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229 results for “Businesses”
Business Models in Energy Communities: an analysis through legal lenses
<p>This research explores energy communities (EC) and their business models’ attributes. We develop a conceptual framework, which combines and extends the social, economic, environmental, and technological dimensions of value generation to include the legal dimension. The latter has been considered only implicitly in previous studies on this sector. Applying this framework to forty business cases of energy communities allows to identify six business model (BM) archetypes representative of ECs. This study can encourage and support new ventures in this sector to model their strategy and comply with the requirements.</p>
Engaging the Business and Tourism Industry in Visualizing Sea Level Rise Impacts to Transportation Infrastructure in Waikiki, Hawaii
<p>Transportation planners in coastal communities plan for future hazards and risks of sea-level rise (SLR), and often, they communicate risk in public meetings via PowerPoint presentations with charts as well as two-dimensional (2D) maps that visualize information using Geographic Information Systems (GIS) technologies. The project investigates the use of immersive technology to communicate SLR risk, including the development of an immersive three-dimensional (3D) model of the Waikiki neighborhood of Honolulu, Hawaii. According to the project’s original methodology, participants would have experienced the model using virtual reality (VR). However, due to the COVID-19 pandemic, the team pivoted to creating and implementing an internet-based survey instrument with embedded 2D charts and video of the animated 3D model. The flooding projections were derived from National Oceanic and Atmospheric Administration (NOAA) data. NOAA supplies the SLR Viewer, a screening-level tool that uses the best-available national projections to map areas vulnerable to current and future flood risks.</p>
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The Business of Satirical Prints in Late-Georgian England: network graphs
<p>These figures support research published in Chapter 8 of James Baker, <em>The Business of Satirical Prints in Late-Georgian England</em> (London: Palgrave, 2017). It contains figures made by James Baker in February 2013. These are figures 8.13-8.20 of <em>The Business of Satirical Prints</em>. Figures generated in Gephi using data deposited at http://dx.doi.org/10.5281/zenodo.49548. All errors are the fault of the author.</p>
Test Data Generation from Business Rules
<p><strong>Overview of Data</strong></p> <p>The site includes data only for the two subjects: Ceu-pacific and JBilling. For both the subjects, the “<em>.model” shows the model created from the business rules obtained from respective websites, and “</em>_HighLevelTests.csv” shows the tests generated. Among csv files, we show tests generated by both BUSTER and Exhaust as well.</p> <p><strong>Paper Abstract</strong></p> <p>Test cases that drive an application under test via its graphical user interface (GUI) consist of sequences of steps that perform actions on, or verify the state of, the application user interface. Such tests can be hard to maintain, especially if they are not properly modularized—that is, common steps occur in many test cases, which can make test maintenance cumbersome and expensive. Performing modularization manually can take up considerable human effort. To address this, we present an automated approach for modularizing GUI test cases. Our approach consists of multiple phases. In the first phase, it analyzes individual test cases to partition test steps into candidate subroutines, based on how user-interface elements are accessed in the steps. This phase can analyze the test cases only or also leverage execution traces of the tests, which involves a cost-accuracy tradeoff. In the second phase, the technique compares candidate subroutines across test cases, and refines them to compute the final set of subroutines. In the last phase, it creates callable subroutines, with parameterized data and control flow, and refactors the original tests to call the subroutines with context-specific data and control parameters. Our empirical results, collected using open-source applications, illustrate the effectiveness of the approach.</p>
Research data - taxonomy for revenue models of platform business models (C2E)
<p>Research data to develop a taxonomy of revenue models for platform business models following a conceptual-to-empirical approach.</p>
BIO4AFRICA_Report on Inclusive and sustainable bio-based business models for rural Africa_D5.2_30/09/2022_v1.0
<p>This dataset constitutes Deliverable D5.2 Report on the 'Inclusive and sustainable bio-based business models for rural Africa', which has been the result of Task 5.2 of the BIO4AFRICA H2020 project. The deliverable aims at co-designing the inclusive and sustainable business models for the bio-based technologies transferred and adapted to each pilot case of the project bio-based products in Uganda, Ghana, Cote D'Ivoire, and Senegal.</p>
Dataset for article "Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"
<p>Dataset for article entitled "<strong>Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"</strong></p>
List of business and system roles of Northern Cluster in OneNet project
<p>The list of roles includes in CSV format the names and definitions of business roles and system roles referred to in the use cases of OneNet Northern cluster. The Harmonised Electricity Market Role Model (HEMRM) defines many of the roles used. However, if not existing in HEMRM, use cases provide their own definitions, sometimes relying on the ones proposed by BRIDGE Initiative.</p> <p>The context of usage of the roles in business use cases and system use cases can be found here: <a href="https://onenet-project.eu/wp-content/uploads/2023/10/D7.2_OneNet_v1.0.pdf">https://onenet-project.eu/wp-content/uploads/2023/10/D7.2_OneNet_v1.0.pdf</a>,<a href="https://onenet-project.eu/wp-content/uploads/2023/05/OneNet_D7.3_v1.0-1.pdf"> https://onenet-project.eu/wp-content/uploads/2023/05/OneNet_D7.3_v1.0-1.pdf</a>,<a href="https://onenet-project.eu/wp-content/uploads/2022/12/OneNet_D7.4_v.1.0.pdf">https://onenet-project.eu/wp-content/uploads/2022/12/OneNet_D7.4_v.1.0.pdf</a></p> <p> The explanation of HEMRM can be found here: <a href="https://mwgstorage1.blob.core.windows.net/public/Ebix/Harmonised_Role_Model_2023-01.pdf">https://mwgstorage1.blob.core.windows.net/public/Ebix/Harmonised_Role_Model_2023-01.pdf</a></p> <p> The explanation of additions to HEMRM as proposed by BRIDGE Initiative: <a href="https://energy.ec.europa.eu/system/files/2021-06/bridge_wg_regulation_eu_bridge_hemrm_report_2020-2021_0.pdf">https://energy.ec.europa.eu/system/files/2021-06/bridge_wg_regulation_eu_bridge_hemrm_report_2020-2021_0.pdf</a></p>
Data-Driven Identification and Analysis of Waiting Times in Business Processes: A Systematic Literature Review
<p>Supplementary Material for Systematic Literature Review titled "Data-Driven Identification and Analysis of Waiting Times in<br> Business Processes: A Systematic Literature Review"</p>
Open dataset of 20 interviews with senior service designers and managers for the Empathy Business research project
<p>The aim of the Empathy Business research project (2023-2024) led by the University of Lapland focused on how to digitalize services and business prototyping through creativity. The research named challenges as well as design methods for developing digital tools for meeting the future needs of service design and business development.<br><br>In total, 20 interviews among senior service designers and managers located in Europe, Latin America and Asia were conducted during spring 2023. The interviews provide perspectives related to the future of service design as a practise, the skills required and further issues of relevance for professionals in the field. Based on affinity diagramming eight main clusters were named: Sustainability, Business compatibility, New tools, Designer’s skills, Art-based methods, People in the centre, Online workshops, and Physical workshops.<br><br>This data set includes an anonymized list of the interviewees, affinity diagram post-it notes of the interviews, short descriptions of the main clusters and an internet link to the online affinity diagram on Miro board.<br><br>The materials provided initial insights for developing Proof-of-Concepts for digitized interfaces, such as suitable plugins, 3D-based photorealistic solutions, or an application with the potential to be used in service design and service prototyping contexts as well as in other development processes within and across organizations.</p>
Jewish Business Partnerships (JeBuPa)
<p><span><span>Räume bzw. Orte mit ausgeprägter jüdischer Siedlungskonzentration, die sich im frühneuzeitlichen Reich oft in Gebieten erkennen lassen, die von kleinräumigen und ungeschlossenen territorialen Verhältnissen geprägt waren. Das DFG-Projekt Fraktalität und die Dynamiken jüdischer Lebensformen im Süden des Alten Reiches geht von der Annahme aus, dass diese spezifischen Strukturen für die Existenz von Juden verantwortlich waren und danach befragt, wie die jüdischen Akteure diese zu nutzen wussten. Im Projekt wurde dieses Konzept mit den Methoden der Netzwerkanalyse verbunden, woraus sich Möglichkeiten ergaben, die Kategorie des Raums sozialgeschichtlich für die jüdische Geschichte fruchtbar zu machen sowie die territorialpolitische Komplexität jüdischer Siedlungsräume in ihrer Wirkung und Bedeutung erstmals umfassend zu beschreiben und zu erklären. Derart komplexe politische Strukturen kommen in besonderer Weise Akteuren entgegen, die es gewohnt waren, in multipolaren Netzwerken zu agieren, wie es für weite Teile der jüdischen Bevölkerung der Vormoderne nachweislich ist. Im Projekt Religiöse Differenz und wirtschaftliche Kooperation. Christlich-jüdische Geschäftsbeziehungen wurden nachgewiesen, dass es eine deutlich engere Zusammenarbeit dieser beiden Religionsgruppen im wirtschaftlichen Bereich als bisher angenommen hat. </span></span><br><span><span>Um diese Netzwerke offenzulegen, wurden im Rahmen der an der KU Eichstätt-Ingolstadt laufenden DFG-Projekte zwei relationale Datenbanken erstellt, die ab jetzt online zur Verfügung stehen. Nähere Informationen zur inhaltlichen Konzeption der Datenbanken finden Sie im Dokument „Jewish Prosopographic Database (Jewish PID) und Jewish Business Partnerships (JeBuPa)“ ( </span></span><a href="https://doi.org/10.5281/zenodo.10969964"><span><span>10.5281/zenodo.10969964)</span></span></a></p>
Airline Satisfaction Survey Data: Data Science for Business with Python
<p>Companion dataset for the textbook entitled "Data Science for Business with Python"</p>
Introduction to the Future Blood Testing Network Plus - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the first talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ </p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/YuBsU3NDdB0</p>
Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).
<p>This video is the first talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/I1xsig9C8w0</p>
Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the first talk from our Landscape Report Funding Call Town Meeting that took place on the 03/02/2022.</p> <p>Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/03-02-2021-future-blood-testing-network-landscape-report-funding-town-meeting/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/I1xsig9C8w0</p>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
Dataset: First Business Financial Services, Inc. (FBIZ) 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: Business First Bancshares, Inc. (BFST) 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: Barrett Business Services, Inc. (BBSI) 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.
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.