Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: http://doi.org/10.1109/ASEW52652.2021.00049. Kindly cite this paper when using AWARE dataset.

Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of smartphone apps reviews dataset that is annotated to support the ABSA task, we present AWARE: ABSA Warehouse of Apps REviews.

AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows:

Aspect Term: a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence.

Aspect Category: one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.).

Sentiment: positive or negative.

Note: games domain does not contain aspect terms.

We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns.

How AWARE can be used?

We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to:

Sentiment Analysis.

Aspect Term Extraction.

Aspect Category Classification.

Aspect Sentiment Analysis.

Explicit/Implicit Aspect Term Classification.

Opinion/Not-Opinion Classification.

Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback.

In recent years, smartphones and software have developed to help to automate imaging processes and thereby support digitalization in clinical microbiology laboratories. Due to the time-consuming nature of manual bacterial colony counting, diverse apps for smartphones have been developed to quantify the number of colony-forming units (CFUs) on agar plate images.

Currently, at least four different apps with a colony counter function exist. Our study aimed to compare the accuracy of the apps compared to human visual manual counting. The images were acquired using four different colony counters with three apps on an iPhone 11 Pro Max (CFU.Ai, Promega Colony Counter, APD Colony Counter App PRO) and one app on a Samsung Galaxy A52 (@BactLAB). Two different measurement conditions were investigated. Firstly, standardized measurement in a handcrafted wooden apparatus, and secondly "freestyle measurement" manually holding the plate. The apparatus measurements were performed against a black background, apart from @BactLAB on a white background (as per the manufacturer's instructions). We used three different types of media: blood agar, chrome agar, and LB. Four Escherichia coli isolates were used. We prepared a bacterial suspension of each strain, made a six-fold serial 1:10 dilution in saline and spread the dilutions "1:10⁴", "1:10⁵", and "1:10^6" at three different solution volumes (100μl, 50μl, and 25μl) homogeneously across the agar plates. We included a total of 108 agar plates.

This dataset contains all analyzed images. It may possibly be useful in order to assess and improve colony counter apps in the future. On the one hand, the dataset contains raw data (images before processing), on the other hand, it contains screenshots of our app measurement and thus enables accountability of our results (screenshots of all apps were uploaded apart from @BactLAB as from its screenshots nothing can be derived).

Gridded datasets used in Jones et al. (2021) paper 'Agrobiodiversity Index scores show agrobiodiversity is underutilized in national food systems'. Details of how datasets were made and underlying sources are provided in Jones et al. (2021) Supplementary Information. Datasets included: - H_2010_spam_V2r0_42c: crop commodity diversity (Shannon's diversity index) at 10x10km resolution, based on SPAM 2010 V2 physical area maps - sr_2010_spam_v2r0_42c: crop commodity richness at 10x10km resolution, based on SPAM 2010 V2 physical area maps - sr_2010_spam_v2r0_42c_maj22: locations of cropland with at least 22 crop commodities (1) versus cropland with <22 crop commodities at 10x10km resolution, based on SPAM 2010 V2 physical area maps - Livestock_8_shannons_LSU: livestock diversity (Shannon's diversity index) calculated from population numbers converted to standard livestock units at 1x1km resolution, based on Global Livestock of the World v3 - Fish_srichness raster: freshwater fish species richness per major river basin, based on Tedesco et al (2017) - CropPasture_2000_bool: locations where cropland and pasture co-exist (1) versus locations where either cropland OR pasture exist (0), at 10x10km resolution, based on cropland and pasture maps for the year 2000 available from EarthStat - esa2015_natag_1km_pc: percentage of natural or semi-natural vegetation within a 1x1km window around cropped pixels, based on European Space Agency Climate Change Initiative (ESA-CCI) land cover maps for 2015 Not uploaded (no post-processing so data can be accessed at source): - potential soil biodiversity index (see https://esdac.jrc.ec.europa.eu/content/global-soil-biodiversity-atlas) - tree cover on agricultural land (see Zomer et al. 2016 and https://apps.worldagroforestry.org/global-tree-cover/index.html)

The Municipal Freedom of Information and Protection of Privacy Act (MFIPPA) gives individuals the right to ask for access to municipal government information. The Act also protects the privacy of individual's personal information and gives them the right to request their own personal information that may exist in government records.

This data set consists of all formal Freedom of Information requests made to the Region of Waterloo under MFIPPA. Identifying information such as names and addresses have been removed from the data to protect personal identification. A notification such as {name removed} has been indicated where information was removed.

Read about this volunteer-driven effort, access data and apps, and contribute your own testing site data:https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-dataItem details page:https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cbThis view is the originalCOVID-19 Testing Locations in the United States - public dataset. A backup copy also exists:https://giscorps.maps.arcgis.com/home/item.html?id=11fe8f374c344549815a716c8472832f. The parent hosted feature service is the same.This version is symbolized by type of test (molecular, antibody, antigen, or combinations thereof).This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and onfindcovidtesting.com. States and counties are encouraged to include this feature service in their own testing site locator apps as well.Please submit new testing sites or updated testing site information via this form:https://arcg.is/10S1ib. Including this link on your organization's testing site finder web app will allow testing providers to add their own sites directly to the map, improving the accuracy and completeness of the dataset.GISCorps volunteers verify each submission prior to including it in this public view. You can also add your sites in bulk by completing a copy ofthis templateand emailing it to admin@giscorps.org.This dataset is updated daily. All information is sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated byGISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document:https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups:https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

The World Mobile Broadband coverage is a 1km resolution raster grid (1/0) representation of cellular mobile wireless Internet access. The grid is derived from Collins Bartholomew’s GSMA Mobile Coverage Explorer database and was computed using both operators’ submission to Global System for Mobile Communication (GSMA) and OpenCell ID cell tower database. The dataset includes operator’s submission coverage for 3G with strong signal, 4G and 5G, sourced from the network operators from submissions made directly to Collins Bartholomew or to GSMA, from operators who provide roaming detail for inclusion in the GSMA’s Network Coverage Maps web application. This data is supplemented with coverage created from OpenCellID tower locations. These derived locations have been used as the centre points of a radius of coverage: 12 kilometres for GSM networks, and 4km for 3G and 4G networks. No 5G data yet exists in the OpenCellID database. These circles of coverage from each tower have then been merged to create an overall representation of network coverage. The dataset uses 3G, 4G and 5G. The Coverage Data is for research use only and may not be used for commercial purposes; for clarification, the use of the data to support research that is partially funded by a corporate source does not constitute a commercial purpose. The Coverage Data may be represented in research results as static images, tables or text as necessary to convey research findings, and all screen images derived or generated from the Coverage Data should incorporate the following acknowledgement: Coverage Data © Collins Bartholomew and GSMA 2020. The data may not be redistributed in its raw form.

The Municipal Freedom of Information and Protection of Privacy Act (MFIPPA) gives individuals the right to ask for access to municipal government information. The Act also protects the privacy of individual's personal information and gives them the right to request their own personal information that may exist in government records.

This data set consists of all formal Freedom of Information requests made to the Region of Waterloo under MFIPPA. Identifying information such as names and addresses have been removed from the data to protect personal identification. A notification such as {name removed} has been indicated where information was removed.

The Municipal Freedom of Information and Protection of Privacy Act (MFIPPA) gives individuals the right to ask for access to municipal government information. The Act also protects the privacy of individual's personal information and gives them the right to request their own personal information that may exist in government records.This data set consists of all formal Freedom of Information requests made to the Region of Waterloo under MFIPPA. Identifying information such as names and addresses have been removed from the data to protect personal identification. A notification such as {name removed} has been indicated where information was removed.

The Municipal Freedom of Information and Protection of Privacy Act (MFIPPA) gives individuals the right to ask for access to municipal government information. The Act also protects the privacy of individual's personal information and gives them the right to request their own personal information that may exist in government records.

This data set consists of all formal Freedom of Information requests made to the Region of Waterloo under MFIPPA. Identifying information such as names and addresses have been removed from the data to protect personal identification. A notification such as {name removed} has been indicated where information was removed.

The Municipal Freedom of Information and Protection of Privacy Act (MFIPPA) gives individuals the right to ask for access to municipal government information. The Act also protects the privacy of individual's personal information and gives them the right to request their own personal information that may exist in government records.

This data set consists of all formal Freedom of Information requests made to the Region of Waterloo under MFIPPA. Identifying information such as names and addresses have been removed from the data to protect personal identification. A notification such as {name removed} has been indicated where information was removed.

This dataset represents building footprints within the City of Campbell River as polygons. The attributes, elev and height, contain the approximate ground elevation where the building sits, and the approximate height of the building, respectively. These values are in meters. The heights are often an average of roof heights of a building. These building footprints have been derived from aerial photos and Lidar data and therefore their areas and shapes may be skewed. For legal dimensions of a building footprint you must refer to legal building drawings.

This dataset is provided by the City of Campbell River. Use it according to its Access and Use Constraints. It may contain errors as it represents a one-time capture of information as it exists at the time the information is transferred to the web. It does not necessarily include the ongoing updates or corrections to the source databases maintained by the City or other agencies and included for the City’s internal purposes. This data is intended for general references only and must not in any way be interpreted to be legal nor be used to establish legal lot sizes or dimensions. Legal information or descriptions must be obtained directly from the City of Campbell River through a visit to City Hall at 301 St. Ann’s Road in Campbell River.

If you have inquiries about this dataset please contact support@campbellriver.ca.