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34 results for “service level”
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC1 SET Surface Water level data from in Biscayne National Park, Florida, USA (2016-2025)
Surface water level data (m) was collected in Biscayne National Park (BISC) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2016 to 2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 1, known as BISC-SET-1 or BISC1. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC2 SET Surface Water level data from in Biscayne National Park, Florida, USA (2017-2025)
Water level data (m) was collected in Biscayne National Park (BISC) by the National Park Service - South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017-2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 2, known as BISC-SET-2 or BISC2. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Mary's Point SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Mary's Point (MARY) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Water Creek SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Water Creek (WACR) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
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>
Maturity levels of the customized CMMI-SVC for testing services and their process areas
<p>This resource is associated with the following paper:</p> <p>Assessing the maturity of software testing services using CMMI-SVC: an industrial case study</p>
Supplementary material 2 from: Vačkář D, Grammatikopoulou I, Daněk J, Lorencová E (2018) Methodological aspects of ecosystem service valuation at the national level. One Ecosystem 3: e25508. https://doi.org/10.3897/oneeco.3.e25508
Results of systematic review of ecosystem service valuation studies in Europe (2000 – 2017) for benefit transfer at the national level .
Supplementary material 1 from: Vačkář D, Grammatikopoulou I, Daněk J, Lorencová E (2018) Methodological aspects of ecosystem service valuation at the national level. One Ecosystem 3: e25508. https://doi.org/10.3897/oneeco.3.e25508
Classification of ecosystems based on CORINE Land Cover and Consolidated Layer of Ecosystems for the Czech Republic.
Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability
<p>This dataset is used for the working paper "Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability," authored by Fahimnia, Tan, and Tahirov. The data was collected during a laboratory experiment designed based on data from a real case in the fast-moving consumer goods (FMCG) industry. Each subject was assigned to one of the following treatment groups:</p> <ul> <li>T1 - forecasts were made for a nonperishable product (shelf life of 9 months), with no service level information.</li> <li>T2 - forecasts were made for a perishable product (shelf life 1 day), with no service level information.</li> <li>T3 - forecasts were made for a nonperishable product, with a high service level information.</li> <li>T4 - the forecasts were still for a nonperishable product, with a lower service level information.</li> <li>T5 - forecasts were made for a perishable product, with high service level information.</li> <li>T6 - forecasts were made for a perishable product, with low service level information.</li> </ul> <p>A total of 368 subjects prepared four forecasts each. For each forecast, a subject was provided with 30 weeks of sales data, including both normal and promotional weeks. The promotional weeks were highlighted as "Promo." The subjects were asked to provide their forecasts for week 31, basing their forecasts solely on historical data and potential sales promotions. Mean absolute percentage error (MAPE) was used to assess the accuracy of the forecasts. Percentage forecast bias was used to measure the deviation of adjusted forecasts from the normative benchmark forecast.</p> <p>The dataset includes four Excel files, two code script files, and a README file:<br><strong>1. Excel files 1 and 2:</strong><br><strong> "database_analysis.xlsx"</strong>: Contains average adjusted forecasts for each subject during both promotional and non-promotional periods, along with demographic information, calculated MAPE, forecast bias, service level, and product perishability. This file is used as input data in the "data_cleaning.R" script.<br><strong>"database_plot.xlsx"</strong>: This Excel file contains a compact and cleaned version of the data from the first file, excluding outliers, and was used to create visuals such as boxplots.<br><strong>2. Excel Files 3 and 4:</strong><br><strong>"Pool_1_Perishable.xlsx" </strong>and "<strong>Pool_2_Non_Perishable.xlsx"</strong>: Contain real datasets for perishable and non-perishable products used in the experiment.<br><strong>3. Code Script File:</strong><br><strong>"data_cleaning.R"</strong>: This script performs data pre-processing by cleaning and transforming the dataset.<br><strong>"analysis.R"</strong>: This script loads the cleaned data and performs statistical analysis, including ANOVA and hypothesis testing. </p>
Databases for "A multiscale approach to identify key factors determining rural WaSH service level and sustainability" (ShinyApp). Application to SIASAR conceptual model v1, outputs obtained from Nicaragua, Honduras, Panama and Dominican Republic SIASAR databases (last accessed: December 21, 2016)".
<p>Attached databases contain the results obtained by applying the SIASAR composite indicators v1 (available at http://upcommons.upc.edu/handle/2117/77587) to data presented in http://doi.org/10.5281/zenodo.571351</p> <p>These files can be directly exploited by using the ShinyApp “A multiscale approach to identify key factors determining rural WaSH service level and sustainability”.</p> <p>Each *.csv file contains one country's database. </p>
Study of the medical service efficiency of county-level public general hospitals based on medical quality constraints: A cross-sectional study
<p><strong>Objectives:</strong> Since the new medical reform in 2009, county-level hospitals in China have achieved rapid development, but health resource waste and shortage issues still exist.</p> <p><strong>Design</strong>: We applied the Meta-frontier and SBM-undesirable(Slacks-based Measurement-undesirable) DEA(Data Envelopment Analysis) model to measure the medical service efficiency with or without medical quality constraints of the county-level public general hospitals (CPGHs). The assessment includes four inputs, three desirable outputs, and one undesirable output. We conducted the assessment via Max-DEA V.8.19 software. Moreover, we analyze the factors affecting CPGHs' medical service efficiency based on the FRM model. </p> <p><strong>Setting:</strong> A total of 77 sample CPGHs were selected from Shanxi province in China from 2013 to 2018.</p> <p><strong>Results</strong>: The results of this study showed that the efficiency level of county-level public hospitals in Shanxi Province is relatively low overall (the mean value of efficiency is 0.61 without quality constraints and 0.63 under quality constraints). This showed that ignoring medical quality constraints will result in lower efficiency and lower health resource utilization for high-medical-quality hospitals. The medical service efficiency of CPGHs differs greatly among different regions. Under the meta-frontier, the hospitals in the central region had the highest efficiency (efficiency score 0.70), followed by those in the south (efficiency score 0.63), and the hospitals in the north had the lowest efficiency (efficiency score 0.54). Factors that have larger impacts on the service efficiency of county public hospitals are the average length of hospital stay, per capita disposable income and financial subsidy income.</p> <p><strong>Conclusions</strong>: To improve CPGHs' medical service efficiency, the government should increase investment in the northern region, and hospitals should improve the management level and allocate human resources rationally.</p>
Resources for "Function-as-a-Service Performance Evaluation with Application-level Workflows"
<p><strong>Thesis</strong></p> <p>"Function-as-a-Service Performance Evaluation with Application-level Workflows"</p> <p><strong>Dataset</strong></p> <p>All extracted data from the benchmark runs in each CSP are available as machine-readable CSV.</p>
The Impact of Comprehensive Medication Management Services on Clinical Outcomes in Patients With Cardiovascular Diseases at Primary Care Level
ClinicalTrials.gov study NCT04778891. IPD Sharing: NO. Countries: 1. Publications: 6.
Effectiveness and Acceptability of Availing Skilled Birth Attendance (SBA) Services Through Community Reproductive Health Nurses (CORN) to a Household Level at Rural Communities of Ethiopia; A Cluster
ClinicalTrials.gov study NCT02501252. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Psychotherapy Strategies for the Treatment of Professionals and Students From Essential Services With High Levels of Emotional Distress in the Context of COVID-19
ClinicalTrials.gov study NCT04635618. IPD Sharing: YES. Countries: 1. Publications: 2.
Study of the medical service efficiency of county-level public general hospitals based on medical quality constraints: A cross-sectional study
Open the record for dataset details and reuse information.
Data on openness level of digital services trade
Open the record for dataset details and reuse information.
Bicycle Level of Service (BLOS) Data
Open the record for dataset details and reuse information.
Supplementary material 2 from: Nedkov S, Nikolova M, Prodanova H, Stoycheva V, Hristova D, Sarafova E (2022) A multi-tiered approach to map and assess the natural heritage potential to provide ecosystem services at a national level. One Ecosystem 7: e91580. https://doi.org/10.3897/oneeco.7.e91580
Priority ES maps of the NH potential
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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.