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2,672 results for “Services”
Dataset: Kelly Services, Inc. (KELYB) 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: Kelly Services, Inc. (KELYA) 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: J.B. Hunt Transport Services, Inc. (JBHT) 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: Information Services Group, Inc. (III) 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: H&E Equipment Services, Inc. (HEES) 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: Healthcare Services Group, Inc. (HCSG) 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.
Fig.1 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples
Fig.1. Ecosystem service class: biomass energy products. Indicator: potential energy wood supply within felling limits.
Fig. 2 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples
Fig. 2. Ecosystem service class: global climate regulation by reduction of GHG concentration. Indicator: Estimated carbon stock in live above-ground tree biomass.
Data from: Agricultural specialisation increases the vulnerability of pollination services for smallholder farmers
<p>Smallholder farms make up 84% of all farms worldwide and feed two billion people. These farms are heavily reliant on ecosystem services and vulnerable to environmental change, yet under-represented in the ecological literature. The high diversity of crops in these systems makes it challenging to identify and manage the best providers of an ecosystem service, such as the best pollinators to meet the needs of multiple crops. It is also unclear whether ecosystem service requirements change as smallholders transition towards more specialised commercial farming – an increasing trend worldwide. Here, we present a new metric for predicting the species providing ecosystem services in diverse multi-crop farming systems. Working in 10 smallholder villages in rural Nepal, we use this metric to test whether key pollinators, and the management actions that support them, differ based on a farmers' agricultural priority (producing nutritious food to feed the family versus generating income from cash crops). We also test whether the resilience of pollination services changes as farmers specialise on cash crops. We show that a farmers' agricultural priority can determine the community of pollinators they rely upon. Wild insects including bumblebees, solitary bees, and flies provided the majority of the pollination service underpinning nutrient production, whilst income generation was much more dependent on a single species - the domesticated honeybee <em>Apis cerana</em>. The significantly lower diversity of pollinators supporting income generation leaves cash crop farmers more vulnerable to pollinator declines. Regardless of a farmers' agricultural priority, the same collection of wild plant species (mostly herbaceous weeds and shrubs) were important for supporting crop pollinators with floral resources. Promoting these wild plants is likely to enhance pollination services for all farmers in the region.</p> <p><em>Synthesis and applications:</em> We highlight the increased vulnerability of pollination services when smallholders transition to specialised cash crop farming and emphasise the role of crop, pollinator, and wild plant diversity in mitigating this risk. The method we present could be readily applied to other smallholder settings across the world to help characterise and manage the ecosystem services underpinning the livelihoods and nutritional health of smallholder families.</p>
Analysing the intra and interregional components of spatial accessibility gravity model to capture the level of equity in the distribution of hospital services: does they influence patient mobility?
<p>aggregated_data_age55+.csv and distance_matrix_age55+.csv have been included in the second version of the dataset as the reference population is limited to resident with 55 years old or more.</p>
Dataset for testing the OpertusMundi Discovery Service
<p>Vector and tabular data in CSV format used to test the discovery service. </p>
A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction
<p>This file contains human-robot interaction data acquired during an experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
OLS, OntoPortal, Skosmos - API mapping specifications for API Gateway and Terminology Service Suite
<p>A set of initial API endpoints have been defined for the central API Gateway together with the initial set of service wrappers for each of the covered technologies (OLS, OntoPortal and Skosmos). This involves detailing the call URL structures, HTTP methods, service parameters, authentication methods, and expected response formats for each type of endpoint. <span>This document outlines the various backend APIs for the API Gateway and provides guidance on how to harmonise and map them. The API Gateway is used to provide information in a format that can be utilised by the widgets within the Terminology Service Suite (TSS). Each table in this document is related to one widget of the TSS.</span></p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 3. Sharing and merging effect on successful allocation
<p>Fig (3) shows the effect of merging and sharing resources by auctioneer in term of success<br> rate of allocation. As shown in it, these factors improve successful allocation rate especially in<br> higher system load.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 2. bid value for time factor
<p>Each consumer is looking for utilizing its requested service with minimum price before its<br> deadline. To utilize a service all required resources should be allocated before deadline and<br> otherwise service failed to utilize and consumer must pay penalty to providers for all other<br> resources which is allocated to it. So Consumer should adjust its bid price rapidly to the acceptable<br> price of the market. Since consumers are generally sensitive to deadline in acquiring requested<br> service, it is intuitive to consider deadline time when formulating the bid price. Consumer agent<br> time dependent bid price formula is determined in.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 5. Comparison on successful allocation
<p>Figure 5 illustrates comparison of successful allocation rate between ICDA and other<br> methods. In the proposed method, intelligent allocation and also time consideration enable<br> consumers to acquire more resources before the deadline and as a result, the number of successful<br> allocation is higher than other methods.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 4. Sharing and tradeoff factor effect on resource utilization
<p>In Figure 4 we consider tradeoff and sharing factors in providers. The result illustrates that<br> by using these factors providers improve resource utilization. Higher resource utilization motivates<br> more providers to participate in the cloud and also enables the cloud market to handle more<br> consumers which influences market efficiency.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 1. Resource allocation schema in proposed method
<p>We assume that the resources allocation satisfies the following conditions:<br> • The quantity of a resource can be measured in arbitrary units (e.g. 60 units of resource<br> A).<br> • A resource can be divided into an arbitrary fraction (e.g. a resource of 60 units is divided<br> into 20 units for consumer 1 and 40 units for consumer 2).<br> • A resource request of a service can be divided into sub-requests and acquired from<br> multiple providers (e.g. a resource request of 40 units utilized as 10 units from provider<br> 1 and 30 units from provider 2).<br> Figure 1 shows a cloud computing environment with the proposed mechanism.</p>
BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 2. Frequency of principal strong indices related to 102 'strong articles'
<p>We ordered the obtained data for the identified strengths and weaknesses. This was done by giving a point for each presence of an index, or not giving it for its absence. Thus, we quantified a total of 63 strengths and 91 weaknesses. In addition to finding a great diversity of views captured in the selected texts, the primary data analysis allowed us to set the parameters with the highest frequency. So, the most important for strengths is flexibility representing 29.7% of the total of studied bibliography and an occurrence frequency of 13.6% from the total of identified strengths (Figure 2). </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.