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170 results for “effectiveness and technology”
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Data set to Conference Paper "The Effect of Queuing Technology on Customer Experience in Physical Retail Environments"
<p>Following an open data policy as supported by the European Union (https://www.openaire.eu/), this is the data set used for the following conference paper: Obermeier, G., Zimmermann, R., & Auinger, A. (2020, July). The Effect of Queuing Technology on Customer Experience in Physical Retail Environments. In <em>International Conference on Human-Computer Interaction</em> (pp. 141-157). Springer, Cham.</p> <p>The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.</p>
Effect of MgO sintering additive on mullite structures manufactured by fused deposition modeling (FDM) technology
<p>An optimized recipe for 3D printing of Mullite-based structures was used to investigate the effect of MgO sintering additive on the processing stages and final ceramic properties. To achieve dense 3:2 mullite, ceramic filaments were prepared based on an alumina powder, a methyl silicone resin, EVA elastomeric binder and MgO powder. Using 1 wt% MgO and a dwell time of 5 h at 1600 °C, a dense mullite structure could be obtained from filaments with a diameter of 1.75 mm. Ceramic structures with and without sintering additive were printed in vertical and horizontal direction, to investigate the effect of printing direction on mechanical strength after sintering. Using four-point bending test, it was demonstrated that by using MgO, the printing orientation did not affect the mechanical strength significantly anymore. The low Weibull modulus could be explained by the closed porosity that emerge during the degassing of the preceramic polymer due to cross-linking.</p>
Data from: Technological Evaluation of Fiber Effects in Wheat-Based Dough and Bread
<p>This dataset is linked to the article by Celeste Verbeke, Els Debonne, Stien Versele, Filip Van Bockstaele and Mia Eeckhout, published in Foods (August 2024):<br>"Technological Evaluation of Fiber Effects in Wheat-Based Dough and Bread" (DOI: https://doi.org/10.3390/foods13162582).</p> <ul> <li>Farinogram curve data.csv & Alveogram curve data.csv & Pasting curve data.csv <ul> <li>Observations: <ul> <li>Ref = wheat flour</li> <li>PF1/5/10 = 1/5/10% pea fiber</li> <li>CF1/5/10 = 1/5/10% cocoa fiber</li> <li>AF1/5/10 = 1/5/10% apple fiber</li> <li>1/2/3/avg = Replicate 1/2/3 & average of the three replicates</li> </ul> </li> </ul> </li> <li>Dough and bread characteristics.csv <ul> <li>Observations:<br> <ul> <li>Ref = wheat flour</li> <li>PF1/5/10 = 1/5/10% pea fiber</li> <li>CF1/5/10 = 1/5/10% cocoa fiber</li> <li>AF1/5/10 = 1/5/10% apple fiber</li> </ul> </li> <li>Abbreviations: <ul> <li>WRC = water retention capacity</li> <li>WA = water absorption</li> <li>DDT = dough development time</li> <li>STAB = stability</li> <li>SOFT = softening</li> <li>P = tenacity</li> <li>L = extensibility</li> <li>W = deformation energy</li> <li>PH = proving height</li> <li>IV = initial viscosity</li> <li>T_past = pasting temperature</li> <li>V_peak = peak viscosity</li> <li>T_peak = peak temperature</li> <li>HS = holding strength</li> <li>V_final = final viscosity</li> <li>BD = breakdown</li> <li>SB_peak = setback from peak</li> <li>SB_total = total setback</li> </ul> </li> </ul> </li> </ul>
Suggested Taxonomy: Tracking Technologies to Effectively Capture and Input Key Data on the Blockchain
<p>Within the paper titled "Transparency with Blockchain and Physical Tracking Technologies: Enabling Traceability in Raw Material Supply Chains" (Mater. Proc. 2021, 5(1), 1; <a href="https://doi.org/10.3390/materproc2021005001">https://doi.org/10.3390/materproc2021005001</a>), we consider the majority of tracking technologies to be part of the IoT ecosystem and suggest a taxonomy with their key features, benefits and use cases in the mining industry. Although technologies such as markers and QR/Barcodes are not necessarily electronic devices, they can integrate with other IoT objects and provide or qualify a digital identity. The common element connecting all these technologies is that they include functionalities that can capture and communicate granular, timely, relevant and accurate data, which can be automatically or manually entered into the blockchain. </p> <p>We have analysed the following most common physical tracking methods which will be described and exemplified in more detail below:</p> <ul> <li> <p>Video monitoring (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t001">Table 1</a>)</p> </li> <li> <p>Bar and QR codes (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t002">Table 2</a>)</p> </li> <li> <p>Markers and taggants (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t003">Table 3</a>)</p> </li> <li> <p>Cellular, near range and low power network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t004">Table 4</a>)</p> </li> <li> <p>Satellite network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t005">Table 5</a>)</p> </li> </ul>
A cost effectiveness analysis on interventions for childhood anemia in developing countries: A health technology assessment
<p>This is a data sheet of the "A cost-effectiveness analysis on interventions for childhood anemia in developing countries: A health technology assessment" used in the study. </p>
Seedball technology counterbalances the effect of small seed-size and low soil nutrients on early pearl millet seedling performance
<p>In the African Sahel region, pearl millet (<em>Pennisetum glaucum</em> (L). R. Brown) is often produced in low-nutrient soils. Seed weight could vary between 4 and 44 mg per seed. Evidence shows chemically infertile soil and small seed size significantly reduce seedling establishment and, in turn, cause low grain yield. Seedball technology can potentially counterbalance this effect. Therefore, the objective of this study was to investigate the influence of seedball on pearl millet seedling establishment. Conventionally sown and seedball-derived pearl millet seedlings of a local and an improved varieties were grown for 29 days from small and large seed sizes, in low- and medium-nutrient soils at a greenhouse of University of Hohenheim, Germany. Results showed that under low-nutrient conditions and with small seed sizes produced biomass was generally inferior to the other factor combinations. Seedball technology significantly enhanced seedling vigour, leaf number, plant height, dry matter, root length as well as fine root development, and nutrient uptake irrespective of soil nutrient level and seed size. These enhancement effects were more obvious in the local variety. A previous study revealed “early nutrient release in the seedling root zone” as seedball pearl millet seedling enhancement mechanism. The released nutrients presumably compensated for nutrient deficiency in low-nutrient soil and small seed seedlings. In the Sahelian pearl millet production system where (i) low soil nutrients, (ii) small seed sizes and (iii) local seed varieties are rampant, the application of the seedball technology under these conditions is proven effective for increased seedling vigour and is therefore recommended.</p>
The dataset for the research "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
In recent years, the topic of digitalisation and sustainability of supply chains has become increasingly important. In addition, as the environmental dynamism becomes more complex, it is essential to explore how technologies impacts on sustainability under the supply chain dynamism. Hence, there is a study to explore the relationship between technologies and sustainability under the supply chain dynamism in the energy supply chain. In this study, the author collects quantitative data from two Chinese companies, including China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd. This is a questionnaire survey and it has 24 questions, including 3 general questions, 5 technologies dimension questions, 12 sustainability dimension questions and 4 supply chain dynamism questions. The author collected data from 30 May 2024 to 6 June May 2024, and there are totally 316 answers.
Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria (AGSUS)
Open the record for dataset details and reuse information.
Dataset for "Integrated acoustic resonators in commercial fin field-effect transistor technology"
<p>Companion dataset and code for "Integrated acoustic resonators in commercial fin field-effect transistor technology" by Anderson, He, Bahr, and Weinstein.</p> <p>https://www.nature.com/articles/s41928-022-00827-6</p> <p>https://arxiv.org/abs/2107.00608</p> <p> </p>
Effects of water source and technology on energy use and environmental impacts of rice production in northern Iran
<p>To analyze the energy flow and greenhouse gas emissions, a total of 200 paddy fields were selected based on the water source utilized for irrigation (river as the surface water source and well as the underground water source), transplanting methods (traditional and mechanical) and grain yield (low- and high-yielding rice cultivars) as a factorial experimental design (2×2×2=8 conditions). Data were collected in 2020 from 200 rice growers in northern Iran using a face-to-face questionnaire survey.</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
The dataset of "Evaluation of Digital Supply Chain Technology's Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
<p>This dataset involves the data from the questionnaire, which come from the project "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain". It comprises three dimensions questions, technology, sustainability and supply chain dynamism. The datas come from two Chinese energy firms, <span>China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd.</span></p>
Benign effects of logging on aerial insectivorous bats in Southeast Asia revealed by remote sensing technologies
<b>Description: </b><p>Number of bat calls recorded by SongMeter bat 2 detectors set to record continuously on a trigger. Counts are classified into 21 acoustic call types, including 13 species.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/101"><b>Impacts of forest modification on bats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK Natural Environment Research Council (NERC) (Human Modified Tropical Forests programme & a PhD scholarship jointly funded by University of Kent & NERC & EnvEast DTP scholarship, NE/L002582/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Economic Planning Unit of the Malaysian Government and the Sabah Biodiversity Council (Research licence UPE: 40/200/19/2723)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740421">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_data_archive_Yoh2.xlsx</p><p><b>SAFE_data_archive_Yoh2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>All data </b> (described in worksheet MasterData)</p><p>Description: All auto and manual identifications for bat passes across a disturbance gradient, identified to functional group or species when possible</p><p>Number of fields: 13</p><p>Number of data rows: 134920</p><p>Fields: </p><ul><li><b>LOCATION</b>: Where the data was collected (Field type: location)</li><li><b>DATE</b>: Date surveyed (Field type: date)</li><li><b>TIME</b>: Time of recording (Field type: time)</li><li><b>AUTO_ID</b>: Taxa as identified using the automatic classifier (Field type: taxa)</li><li><b>ACCURACY</b>: Confidence value for auto identification results (Field type: numeric)</li><li><b>THRESLEVEL</b>: Whether the data met the desired auto-identification confidence value (Field type: categorical)</li><li><b>MANUAL_ID_CLEAN</b>: Taxa as identified manually (Field type: taxa)</li><li><b>FINAL_ID</b>: Final taxa label considering both the auto and manual ID (Field type: taxa)</li><li><b>TREATMENT</b>: Habitat type (Field type: categorical)</li><li><b>fc_100m</b>: Forest extent within 100m buffer of the survey location (Field type: numeric)</li><li><b>chm_100m</b>: Average canopy height within 100m buffer from survey location (Field type: numeric)</li><li><b>shape_100m</b>: Forest shape within 100m buffer of survey location (Field type: numeric)</li><li><b>TRI_100m</b>: Topographic ruggedness within 100m buffer of survey location (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2011-04-01 to 2012-06-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Chiroptera <br> -  -  -  -  -  [CF_CROB] <br> -  -  -  -  -  [CF_H140] <br> -  -  -  -  -  [FMQCF1] <br> -  -  -  -  -  [FMQCF2] <br> -  -  -  -  -  [FMQCF3] <br> -  -  -  -  -  [FMQCF4] <br> -  -  -  -  -  [FMQCF5] <br> -  -  -  -  -  [FMQCF6] <br> -  -  -  -  -  [QCF] <br> -  -  -  -  -  [FM] <br> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus affinis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus creaghi</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus luctus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus philippinensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ater</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros galeritus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br></div><p></p>
A Clinical Study to Assess the Effects of Various Dentifrice Technologies on Dentinal Hypersensitivity
ClinicalTrials.gov study NCT03965039. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluating the Effects of Frozen Section Technology on Oncological and Functional Outcomes at Radical Prostatectomy.
ClinicalTrials.gov study NCT03317990. IPD Sharing: YES. Countries: 1. Publications: 7.
A Home-based Study Using Mobile Technology to Test Whether BI 1358894 is Effective in People With Depression
ClinicalTrials.gov study NCT04423757. IPD Sharing: YES. Countries: 1. Publications: 0.
Effectiveness of Technology-Supported Hand Strengthening and Stretching Exercises in Patients With Rheumatoid Arthritis
ClinicalTrials.gov study NCT04365842. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Identify Effective Doses of SHR7280 Tablets in Controlled Ovarian Hyperstimulation (COH) for Female Subjects Undergoing Assisted Reproductive Technology (ART)
ClinicalTrials.gov study NCT05082233. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Clinical Effectiveness of a Patient-tailored Orthosis Based on 3-dimensional (3D) Scanner Modeling and 3D Printing Technology for Microstomia Caused by Burns : Pilot Study
ClinicalTrials.gov study NCT07264218. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
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.