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208 results for “Research System”
Raw research data supporting "Assembly of fillable microrobotic systems by microfluidic loading with dip sealing"
<p>Raw research data supporting Sun, R., Song, X., et al., "Assembly of fillable microrobotic systems by microfluidic loading with dip sealing." Advanced Materials. DOI: 10.1002/adma.202207791.</p>
Research data supporting 'Fiber pumps for wearable fluidic systems'
<p>The dataset supporting 'Fiber pumps for wearable fluidic systems', published in <em>Science</em> in March 2023.</p> <p>In this work, we present a fluidic pump in the form of a flexible and stretchable tube. These tubes - or 'fiber pumps' - can generate significant fluidic pressure and flowrate without moving parts or vibration. They are intended to be combined with textiles and clothing. This allows high pressure fluidic circuits to be seamlessly integrated into wearable devices, powering soft supportive exosuits, thermoregulatory clothing, and wearable haptics. </p> <p>This dataset contains the characterisation data of the pumps themselves, as well as characterisation data of the demonstrations that incorporate these pumps. Pump characterisation data includes measurements of pressure, flowrate, power density and other pump metrics. Demo characterisation includes measurements of strain, force, thermal transport and other data. A detailed description of the measurements, file types and naming conventions can be found in the read me file included in the dataset. </p> <p> </p>
Database for terrestrial water system research
<p><strong>Database for terrestrial water system research</strong></p> <p>This database was developed in a study of “<strong>Mohanna Zarei & Georgia Destouni, Research gaps and priorities for tressterial water and Earth System connections from catchment to Global scale, Earth's Future, Wiley, 2024. (https://doi.org/10.1029/2023EF003792)</strong>). It includes data from a scoping literature review, based on searches performed with the Web of Science (WoS) search engine across all available databases. The search was performed on April 22nd 2022, encompassing the fields of title, abstract, authors’ keywords and WoS generated keywords. See <strong>Mohanna Zarei & Georgia Destouni, Research gaps and priorities for tressterial water and Earth System connections from catchment to Global scale, Earth's Future, Wiley, 2024. (https://doi.org/10.1029/2023EF003792)</strong> for further search method and other study descriptions.</p> <p> </p> <p>The database is provided in an Excel (xsls) file, comprising and organised in four sheets as follows:</p> <p>1. Description of the data and project;</p> <p>2. Water flow papers;</p> <p>3. Water storage papers;</p> <p>4. Water quality papers.</p> <p> </p> <p>Column A in each data sheet (2-4) lists the published papers by the end of 2021 that address both ground- and surface water at scales of whole catchment or larger, up to global, for the aspects of water flow (Sheet 2: Water flow papers), water storage (Sheet 3: Water storage papers) or water quality (Sheet 4: Water quality papers), as returned from the automatic WOS search, along with their WOS index (column B) and our article number ID (column C). The other columns in each sheet list manually extracted data regarding the: i) study method (columns D-J), ii) study scale (columns K-N), and iii) study region (columns O-U).</p> <p> </p> <p><strong>Reference</strong></p> <p><strong>Mohanna Zarei & Georgia Destouni, Research gaps and priorities for tressterial water and Earth System connections from catchment to Global scale, Earth's Future, Wiley, 2024. (https://doi.org/10.1029/2023EF003792)</strong></p>
COLLABORATIVE RESEARCH: Cyberinfrastructure for a Virtual Observatory and Ecological Informatics System (VOEIS)
<p>Internally logging temperature sensor (HOBO Water Temp Pro v2) tethered to a buoy at 5 m fixed depth from 2011 to 2014. Buoy located in Flathead Lake, Montana, USA, at the sampling location termed 'Midlake Deep' (-114.0663, 47.8631).</p> <p>Data presented in: Evans, K. A., L. M. Peoples, J. R. Ranieri, E. K. Wear, and M. J. Church. 2023. Mixing-driven changes in distributions and abundances of planktonic microorganisms in Flathead Lake.</p>
Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth
<p>Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth</p>
Research Study to Look at Fast-acting Insulin Aspart With the Insulin Pump System 'iLet™' in Adults With Type 1 Diabetes
ClinicalTrials.gov study NCT03816761. IPD Sharing: YES. Countries: 1. Publications: 1.
Effects of an Evidence Service on Health System Policymakers' Use of Research Evidence
ClinicalTrials.gov study NCT01307228. IPD Sharing: NO. Countries: 1. Publications: 1.
Data for: Research and application of bag filter system for railway ballast bed coal suction vehicles
Open the record for dataset details and reuse information.
EML congruence checker reports from the Long Term Ecological Research Network PASTA system, 2013-2015
These data are results of LTER EML Congruence Checker reports from 9,353 data packages from 2013 to 2015, by 27 current and former LTER sites, plus ad hoc packages contributed by the Network. All data were gleaned from publicly available reports that accompany each data package at upload. These data and related checks and checking software are discussed in O'Brien, M. C, D. Costa and M. Servilla. 2016. Ensuring the quality of data packages in the LTER network data management system. Ecological Informatics. DOI: 10.1016/j.ecoinf.2016.08.001. Data are organized into a denormalized flat table, with one row per check run (nultiple rows per dataset and per entity). No data for error-responses are present, since these data are from uploaded datasets, and datasets with errors are not accepted.
Enhancing the usefulness of artificial seeds in seed beetle model systems research
<p>Seed beetles are among textbook examples of experimental model systems used to better understand nature's complexities. A potential seed beetle model systems strength is the use of artificial seeds to remove experimental confounds. This is particularly relevant for scaling life histories to population dynamics but requires many artificial seeds. Current methods of producing seeds are laborious, limiting their application.</p> <p>Building on previous work, we developed efficient methods to produce artificial seeds and expand their use. We outline steps to produce artificial seeds and describe a new technique for transferring beetle eggs laid on natural seeds to artificial seeds. </p> <p>Our methods yielded a 100-fold increase in artificial seed production that is 80% more efficient than current methods. Burrowing success of beetle larvae from eggs laid on natural seeds and transferred to artificial seeds (85.4%) was comparable to rates on natural seeds. </p> <p>Streamlining artificial seed production enables highly replicated life-history and time-series assays with large sample sizes. The ability to transfer eggs from natural to artificial seeds allows research on many phenomena including oviposition strategies, maternal provisioning, and competitive strategies, broadening the usefulness of seed beetle model systems in ecological and evolutionary research. </p>
Recommender Systems and AI Techniques in E-commerce: An Analysis of Trends and the Research Agenda
Open the record for dataset details and reuse information.
Supplementary material 1 from: Talamas EJ, Mikó I, Johnston-Jordan D (2017) Convergence in the ovipositor system of platygastroid wasps (Hymenoptera). In: Talamas EJ, Buffington ML (Eds) Advances in the Systematics of Platygastroidea. Journal of Hymenoptera Research 56: 263-276. https://doi.org/10.3897/jhr.56.12300
URI table of HAO morphological terms : Explanation note: This table lists the morphological terms used in this publication and their associated concepts in the Hymenoptera Anatomy Ontology.
On a Microservice System Benchmark with Multiple Architected Variants for Software Engineering Research
Open the record for dataset details and reuse information.
Dataset related to Research Article: Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system
<p>Dataset related to research article:</p> <p><strong>Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system</strong></p> <p>by Jacob Mannhardt, Paolo Gabrielli, Giovanni Sansavini (sansavig@ethz.ch)<br>Reliability and Risk Engineering Lab, Institute of Energy and Process Engineering, ETH Zurich</p> <p>All rights lie with the authors. All input data, source code, and result files needed to reproduce results and study. <br>Refer to README.docx for further information on content.</p>
Data supplementing article "Role of baroclinic processes on flushing characteristics in a highly stratified estuarine system, Mobile Bay, Alabama" submitted to Journal of Geophysical Research: Oceans
<p>The dataset uploaded includes the model inputs and outputs, as well as the time-series of dye mass for each of the 16 numerical experiments that are tested in this study. </p>
Supplementary material 3 from: Hardisty AR, Addink W, Glöckler F, Güntsch A, Islam S, Weiland C (2021) A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo). Research Ideas and Outcomes 7: e67379. https://doi.org/10.3897/rio.7.e67379
Comparison of main roles and responsibilities in the two main categories of administration of a Handle-based PID scheme
Matrix aggregation of species of Phyla Annelida (Polychaeta), Mollusca, Arthropoda (Decapoda, Stomatopoda, Amphipoda, and Chelicerata), and Echinodermata registered of the Caribbean Sea and Gulf of Mexico region by Ocean Biodiversity Information Systems of the research "Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) for capturing the biological diversity of two coral reefs in the Yucatán Península, México"
<p>This database consists of an aggregation matrix of species from Ocean Biodiversity Information Systems using as geographic filters the Caribbean Sea region (ID 34287) and the Gulf of Mexico region (ID 34287) nomenclature and hierarchical classification of each Phyla from World Register of Marine Species used for the calculation of average taxonomic distinction of species belonging to the Phyla Annelida (Polychaeta), Mollusca, Arthropoda (Decapoda, Stomatopoda, Amphipoda, and Chelicerata), and Echinodermata associated to Autonomous Reefs Monitoring Structures from the research “Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) to estimate cryptic diversity in two coral reefs of the Yucatan Península, México”</p> <p><strong>*Corresponding autor: </strong>edlinguerra@gmail.com</p> <p>BIS Ocean Biodiversity Information System. Available online: <a href="http://www.iobis.org/">www.iobis.org</a>.</p> <p>Horton, T.; Gofas, S.; Kroh, A.; Poore, G.C.B.; Read, G.; Rosenberg, G.; Stöhr, S.; Bailly, N.; Boury-Esnault, N.; Brandão, S.N.; et al. Improving nomenclatural consistency: A decade of experience in the World Register of Marine Species. <em>Eur. J. Taxon.</em> <strong>2017</strong>, <em>2017</em>, doi:10.5852/ejt.2017.389.</p> <p><span lang="EN-US">was produced in collaboration with the Biodiversidad Marina de Yucatán project. </span><a href="https://www.bdmy.org.mx/carteles-publicaciones/" target="_blank" rel="noopener">https://www.bdmy.org.mx/,</a> Universidad Nacional Autonoma de México and Escuela Nacional de Estudios Superiores</p>
Research data related to the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)"
<p><strong>Research Data related to the publication "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023) published in <em>Water Resources Research</em> </strong></p> <p>Dear reader,</p> <p>research data are provided for the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023). The authors hope that the research data allows for a better understanding of the paleo-modeling workflow. Feedback on the model files or questions regarding the modeling approach etc. can be addressed to the authors of the article, see contact details below. The research data comprises the following files:</p> <ul> <li>files related to the parameter estimation procedure using PEST (Doherty, 2021a,b) (see subfolder "<em>parameter_estimation</em>")</li> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for each model variant. Note that model variants consist of several time slice models, indicated by the corresponding file names, e.g., '<em>Model_BC_slice_01.py'</em> etc. (see '<em>scripts.zip</em>' in the subfolders 'Model BC', 'Model CP', 'Model NE-ND-NP', 'Model NE-NP', 'Model NG', 'Model NP', 'Model R1', 'Model R2', 'Model R3', 'Model R4', 'Model R5', 'Model R6', 'Model SS')</li> <li>simulation output files, including concentration and head data for each model stress period (3-D), mean/max. concentration and head data for each model stress period (2-D), as well as depth [mbgs] of different salinity interfaces (2-D), i.e., marking the transitions from fresher to more saline groundwater using thresholds of 0.45 ('<em>depth_interface_mbgs</em>'), 1, 5, 10 and 20 g TDS L<sup>-1</sup>, respectively (see subfolders '<em>output/npy_arrays'</em> within each model variant subfolder). Moreover, sea levels, time slice names and stress period numbers are provided in the '<em>output/npy_arrays'</em> subfolders as well as final concentrations and heads (3-D) for each time slice model of each model variant (e.g., '<em>Model_BC_slice_01_final_concentrations.npz</em>' and '<em>Model_BC_slice_01_final_heads.npz</em>'; see '<em>output.zip'</em> in the model variant subfolders)</li> <li>iMOD-Python (Visser and Bootsma, 2019) input files, such as digital elevation models, geologic models etc. (see subfolder '<em>imod_input'</em>). However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consult the corresponding meta-data files or get in touch with one of the authors for further information</li> <li>bash scripts for the execution of iMOD-Python .py- and iMOD-WQ .run-files in a linux environment (see subfolder '<em>bash_scripts'</em>)</li> <li>figure files as well as the corresponding .py and .m scripts and shape-files, where applicable (see subfolder '<em>figures'</em>); note that consent for re-distribution for some figure input files doesn't exist, compare corresponding meta-data files</li> <li>videos presenting the concentration evolution of the different model variants (vertically averaged concentrations & cross-sectonal view, see subfolder '<em>videos'</em>)</li> </ul> <p>Meta-data files are usually provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input data and .run-files were executed on the University Oldenburg High-Performance Cluster 'Carl', running simulations in parallel with 32 computational cores.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>Literature:</p> <p>Doherty, J. E., (2021a). PEST Model-Independent Parameter Estimation User Manual Part I: PEST, SENSAN and Global Optimisers. Watermark Numerical Computing. p.394.</p> <p>Doherty, J. E. (2021b). PEST Model-Independent Parameter Estimation User Manual Part II: PEST Utility Support Software. Watermark Numerical Computing. p.274.</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p><strong>If you have further questions, please, contact one of the following authors</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Research Data for "Vertical Leaching of Paleo-Saltwater in a Coastal Aquifer–Aquitard System of the Pearl River Delta"
<p>Supplementary material includes one supplemental text on analytical derivation, two supplemental figures, and the measured data of radium and radon.</p>
Clinical Research on the Application of Single-channel Uterine Fibroid Morcellation System in Laparoscopic Myomectomy
ClinicalTrials.gov study NCT06442605. IPD Sharing: NO. 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.