Sector of CCAMLR Division 58.4.2: Trends in Euphausiids off Mawson, Predators, and Oceanography “TEMPO”, Working Group on Ecosystem Monitoring and Management, CCAMLR, WG-EMM-2021/07, 26pp.

The Taipei City pedestrian fixed facilities related to the location data of box facilities include name, administrative district, XY coordinates, geometric location data type, and geometric position relative coordinates. The coordinate system adopted for this data is TWD97.

This is the data of XY coordinates of particles in the film.

This raster dataset represents approximately 49,581 square kilometers of Simrad EM122 multibeam backscatter-intensity data collected in the Bering Sea during U.S. Geological Survey (USGS) cruise MGL1111 aboard the R/V Marcus G. Langseth. Calibrated backscatter-intensity time-series data were adjusted for range-angle, beam pattern, and power-gain distortions.

If you use this dataset, please cite the IJRR data paper (bibtex is below). We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an IMU, and a GPS device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The data is divided into subsets, which can be downloaded individually. Video previews are available on Youtube: https://www.youtube.com/channel/UCOU9e7xxqmL_s4QX6jsGZSw The information below can also be found in the README files provided in the 527 dataset and each of its subsets. The purpose of this document is to assist researchers in using this dataset. Images ====== Raw --- The raw images are stored in the cam0 and cam1 directories in bmp format. They are bayered images that need to be debayered and undistorted before they are used. The camera parameters for these images can be found in camchain-imucam.yaml. Note that the camera intrinsics describe a 1600x1200 resolution image, so the focal length and center pixel coordinates must be scaled by 0.5 before they are used. The distortion coefficients remain the same even for the scaled images. The camera to imu tranformation matrix is also in this file. cam0/ refers to the left camera, and cam1/ refers to the right camera. Rectified --------- Stereo rectified, undistorted, row-aligned, debayered images are stored in the rectified/ directory in the same way as the raw images except that they are in png format. The params.yaml file contains the projection and rotation matrices necessary to use these images. The resolution of these parameters do not need to be scaled as is necessary for the raw images. params.yml ---------- The stereo rectification parameters. R0,R1,P0,P1, and Q correspond to the outputs of the OpenCV stereoRectify function except that 1s and 2s are replaced by 0s and 1s, respectively. R0: The rectifying rotation matrix of the left camera. R1: The rectifying rotation matrix of the right camera. P0: The projection matrix of the left camera. P1: The projection matrix of the right camera. Q: Disparity to depth mapping matrix T_cam_imu: Transformation matrix for a point in the IMU frame to the left camera frame. camchain-imucam.yaml -------------------- The camera intrinsic and extrinsic parameters and the camera to IMU transformation usable with the raw images. T_cam_imu: Transformation matrix for a point in the IMU frame to the camera frame. distortion_coeffs: lens distortion coefficients using the radial tangential model. intrinsics: focal length x, focal length y, principal point x, principal point y resolution: resolution of calibration. Scale the intrinsics for use with the raw 800x600 images. The distortion coefficients do not change when the image is scaled. T_cn_cnm1: Transformation matrix from the right camera to the left camera. Sensors ------- Here, each message in name.csv is described ###rawimus### time # GPS time in seconds message name # rawimus acceleration_z # m/s^2 IMU uses right-forward-up coordinates -acceleration_y # m/s^2 acceleration_x # m/s^2 angular_rate_z # rad/s IMU uses right-forward-up coordinates -angular_rate_y # rad/s angular_rate_x # rad/s ###IMG### time # GPS time in seconds message name # IMG left image filename right image filename ###inspvas### time # GPS time in seconds message name # inspvas latitude longitude altitude # ellipsoidal height WGS84 in meters north velocity # m/s east velocity # m/s up velocity # m/s roll # right hand rotation about y axis in degrees pitch # right hand rotation about x axis in degrees azimuth # left hand rotation about z axis in degrees clockwise from north ###inscovs### time # GPS time in seconds message name # inscovs position covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz m^2 attitude covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz deg^2 velocity covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz (m/s)^2 ###bestutm### time # GPS time in seconds message name # bestutm utm zone # numerical zone utm character # alphabetical zone northing # m easting # m height # m above mean sea level Camera logs ----------- The files name.cam0 and name.cam1 are text files that correspond to cameras 0 and 1, respectively. The columns are defined by: unused: The first column is all 1s and can be ignored. software frame number: This number increments at the end of every iteration of the software loop. camera frame number: This number is generated by the camera and increments each time the shutter is triggered. The software and camera frame numbers do not have to start at the same value, but if the difference between the initial and final values is not the same, it suggests that frames may have been dropped. camera timestamp: This is the cameras internal timestamp of the frame capture in units of 100 milliseconds. PC timestamp: This is the PC time of arrival of the image. name.kml -------- The kml file is a mapping file that can be read by software such as Google Earth. It contains the recorded GPS trajectory. name.unicsv ----------- This is a csv file of the GPS trajectory in UTM coordinates that can be read by gpsbabel, software for manipulating GPS paths. @article{doi:10.1177/0278364917751842, author = {Martin Miller and Soon-Jo Chung and Seth Hutchinson}, title ={The Visual–Inertial Canoe Dataset}, journal = {The International Journal of Robotics Research}, volume = {37}, number = {1}, pages = {13-20}, year = {2018}, doi = {10.1177/0278364917751842}, URL = {https://doi.org/10.1177/0278364917751842}, eprint = {https://doi.org/10.1177/0278364917751842} }

PLSS Townships and Sections dataset current as of 2008. Public Land Survey square-mile section boundaries within Sedgwick County. Layer was developed interactively by GIS staff. Primary attribues include section, township, and range identifiers, and x-y coordinates, and Public Safety (ortho) map numbers..

Summary Rail Crossings is a spatial file maintained by the Federal Railroad Administration (FRA) for use by States and railroads. Description FRA Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing, Inventory Program. The program is to provide information to Federal, State, and local governments, as well as the railroad industry for the improvements of safety at highway-rail crossing. Credits Federal Railroad Administration (FRA) Use limitations There are no access and use limitations for this item. Extent West -79.491008 East -75.178954 North 39.733500 South 38.051719 Scale Range Maximum (zoomed in) 1:5,000 Minimum (zoomed out) 1:150,000,000 ArcGIS Metadata ▼►Topics and Keywords ▼►Themes or categories of the resource  transportation * Content type  Downloadable Data Export to FGDC CSDGM XML format as Resource Description No Temporal keywords  2013 Theme keywords  Rail Theme keywords  Grade Crossing Theme keywords  Rail Crossings Citation ▼►Title rr_crossings Creation date 2013-03-15 00:00:00 Presentation formats  * digital map Citation Contacts ▼►Responsible party  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Responsible party  Organization's name Research and Innovative Technology Administration/Bureau of Transportation Statistics Individual's name National Transportation Atlas Database (NTAD) 2013 Contact's position Geospatial Information Systems Contact's role  distributor Contact information  ▼►Phone  Voice 202-366-DATA Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 e-mail address answers@BTS.gov Resource Details ▼►Dataset languages  * English (UNITED STATES) Dataset character set  utf8 - 8 bit UCS Transfer Format Spatial representation type  * vector * Processing environment Microsoft Windows 7 Version 6.1 (Build 7600) ; Esri ArcGIS 10.2.0.3348 Credits Federal Railroad Administration (FRA) ArcGIS item properties  * Name USDOT_RRCROSSINGS_MD * Size 0.047 Location withheld * Access protocol Local Area Network Extents ▼►Extent  Geographic extent  Bounding rectangle  Extent type  Extent used for searching * West longitude -79.491008 * East longitude -75.178954 * North latitude 39.733500 * South latitude 38.051719 * Extent contains the resource Yes Extent in the item's coordinate system  * West longitude 611522.170675 * East longitude 1824600.445629 * South latitude 149575.449134 * North latitude 752756.624659 * Extent contains the resource Yes Resource Points of Contact ▼►Point of contact  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Resource Maintenance ▼►Resource maintenance  Update frequency  annually Resource Constraints ▼►Constraints  Limitations of use There are no access and use limitations for this item. Spatial Reference ▼►ArcGIS coordinate system  * Type Projected * Geographic coordinate reference GCS_North_American_1983_HARN * Projection NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet * Coordinate reference details  Projected coordinate system  Well-known identifier 2893 X origin -120561100 Y origin -95444400 XY scale 36953082.294548117 Z origin -100000 Z scale 10000 M origin -100000 M scale 10000 XY tolerance 0.0032808333333333331 Z tolerance 0.001 M tolerance 0.001 High precision true Latest well-known identifier 2893 Well-known text PROJCS["NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree"

Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication.Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication. You can see the map on Ottawa Public Health's website.Accuracy: Points of consideration for interpretation of the data:Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) on May 12th, 2020. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. Cases are assigned to Ward geography based on their postal code and Statistics’ Canada’s enhanced postal code conversion file (PCCF+) released in January 2020. Most postal codes have multiple geographic coordinates linked to them. Thus, when available, postal codes were attributed to a XY coordinates based on the Single Link Identifier provided by Statistics’ Canada’s PCCF+. Otherwise, postal codes that fall within the municipal boundaries but whose SLI doesn’t, were attributed to the first XY coordinates within Ottawa listed in the PCCF+. For this reason, results for rural areas should be interpreted with caution as attribution to XY coordinates is less likely to be based on an SLI and rural postal codes typically encompass a much greater surface area than urban postal codes (e.i. greater variability in geographic attribution, less precision in geographic attribution). Population estimates are based on the 2016 Census. Rates calculated from very low case numbers are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time.Update Frequency: Biweekly Attributes:Ward Number – numberWard Name – textCumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a Ward, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardCumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward, excluding cases linked to outbreaks in LTCH and RHCumulative number of cases linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19 linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 30 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 30 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHNumber of cases in the last 30 days linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19, reported in the 30 days prior to the data pull, linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 14 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 14 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHContact: OPH Epidemiology Team

Global-Positioning System (GPS) coordinates, population size estimated using Migrate-n analysis, the number of D-loop haplotypes of M. fuliginosus, and GenBank accession numbers of D-loop sequences.

'Graphene_CrSi.npy' contains a scanning transmission electron microscopy (STEM) movie from graphene monolayer. The movie is a sequence of atom-resolved images from the same sample region that undergoes chemical and structural transformations due to interaction with electron beam (which is used to perform imaging). 'topo_defects.npy' contains coordinates of some of the objects of interest (topological defects in graphene). It is a dictionary, where keys are frame numbers and values are xy coordinates. These objects are usually localized in relatively small areas of the image and we are interested in identifying them without having to scan an entire grid (which leads to fast degradation of the sample).