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83 results for “Supporting services”
EOSCpilot D3.7 Updates to Policy Supporting Services: supporting material for the Monitor
<p>This is to support use cases of the Monitor section of EOSCpilot D3.7 Updates to Policy Supporting Services.</p>
Values and attitudes associated with public support for biodiversity and ecosystem services as goals of ecological restoration
<p>Source data and metadata for "Public support for restoration: Does including ecosystem services as a goal engage a different set of values and attitudes than biodiversity protection alone?", accepted to PLOS One in December 2020. </p>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Dataset for: Developing research data management services and support for researchers: a mixed methods study
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115.</p> <p>Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a></p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset: </p> <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> <p>Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a></p>
Digital Scholarship services and supports - an overview from Irish Research and National Libraries - Data with Comments
<p>This dataset contains survey data with comments (cleaned, direct references to institutions removed) and broken down by survey question sections. Some of the data has been converted to counts where it was used to generate the charts.</p> <p>The data is the output of a 2018 CONUL (Ireland’s consortium of research and national libraries) survey focusing on Digital Scholarship services and supports from a Irish Research and National Library perspective.</p>
Urban tree pests can support biological control services in landscape shrubs
<p>Scale insects are common tree pests in urban ecosystems. Although severe scale infestations can worsen tree condition, trees can tolerate moderate scale densities. Scale insects are prey for many arthropod natural enemies that also feed on plant pests throughout urban landscapes. Because scale-infested trees support natural enemy communities, they may support biological control services on nearby plants and function analogously to banker plants in greenhouse production systems. In this study, we tested if sentinel insect prey were more likely to be removed on shrubs below scale-infested trees compared to scale-uninfested trees. We conducted several biological control experiments from 2019–2021 using fruit flies, aphids, and caterpillars in potted and planted holly shrubs below scale-infested and scale-uninfested oak trees. We found that caterpillars in potted shrubs and fruit flies in planted landscape shrubs were more likely to be removed underneath scale-infested trees compared to scale-uninfested trees. Caterpillars were also more likely to be removed from landscape <em>Ilex</em> <em>vomitoria</em> shrubs compared to <em>I</em>. <em>cornuta</em> shrubs. In all other experiments, we found no effect of scale infestation status or shrub species on prey removal. Our results suggest that scale-infested trees can support biological control services in shrubs below them but that this effect can vary depending on prey and shrub species. The natural enemy communities in urban trees and shrubs appear to be linked and tolerating tree pests can favor conservation biological control services in urban landscapes.</p>
Supporting data for "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV"
<p>Supporting data for the paper "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV".</p> <p><a href="https://doi.org/10.1016/j.cliser.2023.100395">https://doi.org/10.1016/j.cliser.2023.100395</a></p>
FIGURE 1 The Senckenberg model for a streamlined taxonomy workflow involving a in Why is there no service to support taxonomy?
FIGURE 1 The Senckenberg model for a streamlined taxonomy workflow involving a commercial service that covers transferable technical aspects of species descriptions. This figure was designed using icons from Flaticon.com.
Urban tree pests can support biological control services in landscape shrubs
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Effective use of personal health records to support emergency services
<p>Data and code supporting the paper titled "Effective use of personal health records to support emergency services" by Morales et al. (2020)</p> <p>Content:</p> <ol> <li>Synthetic Health Records dataset <ul> <li>ttl and nt files</li> </ul> </li> <li>Gold Standard <ul> <li>Curated Gold Standard based on the data set</li> </ul> </li> <li>Code used to analyse the dataset <ul> <li>Python</li> <li>Queries the data in Blazegraph, identifies valid data points and obtains people in need of help and the type of disability</li> </ul> </li> </ol>
Black American women's attitudes toward seeking mental health services and use of mobile technology to support the management of anxiety
<p><strong>Objective</strong>: This study aimed to understand Black American women's attitudes toward seeking mental health services and using mobile technology to receive support for managing anxiety.</p> <p><strong>Methods</strong>: A self-administered web-based questionnaire was launched in October 2019 and closed in January 2020. Women who identify as Black/African American were eligible to participate. The survey consisted of approximately 70 questions and covered topics such as attitudes toward seeking professional psychological help, acceptability of using a mobile phone to receive mental health care, and screening for anxiety.</p> <p><strong>Results</strong>: The findings of the study (N=395) showed that younger Black women were more likely to have greater severity of anxiety than their older counterparts. Respondents were most comfortable with the use of a voice call or video call to communicate with a professional to receive support to manage anxiety in comparison to text messaging or mobile app. Younger age, higher income, and greater scores for psychological openness and help-seeking propensity increased the odds of indicating agreement with using mobile technology to communicate with a professional. Black women in the South region of the U.S. had twice the odds of agreeing to the use of mobile apps than women in the Midwest and Northeast regions.</p> <p><strong>Discussion</strong>: Black American women, in general, have favorable views toward the use of mobile technology to receive support to manage anxiety.</p> <p><strong>Conclusion</strong>: Preferences and cultural appropriateness of resources should be assessed on an individual basis to increase the likelihood of adoption of and engagement with digital mental health interventions for management of anxiety. </p>
A Refined Supply-demand Framework to Quantify variability in Ecosystem Services Related to Surface Water in support of Sustainable Development Goals
<p>The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang.</p>
Quality of care and performance indicators of mental health supported accommodation services in England
<p class="MsoNormal"><span>This dataset includes data from Mental Health supporting accommodation services in England. It includes information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript published in Plos One: "Almeda, N., García-Alonso, C. R., Killaspy, H., Gutiérrez-Colosía, M. R., & Salvador-Carulla, L. (2022). The critical factor: The role of quality in the performance of supported accommodation services for complex mental illness in England. Plos One, 17(3), e0265319. https://doi.org/10.1371/journal.pone.0265319"</span></p> <p class="MsoNormal"><span>The research associated with the present data focused on developing an analytical process for assessing the performance of the Mental health (MH) supporting accommodation services from 14 different regions of England considering the effect of the quality-of-care indicators in the performance. For doing so every service was classified in Residential Care (move on and non-move on oriented), Supported Housing or Floating Outreach. Then, information about the quality-of-care was collected from each domain of the instrument QuIRC-SA. Finally, a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence was used.</span></p> <p class="MsoNormal"><span>The main results of the analyses pointed out that the incorporation of quality domains as variables (outputs) in DEA had a neutral-positive or positive global impact on the performance of MH-supported accommodation services.</span></p>
Support data for conference paper "Service-Oriented Model for Handling mMTC Subscribers' Traffic in a 5G Cluster"
<p>Support data for conference paper<br>V. Kovtun, and O. Kovtun, “Service-Oriented Model for Handling mMTC Subscribers’ Traffic in a 5G Cluster.” In Proc. 5th 5th International Workshop on Intelligent Information Technologies & Systems of Information Security, CEUR-WS, vol. 3675, 2024; pp. 236-246.<br>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
KR, Vehicle quality of service support, Tethering via vehicle using mmWave communication
<p>Use Case Category: <strong>Vehicle quality of service support</strong><br> User Story: <strong>Tethering via vehicle using mmWave communication</strong><br> Location: Korean (KR) trial site</p> <p>According to 3GPP TS 22.186 R16, Vehicle quality of service support “enables a V2X application to be timely notified of expected or estimated change of quality of service before actual change occurs and to enable the 3GPP System to modify the quality of service in line with V2X application’s quality of service needs. Based on the quality of service information, the V2X application can adapt behaviour to 3GPP System’s conditions. The benefits of this use case group are offerings of smoother user experience of service”.</p> <p>User Story: <strong>Tethering via vehicle using mmWave communication</strong></p> <p>Tethering via Vehicle use case enables in-vehicle UEs and pedestrian UEs to access the network with the help of a vehicle relay which is deployed at a vehicle. For in-vehicle UEs, through Tethering via Vehicle use case, it is possible to avoid high penetration loss occurring from the metallic vehicle surface, thereby achieving more reliable wireless connectivity as well as reduced UE power consumption. The in-vehicle UEs are also benefited from the minimized handover operations. Only the vehicle relays involve in the handover operations. For pedestrian UEs, tethering via Vehicle use case enables more reliable connectivity, increased throughput and reduced UE power consumption since it reduces the communication range of the pedestrian UEs.</p> <p>As a deployment scenario for Tethering via Vehicle use case, a vehicle relay can have the Internet connectivity to the network through a base station (BS) including macrocell BS, microcell BS, and BS-type road side unit (RSU). Another UE such as UE-type RSU can also provide the Internet connectivity to the network. Non-terrestrial links such as satellite BS, satellite relay, and high-altitude platform (HAP) can also be used to provide the Internet connectivity to the network.</p> <p>Tethering via Vehicle use case generally supports eMBB-type services such as web surfing, FTP, and video streaming. Hence, it intrinsically requires high data throughput up to several Gbps. In order to satisfy such very high throughput, large bandwidth is necessary which is quite difficult in lower frequency bands below 6 GHz. Therefore, mmWave frequency band should be employed to support such high throughput and to satisfy the Tethering via Vehicle use case.</p>
Training of maternity service providers on supportive and dignified maternity care: pre-post training assessment
<p>Poor psychosocial support and lack of respectful care for women during childbirth are commonplace in health facilities in low- and middle-income countries. While WHO recommends providing supportive care to pregnant women, there is a scarcity of material for building the capacity of maternity staff to provide systematic and inclusive psychosocial support to women in the intrapartum phase and prevent work stress and burnout in maternity teams. To address this need we adapted WHO's mhGAP and its training manual for maternity care staff to provide psychosocial support in labour room settings. Mental Health Gap Action Programme (mhGAP) is an evidence-based intervention which focuses on providing psychosocial support to people with mental, neurological and substance-use disorders (MNS).</p> <p>The objective of this paper is to describe the adaptation of mhGAP to develop psychosocial support-capacity-building materials for maternity staff to provide support to maternity patients, and also to staff, in the labour room.</p> <p>Adaptation was conducted within the Human-Centered Design framework in three phases: inspiration, ideation, and implementation feasibility. In the inspiration phase, a review of national-level maternity service-delivery documents and formative study via in-depth interviews of maternity staff were conducted. Ideation involved a multidisciplinary team to develop capacity-building materials by adapting mhGAP. This phase was iterative and included cycles of pretesting with staff, deliberations among the core team, and revision of materials. In the implementation feasibility phase, materials were tested via the training of 98 maternity staff and exploring system feasibility via post-training visits to health facilities.</p> <p>The inspiration phase identified gaps in policy directives and implementation. A formative study found that staff lacked the understanding and skills to assess patients' psychosocial needs and provide appropriate support. Allied with this, it became evident that maternity staff themselves needed psychosocial support. In ideation, the team developed capacity-building materials comprising two modules: one dedicated to conceptual understanding, and the other to implementing psychosocial support in collaboration with maternity staff. In the implementation feasibility phase, Staff found the materials relevant, acceptable and feasible for the labour room setting. Finally, users and experts reviewed and endorsed the relevance and usefulness of the materials. </p> <p>Our work in developing psychosocial-support training materials for maternity staff extends the utility of mhGAP by building the capacity of maternity staff to identify specific, hitherto-neglected needs of patients and staff, and by supporting, respectively, pregnant women to have positive birthing experiences, and maternity staff to manage workplace stress and avoid burnout. These materials can be used in a similar maternity setting after cultural adaptation.</p>
Advocating for Supports to Improve Service Transitions
ClinicalTrials.gov study NCT04173663. IPD Sharing: NO. Countries: 1. Publications: 6.
The Outcomes Among Persons With Schizophrenia Under the Peer-support Service in Vocational Rehabilitation Program
ClinicalTrials.gov study NCT04767204. IPD Sharing: NO. Countries: 1. Publications: 4.
FASTT Telehealth Behavioral Support Service for Caregivers of Children With Developmental Delay or Disability
ClinicalTrials.gov study NCT05909670. IPD Sharing: YES. Countries: 1. Publications: 13.
Acceptability of short message service reminders as the support tool for PrEP adherence among young women in Mukono district, Uganda
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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.