School profile information for all schools in the Chicago Public School district for the school year 2023-2024.

• Data set is no longer being updated when data set for next year is created

School profile information for all schools in the Chicago Public School district for the school year 2017-2018.

School profile information for all schools in the Chicago Public School district for the school year 2016-2017.

How high is the brand awareness of Chicago Town in the UK?When it comes to frozen pizza eaters, brand awareness of Chicago Town is at 85% in the UK. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Chicago Town in the UK?In total, 53% of UK frozen pizza eaters say they like Chicago Town. However, in actuality, among the 85% of UK respondents who know Chicago Town, 62% of people like the brand.What is the usage share of Chicago Town in the UK?All in all, 49% of frozen pizza eaters in the UK use Chicago Town. That means, of the 85% who know the brand, 58% use them.How loyal are the customers of Chicago Town?Around 43% of frozen pizza eaters in the UK say they are likely to use Chicago Town again. Set in relation to the 49% usage share of the brand, this means that 88% of their customers show loyalty to the brand.What's the buzz around Chicago Town in the UK?In October 2022, about 31% of UK frozen pizza eaters had heard about Chicago Town in the media, on social media, or in advertising over the past three months. Of the 85% who know the brand, that's 36%, meaning at the time of the survey there's some buzz around Chicago Town in the UK.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

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University of Chicago - BioCentury Company Profiles for the biopharma industry

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Separate tables are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs)There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary.NOTE: Much of the data is from 5-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

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This dataset contains the ground-truth intersection regulators for all intersections crossed by the open GPS trajectory data of Chicago, published on Mapconstruction.org.

Data Acquisition

The ground-truth data is generated by manually inspecting images at the intersections crossed by the published trajectory dataset on Mapconstruction.org. These images are derived from mapillary.com at the respective intersections. Satellite images are investigated as an additional data source, if needed. This procedure result in labels for each intersection arm.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

Data has been processed by NODC to the NODC standard Bathythermograph (XBT) (C116) format. The C116/C118 format contains temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Cruise information, position, date and time were reported for each observation. The data record was comprised of pairs of temperature-depth values. Unlike the MBT Data File, in which temperature values were recorded at uniform 5 m intervals, the XBT data files contained temperature values at non-uniform depths. These depths were recorded at the minimum number of points ("inflection points") required to accurately define the temperature curve. Standard XBTs can obtain profiles to depths of either 450 or 760 m. With special instruments, measurements can be obtained to 1830 m. Prior to July 1994, XBT data were routinely processed to one of these standard types. XBT data are now processed and loaded directly in to the NODC Ocean Profile Data Base (OPDB). Historic data from these two data types were loaded into the OPDB.

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No description is available. Visit https://dataone.org/datasets/%7BB5C9F3FD-83D7-4449-BC40-0A05DCCB69B3%7D for complete metadata about this dataset.

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