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1,618 results for “City”
Dataset: Citi Trends, Inc. (CTRN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Capital City Bank Group, Inc. (CCBG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Behavioural changes in the city: the common black garden ant defends aphids more aggressively in urban environments
<p>Data and R code to analyse changes in aphid and ant populations and behaviour along a gradient of urbanisation in Berlin, Germany. This release is associated to a publication in preparation and includes the updated R code used for publication:</p> <p>Gaber, H., Ruland, F, Jeschke, J. & Bernard-Verdier, M. (2024) Behavioural changes in the city: the common black garden ant defends aphids more aggressively in urban environments. <em>Ecology & Evolution</em> (publication details will soon be added)</p>
Mobile Meteorological Data from Dresden city center (June-August, 2022)
<p>This file contains meteorological data collected with built-for-purpose low-cost meteorological device for mobile thermal comfort mapping. Data was collected in the city center of Dresden, Germany, around a 3.1km route for the 19th of June, 23rd of June, 19th of July, 25th of July, 16th of August, and 17th of August, 2022 in the morning (06:00), midday (12:00), afternoon (15:30), and evening (20:00) of each of the 6 monitoring days. Each data file also contains thermal indices calculated using the software RayMan Pro.</p> <p>Folders are labeled by date e.g. 19th of June is written as 1906. Subfolders are labeled to correspond with the starting time of each measurement where A corresponds to 06:00, B is 12:00, C is 15:30, and D is 20:00. For each of these time periods (A, B, C, D) 2 laps of the 3.1km route were made. </p>
Fig. 3 in An Evaluation Of Stone Marten (Martes Foina) Records In The City Of Budapest, Hungary
Fig. 3. Density of stone marten records (n = 214) in Budapest districts (n = 23) between 1996 and 2008. The black coloured columns represent the districts with at least one or more registrations per km2
Fig. 4 in An Evaluation Of Stone Marten (Martes Foina) Records In The City Of Budapest, Hungary
Fig. 4. Average greenness (%) of those 25 ha patches (n = 225) that contained at least one marten record. Green (> 50%) districts are highlighted in black
Fig. 2 in An Evaluation Of Stone Marten (Martes Foina) Records In The City Of Budapest, Hungary
Fig. 2. The number of yearly topographical records of stone martens in Budapest (n = 303) during the 13 years of monitoring
Fig. 1 in An Evaluation Of Stone Marten (Martes Foina) Records In The City Of Budapest, Hungary
Fig. 1. Numbering and location of the 23 districts in Budapest. The grey patches represent the DESERT (≤ 50% green) and the striped patches represent the GREEN (≥ 50% green) type districts according to the records on the stone marten. There were no topographical data available for districts
Figure 5 in Phenolic compound and fatty acid properties of some microalgae species isolated from Erbil City
Figure 5. Scatterplot matrix shows the correlation between palmitic acid, stearic acid, oleic acid and linoleic acid in a- Spirogyra sp. b- Spirulina sp. c- Chara sp. d- Chlorella sp.
Figure 3 in Phenolic compound and fatty acid properties of some microalgae species isolated from Erbil City
Figure 3. The distribution of DPPH and total phenol shows the same across categories of Treatment, Independent-Samples KruskalWallis Test and rejects the hypothesis on the base of Null Hypothesis with highly significant levels. A- Spirogyra sp., b-Spirulina sp. c- Chlorell sp. a d- Chara sp.
Figure 1 in Phenolic compound and fatty acid properties of some microalgae species isolated from Erbil City
Figure 1. Morphology of Algal genera isolated from Erbil City (a-Spirogyra, b-Spirulina, C-Chlorella d- Chara).
Interactive map of Heat Stress Compensability Classification (HSCC) application in 96 United States cities.
<p>This repository includes the interactive map in format .html of the very first application of the <strong>Heat Stress Compensability Classification (HSCC) in 96 cities in the United States </strong>showing the proportion of days with compensable and uncompensable heat stress from the top 10th percentile of hottest days from 2005-2020 in each place.</p> <p>This map offers the detailed results of the very first application of the classification system as in the journal article: <strong>The Development of an Adaptive Heat Stress Compensability Classification Applied to the United States</strong>, published in the 4th SNP special issue in the International Journal of Biometeorology. The results of this visualization were obtained from open-source data and coding packages such as Folium, and the model results were obtained by applying the Python Human Heat Balance (PyHHB) on weather dataset freely available.</p> <p>The interactive map offers a detailed visualization of the results from each of the cities, allowing you to see 3 tabs when the icon of the pie chart from each location is clicked.</p> <p><strong>Tab statistics:</strong> Detail per city of Figure 4b of related paper.</p> <p><strong>Tab Histogram 2D: </strong>Details per city of Fig 6 of related paper</p> <p><strong>Tab How to read: </strong>Figure 2 in related paper.</p> <p>Please for questions related to this dataset/code contact Gisel Guzman-Echavarria (gguzma20@asu.edu).</p> <p>Guzman-Echavarria, G., & Vanos, J. (2023). PyHHB: Physiological-based estimations of human survivability and liveability to heat in a changing climate (Nature Communications (1.0.0)). Zenodo. https://doi.org/10.5281/zenodo.10020137</p> <p> </p> <p> </p>
Fig. 1 in Ingestion of plastics in the European bass (DIcentrarchus labrax Linnaeus, 1758): first known observation in the city of Plovdiv, Bulgaria
Fig. 1. Different plastic products found in the gut of European bass, purchased from the city of Plovdiv (photograph: Velichka Pachedzhieva).
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan) in Free-living protozoa of freshwater and soils of the North-East Azerbaijan
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan)
Figure 3. Screenshot of the MQTT broker, publisher, and two subscribers.-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p>As it was mentioned above, IoT needs the appropriate lightweight protocols to transmit the<br> info because web-protocols (e.g. TCP) generate several times more traffic usually for IoT (e.g.<br> remote connection to the Arduino weather station). MQTT (Message Queuing Telemetry Transport)<br> and CoAP (Constrained Application Protocol) IoT protocols are mainly in use nowadays<br> (http://postscapes.com/internet-of-things-protocols). In this activity,Arduino Ethernet Shield and C#<br> console app are connected by MQTT Mosquitto open source software (http://mosquitto.org).Similar<br> work presented against https://iotguys.wordpress.com/2014/11/13/arduino-with-mqtt/.The activity<br> consists of the following steps:<br> 1. Download and installation of Mosquitto software gainst http://mosquitto.org/download/.<br> 2. Download and installation of the latest Arduino software against<br> http://arduino.cc/en/main/software.<br> 3. Development of the MQTT subscriber based on C programming language in Arduino<br> IDE.<br> 3. Development of the MQTT subscriber based on C# console app (laptop HP ProBook 650<br> G1 and Windows 10 are used) in Visual Studio.<br> Screen shot of the software is shown in Fig. 3.</p>
Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-
<p>A non-anticipative analog method consists of four main steps:<br> 1. Generation of the prediction rules.<br> 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at<br> the same frame, are excluded.<br> 3. Generation of possible extremes.<br> 4. Analysis of the generated possible extremes. The extremes with time slots, which do not<br> correspond to the time slots of the appropriate rules, are excluded.<br> The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations<br> (places are selected randomly) are presented in Table 2.</p>
Figure 1. Azure management portal and VM with two Delphi desktop apps-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p><br> Nowadays, only D-Wave Systems Company produces commercially the 2nd generation<br> adiabatic quantum computer with up to 512 flux qubits (project code name ″Vesuvius″). They are<br> microscopic loops of niobium metal that are capable of quantum behavior at low temperatures.<br> Hence, electrical currents in the loops can flow in clockwise (+1) or counterclockwise (-1)<br> direction, or both, when in quantum superposition. Qubits are connected to neighbors according to<br> the topology of quantum processor. The hardware is controlled by a framework of Josephson<br> junctions that allow individual qubit values to be stored and read, and to influence the states of<br> neighboring qubits.</p>
Dataset for: IoT deployment for city scale air quality monitoring with Low-Power Wide Area Networks
<p>Air Quality (AQ) is a very topical issue for many cities and has a direct impact on the health of its citizens. We propose to investigate the air quality of a large UK city using low-cost commodity Particulate Matter (PM) sensors, and compare them with government operated air quality stations. In this pilot deployment we design and build six AQ IoT devices, each with four different low-cost PM sensors and deploy them at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide-Area Network network coverage. We conclude that some low-cost PM sensors are viable for monitoring AQ and demonstrate that our device design can be used via LoRaWAN to facilitate more granular city coverage without limitations of network access. Based on these findings we intend to deploy a larger LoRaWAN enabled Air Quality sensor network deployment across the city.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 2. Traditional growth model vs. adapted growth model Source: Adapted after Purdy & Daugherty, 2016
<p>The use of AI is not limited to smart buildings or transportation. It covers a wide range of application from medical diagnosis, to robot control and virtual assistance scientific tools. Nowadays, AI can be encountered in many services such as: cars speech recognitions, industrial robots, intelligent vacuum cleaners or fridges and so further. It can also be used in smart homes which permits by using hundreds or even thousands of sensors to provide services according to our preferences such as: ambient assisted living, energy saving etc. According to Skouby et al. (2014), AI also can be utilized in smart homes by adding personalized features in form of context awareness which allows AI to move beyond automation level. These authors designed a four-layer pyramid which encases the ICTs based infrastructure for future smart cites (Figure 3).</p>
Artificial Intelligence and the Future of Smart Cities-Table 1. Smart city complex system factors
<p>Figure 2 underlines the importance of traditional production factors such as capital and labour in achieving and driving growth which arises when either stock of capital or labour increase or they are more effectively used. Total factor productivity (TFP) represents the growth enhanced by the use of technology and innovation. Besides these traditional factors, Purdy and Daugherty (2016) consider that AI can be seen as a new production factor, a capital-labour hybrid that will lead to significant growth opportunities. This is due to the advancement made in AI, which allows nowadays to replicate some labour activities at a greater and faster scale than humans (e.g. virtual text assistance, self-learning machines) (Purdy and Daugherty, 2016).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.