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2,672 results for “services”
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>
Maintaining habitat diversity at small scales benefits wild bees and pollination services in mountain apple orchards
<p>In 2021, we conducted our study in apple orchards in South Tyrol, an Alpine region in Italy, using pan-traps, direct observations of visitation frequency, and a pollinator exclusion experiment. We investigated the scale-dependent effects of landscape heterogeneity and other parameters on wild bee assemblages and the related pollination service they provide at five spatial scales (radius 100 – 2,000 m).</p>
Ecosystem Services Potential Dynamics of European Capital Metropolitan Areas
<p>These are the Supplementary Dataset of the article "Ecosystem Services Potential is Declining across European Capital Metropolitan Areas". These results rely on the Urban Atlas (UA) data. In our study, we first used the UA data 2018 to compare all ECMA by their current ESP. While the UA change products enabled us to reveal the ESP dynamics across three different periods. Consequently, we could differentiate between metropolitan areas that have faced high rates of ESP reduction and ECMA that have slightly improved. </p> <p>The data presented here include the following files:</p> <ol> <li>UAch_12_18_ALL.gpkg: Includes all altered LULC patches within the European capital metropolitan areas.</li> <li>FINAL_corr_Table.xlsx: Is the cumulative table which feeds the correlation analysis between ESP and ESPD results to socio-economic and other variables.</li> <li>WB_Urban_Population_Growth_Europe.xlsx: Delivers the population change metrics based on World Bank data.</li> <li>ESPD__Experts_Matrix_Revision: Including the revision procedure of the expert matrox evaluation criteria.</li> <li>ESPD_1806_ALL_City_level: Results of Ecosystem Services Potential Dynamics between 12 year period for 27 European metropolitan regions.</li> <li>ESPD_1812_ALL_City_level: Results of Ecosystem Services Potential Dynamics between 6 year period for 38 European metropolitan regions.</li> <li>ESPD_1812_ALL_Patch_level: Detailed table including all changed patches within 38 European metropolitan regions.</li> <li>ESPD_1812_UD_Pop_correlation: cumulative table incuding the urban expansion ratio and population growth, which feed our correlation analysis in our article.</li> </ol> <p> </p>
Content Hosting & Services Agreement between Google Arts & Culture and The University of Texas at Austin
<p>This document was obtained through a public records request filed under the <a href="https://www.texasattorneygeneral.gov/open-government/members-public/overview-public-information-act" target="_blank" rel="noopener">Texas Public Information Act</a>. It details the terms of a collaboration between the University of Texas at Austin and the Google Cultural Institute (now Google Arts & Culture). The document was requested as part of a data collection process for my PhD dissertation at the University of Texas at Austin, which, among other topics, examined the platform's use by cultural institutions. Contracts, alongside terms of service and content guidelines, are a critical aspect of a platform’s governance mechanisms.</p> <p>The document was released in response to request <strong>R005413-103123</strong> and made available on <strong>November 17, 2023</strong>, without redactions. For this version, I redacted email addresses and other personally identifiable information.</p> <p>You can find my dissertation, which references this document, on <a href="https://zenodo.org/records/13994044" target="_blank" rel="noopener">Zenodo</a> or through the <a href="https://doi.org/10.26153/tsw/55818" target="_blank" rel="noopener">University</a>.</p>
PM2.5 4 days forecast from December, 22 2020 retrieved from Copernicus Monitoring Service
<p>Dataset used in the Galaxy Pangeo tutorials on Xarray.</p> <p>Data is in netCDF format and is from <a href="https://ads.atmosphere.copernicus.eu/">Copernicus Air Monitoring Service</a> and more precisely PM2.5 (<a href="https://en.wikipedia.org/wiki/Particulates#Size,_shape_and_solubility_matter">Particle Matter < 2.5 μm</a>) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset.</p> <p> </p> <p><strong>This dataset is not meant to be useful for scientific studies.</strong></p>
Datasets for "Recreational hunting as an ecosystem service of restoration in the Bay-Delta watershed"
<p>These are the raw data from two surveys of recreational hunters in northern California conducted from 2019-2021 in association with the Delta Stewardship Council funded project "Recreational hunting as an ecosystem service of restoration in the Bay-Delta watershed" (Agreement #18211). Intercept data were collected in-person at hunt sites during deer, upland gamebird, and waterfowl hunt seasons in 2019-20. Followup survey data were collected online from a subset of the original interceptees as well as No. Cal hunters solicited for participation via social media, snowball sampling, and hunter chatrooms. Headers refer to questionnaires archived in Zenodo at https://doi.org/10.5281/zenodo.5809668.</p>
Copernicus Global Land Service: Global biome cluster layer for the 100m global land cover processing line
<p><strong>A map of 73 global biome clusters, geographic areas that were grouped to optimize the global 100m land cover processing.</strong></p> <p>In order to group Earth Observation data for faster processing or adaptation of algorithms to specific regions, the 100m global land cover (CGLS-LC100) algorithm uses a Global Biome Cluster layer. The term <em>biome cluster</em> hereby refers to a geographic area which has similar bio-geophysical parameters and, therefore, can be grouped for processing. In other words, the biome cluster layer can be seen as an ecological regionalisation which outlines areas of similar environmental conditions, ecological processes, and biotic communities (Coops et al., 2018). There are already several global regionalisation layers existing, e.g. Ecoregions 2017 global dataset (Dinerstein et al., 2017), Geiger-Koeppen global ecozones after Olofsson update (Olofsson et al., 2012), Global ecological zones for FAO forest reporting with update 2010 (FAO, 2012). But several tests in the CGLS-LC100 workflow have shown that the existing layers did not provide the required global and continental classification accuracy. These findings go along with Coops et al. (2018) who stated that "<em>Most regionalisations are made based on subjective criteria, and cannot be readily revised, leading to outstanding questions with respect to how to optimally develop and define them."</em></p> <p>Therefore, we decided to develop a customized ecological regionalisation layer which performs best with the given PROBA-V remote sensing data and the specifications of the CGLS-LC100 product. It groups spectral similar areas and helps to optimize the later classification/regression to regional patterns. Input into the layer creation were well-known existing datasets which were combined, re-grouped and advanced based on prior CGLS-LC100 classification results and local mapping knowledge of the workflow developer. To ensure that this layer is clearly separable from other existing regionalisations and not mistakenly interpreted as an eco-region layer, we decide to call it <em>biome clusters</em> <em>layer</em>.</p> <p>The following steps outline the global biome clusters layer generation:</p> <ul> <li>Spatial union of Ecoregions 2017 dataset (Dinerstein et al., 2017), Geiger-Koeppen dataset (Olofsson et al., 2012) and Global FAO eco-regions datasets (FAO, 2012);</li> <li>Regrouping and dissolving by using experience from first global CGLS-LC100 mapping results and subjective mapping experience of the developer;</li> <li>Refinement of the biome clusters in the High North latitudes via incorporation of a Global tree-line layer (Alaska Geobotany Center, 2003);</li> <li>Manual improvement of borders between biome clusters to reduce classification artefacts by using a DEM and mapping experience from previous projects and continental test runs;</li> <li>Usage of a global land/sea mask, the Sentinel-2 tiling grid and PROBA-V imaging extent to extend the borders of the biome clusters into the sea to make sure that also small islands on the coastline are correctly processed.</li> </ul> <p>When developing a regionalisation, the definition of the clusters and the boundaries that delineate them in time and space is the key challenge. Overall, the map distinguishes <strong>73 global biome clusters</strong>.</p>
The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches
<p>Forest restoration is being scaled-up globally to deliver critical ecosystem services and biodiversity benefits, yet we lack rigorous comparison of co-benefit delivery across different restoration approaches. In a global synthesis (Hua et al. 2022, Science; DOI: <a href="https://doi.org/10.1126/science.abl4649">10.1126/science.abl4649</a>), we use 25,950 matched data pairs from 264 studies in 53 countries to assess how delivery of climate, soil, water, and wood production services as well as biodiversity compares across a range of tree plantations and native forests. Carbon storage, water provisioning, and especially soil erosion control and biodiversity benefits are all delivered better by native forests, with compositionally simpler, younger plantations in drier regions performing particularly poorly. However, plantations exhibit an advantage in wood production. These results underscore important trade-offs among environmental and production goals that policymakers must navigate in meeting forest restoration commitments. The Excel file and the R code here are the datasets and analysis code that underlie the above study.</p>
Response to COVID-19: Clients' perspectives on the utilization of reproductive, maternal and child health care services in Nigeria
<p>This dataset was a part of a cross-sectional descriptive study in 320 rural communities in 32 Local Government Areas (LGAs) across the Federal Capital Territory (FCT), Abuja, and 9 out of the 36 Nigerian States in 2020. The dataset contains views of women who used primary health care centres in the selected LGAs for maternal and child health care, and family planning services before, during, and after the COVID-19 pandemic lockdown in Nigeria. The sample size was determined using the Yamane sample size formula. The estimated sample size per State was 384 (3,840 for nine States and FCT), with a 10% adjustment for non-response, the total sample size was 422 per State or location (4220 for the nine States and FCT). The data are stored in SPSS format. <br> The study was an initiative of UNFPA Nigeria. They coordinated it under the One UN Basket fund to respond to COVID-19 and implemented it under the supervision of three national NGOs: The Women’s Health and Action Research Centre (WHARC), Education as a Vaccine (EVA), and the Planned Parenthood Federation of Nigeria (PPFN). </p> <p> </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>
Data collection for article "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar's Forest Frontier Landscape"
<p>This dataset contains the nine ecosystem service models (in .neta format) underlying the publication "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar’s Forest Frontier Landscape". The ecosystem models were implemented using the commercial software Netica (version 6.05) for constructing and analysing Bayesian Networks.</p>
PID Registration Service Demo Application
<p>The demonstration will cover the four functions: verification, registration, identified user taskId and taskId status. </p> <ul> <li> <p>The API Reference Overview demonstrates four functions of the pid registration service for variables: the verification (validation) of the variables and the metadata to be registered. It returns a validation report.</p> </li> <li> <p>The variable registration prototype (not implemented yet) returns a taskId to follow up on the registration status. </p> </li> <li> <p>The Variable status tasks check the status of the tasks according to the identified user</p> </li> <li> <p>The variable/status/task/{taskId} that returns either the verification status or register process identified by the taskId.</p> </li> </ul> <p>The demonstration also cover the status pages. The first status page organizes the tasks by Username, amount of tasks, and their respective status (Finished, Pending/running or failed). The user task overview demonstrates the JSON file validation. Some tasks can register a bunch of variables, taking longer, so it is important to follow up on the running tasks from the user's perspective. The status page breakdowns the follow-up for each variable registration requested at a given taskId.</p>
DATASET: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods
<p>DATASET:</p> <p>Article: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods</p> <p>Journal of Water Process Engineering, Volume 37, October 2020, 101469</p> <p>https://doi.org/10.1016/j.jwpe.2020.101469</p>
Intention to utilize telehealth service in Bangladesh
<p>This data consists of information on participants' knowledge, perceived benefit, perceived concerns, and predispositions related to telehealth services in Bangladesh. In the data set, k1 to k5 indicted the items of knowledge. Similarly, pb1 to pb4 indicate perceived benefit, pc indicates the item of perceived concern, and pd1 to pd2 indicates the items of predisposition. This data set also includes information related to the demographic and perceived health status information. </p>
See no evil in the voice-to-voice customer service context
<p>A sample of more than 28,000 front-line employee (FLE) - customer interactions, extrapolating from foundational framing, we pit conventional service approaches against one another to propose a dual-process model, situating customer frustration/satisfaction as mediators of the indirect relationships between resolution/relational tactics and call duration – a key customer service efficiency outcome.</p>
Data collection for article "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar"
<p>This dataset contains supply, demand, and flow maps from nine ecosystem service models (as layer files in .tif format) underlying the publication "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar". </p>
Data collection for article "Local Perspectives on Ecosystem Service Trade-Offs in a Forest Frontier Landscape in Myanmar"
<p>This dataset contains 46 transcripts from semi-structured interviews and focus group discussions. An overview of the interviews is additionally provided, as well as information on the encoding. These data underlie the publication "Local Perspectives on Ecosystem Service Trade-Offs in a Forest Frontier Landscape in Myanmar".</p>
Use of health care services in community-dwelling older adults in two regions of Spain
<p>This dataset contains data on use of health care resources of community-dwelling older adults aged 70 or over, who were functionally independent. Data of health resources use included contacts along two consecutive years with: the general practitioner, primary care nurse, the specialists, visits to emergency rooms, and hospital admissions and length of stay. The data included also information about sex, region, polipharmacy, age-adjusted Charlson Comorbidity Index and funcionality, measured by Timed Up and Go test. The data collection was performed in two Spanish regions. Baseline assessment was done between 2015 and 2016, and patients were followed for 2 years. There were in total 1488 registries considering both years.</p>
The theory of planned behavior and the prediction of pre-service biology teachers' intention to teach evolution
<p>We developed the project to identify and analyze variables that promote or hinder prospective biology teachers’ intentions to teach evolution. We adopted the model of the theory of planned behavior (TPB). We extended it to include additional variables described by teacher education research as key determinants of behavioral intention to teach evolution. We initially hypothesized that attitudes toward teaching evolution, subjective norms, perceived behavioral control, personal religious beliefs, perceived usefulness, and knowledge about evolution would determine a person’s behavioral intentions. To test the hypotheses, we developed an online questionnaire and conducted a quantitative cross-sectional survey in the field of teacher education. The data included information on <em>N</em> = 309 participants. Because we initially analyzed the data using a two-stage structural equation model (SEM), we uploaded two data files that were created in subprocesses of our original analyses (for more information, see the original publication). The dataset “data3” contains 77 variables and has missing values. Since we wanted to use complete data for the SEM, we trimmed the data set “data3” to include only the 67 variables necessary for the SEM, then applied an expectation-maximum (EM) algorithm with multiple imputations, and obtained the data set “data4”. </p>
Recording of ETAPAS 4th Dissemination Event: Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations
<p>On the 12th of October 2022 the 4th ETAPAS dissemination event "Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations" took place online and on site at the Centre for Research & Technology Hellas (CERTH).</p> <p>If you missed the workshop you can find here the recording of the event, where we presented the first outputs generated by the ETAPAS Project to Greek Public Administration in order display concrete results on how AI can be ethically embedded in their activities.</p> <p>Among the topics we discussed:</p> <ul> <li>the ETAPAS Project and key tools developed for the good governance of AI;</li> <li>the chatbot Kari and the challenges using AI-based solutions presents;</li> <li>the development of a misinformation detection platform by CERTH;</li> <li><a href="https://www.pop-ai.eu/">popAI</a> and <a href="https://token-project.eu/the-project/">TOKEN</a> projects;</li> <li>strategies for a governance framework for artificial intelligence in public administration.</li> </ul> <p>Check this recording to see all the interesting presentations and discussions that emerged during the project.</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.