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288 results for “Method Development”
Mappings for "Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach"
<p>Studies identified false and non-actionnable alarms as a factor for alarm fatigue in intensive care units.</p> <p>To annotate patient alarms, and analyse the alarm situation in intensive care units, we conceptualized and performed data mappings related to airway management and medication interventions. The mappings were based on information retrieved from the patient data management system (PDMS) and clinical expertise. For the airway management mappings, we used additional resources such as ISO 19223:2019 or ventilator instruction manuals. The mappings do not include patient data.</p> <p>As the mappings are generic, they could be used in other contexts than alarm annotation and research.</p> <p><strong>1. Respiratory Management Mappings:</strong></p> <ul> <li>General tables summarizing the 1) categories based on ISO 19223:2019 to describe respiratory support therapies (RSTs), 2) defining the invasiveness level of a RST and 3) listing the abbreviations used in the mappings</li> <li> <p>Tables including PDMS entries for airway devices (ADs), ventilation devices (VDs), and ventilation modes (VMs)</p> </li> <li> <p>Mapping of AD entries (from the PDMS) to defined categories</p> </li> <li> <p>Mapping of VDs, VMs, and ADs to defined RSTs, including information on invasiveness</p> </li> <li> <p>Table specifying suitable ventilation parameters in the context of each RST</p> </li> </ul> <p><strong>2. Medication Mappings:</strong></p> <ul> <li> <p>General tables providing information on physiological alarm conditions (PACs), interventions, routes, and techniques of administration of interest</p> </li> <li> <p>Mapping of routes of administration to techniques of administration including PDMS entries</p> </li> <li> <p>Mapping of active ingredients (including SNOMED CT Fully Specified Names and Identifiers), related PDMS information, and routes and techniques of administration to defined PAC and interventions</p> </li> </ul>
A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods
<p><strong>Description</strong><br> This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br> The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. <br> The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. <br> In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br> We also include sample codes of baseline models for benchmarking of more elaborated models.</p> <p><strong>Data usage</strong><br> The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:<br> <em>Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494</em></p> <p>Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.</p> <p><strong>Sample code</strong><br> As part of the data release, we are also including the sample code written in Python 3. <br> The preprocessed data used in the scripts are also provided. <br> The code can be used to reproduce the results presented in this work and as a starting point for future studies. <br> Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. <br> All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. </p> <p><strong>Units</strong><br> All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br> All irradiance and weather data are in SI units.<br> Sky image features are derived from 8-bit RGB (256 color levels) data.<br> Satellite images are derived from 8-bit gray-scale (256 color levels) data.</p> <p><strong>Missing data</strong><br> The string "NAN" indicates missing data</p> <p><strong>File formats</strong><br> All time series data files as in CSV (comma separated values)<br> Images are given in tar.bz2 files</p> <p><strong>Files </strong></p> <ul> <li><em>Folsom_irradiance.csv</em> Primary One-minute GHI, DNI, and DHI data.</li> <li><em>Folsom_weather.csv </em> Primary One-minute weather data.</li> <li><em>Folsom_sky_images_{YEAR}.tar.bz2</em> Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li> <li><em>Folsom_NAM_lat{LAT}_lon{LON}.csv </em> Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li> <li><em>Folsom_sky_image_features.csv </em> Secondary Features derived from the sky images.</li> <li><em>Folsom_satellite.csv </em> Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. </li> <li><em>Irradiance_features_{horizon}.csv</em> Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). </li> <li><em>Sky_image_features_intra-hour.csv</em> Secondary Sky image features for the intra-hour forecasting issuing times. </li> <li><em>Sat_image_features_intra-day.csv</em> Secondary Satellite image features for the intra-day forecasting issuing times. </li> <li><em>NAM_nearest_node_day-ahead.csv </em> Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li> <li><em>Target_{horizon}.csv</em> Secondary Target data for the different forecasting horizons.</li> <li>F<em>orecast_{horizon}.py </em> Code Python script used to create the forecasts for the different horizons. </li> <li><em>Postprocess.py</em> Code Python script used to compute the error metric for all the forecasts.</li> </ul> <p> </p>
Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer
<p>This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the <span>Laboratoire de Glaciologie (GLACIOL)</span> at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf). </p>
Organizing the fragmented landscape of multidisciplinary product development: A mapping of approaches, processes, methods and tools from the scientific literature - Searchable cartographies
<p>This document gathers cartographies for the development of mechatronic products, cyber-physical systems and smart products. The three cartographies presented are associated with an open-access article – see the citation box below – and differ from the ones provided in the article in that they are searchable, which makes it easier to pinpoint references, concepts and techniques. This document comprises a legend, the cartographies and a list of associated references. </p> <p>To contextualize the cartographies, the integration of digital and connectivity technologies in new products can invite companies to adapt their development. Organizing the fragmented landscape of multidisciplinary product development to help companies navigate the dense scientific literature corpus is a first step in supporting them in doing so. Multidisciplinary product development can be investigated by analyzing specific types of products that deal with both software and hardware development and can be referred to as cyber-physical systems, mechatronics, and smart products and systems in the literature. To support their development, 236 “concepts and techniques” (an expression that encompasses approaches, processes, methods and tools) were identified from 167 scientific papers through an extensive literature review and organized based on a four-level model paired with a decision tree. The mapping of the sorted concepts and techniques made it possible to generate graphical representations called “cartographies.” These cartographies represent a database of concepts and techniques for multidisciplinary product development and serve to support companies in their transformation from the product development perspective by providing them with a general overview of the related literature.</p>
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3).
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 4. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок приморского гребешка (номограмма для 1972 г.). Fig. 4. A grif of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the Crassostrea gigas (nomogram for 1972).
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 3. Сетка термальных ресурсов Зал. Посьета с кривой раЗвития личинок тихоокеанской устрицы (номограмма) [Раков, 1977]. Fig. 3. A grid of thermal resources of waters of Possjet Bay and the curve line of development of larvae of the giant oyster Crassostrea gigas (nomogram) [Rakov, 1977].
Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy
<p>Datasets and scripts for data evaluation</p>
Enzymatic digestion method development for long-term stored chitinaceous planktonic samples - Data
<table> <tbody> <tr> <td>Data for Carrillo-Barragán, Priscilla, Heather Sugden, Catherine Scott, and Clare Fitzsimmons. 2022.<br> “Enzymatic Digestion Method Development for Long-Term Stored Chitinaceous Planktonic Samples.”<br> Marine Pollution Bulletin. </td> </tr> </tbody> </table>
Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation - source data
<p>Full data files for the paper:</p> <p>Duvillier, C., Eckert, N., Evin, G., and Deschâtres, M.: Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation, Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, 2023.</p> <p>Can be used to reproduce all the results of the paper and for further benchmarking of snow avalanche potential release area detection methods.</p>
A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action
<p>This data set pertains to the following research article: Purnell, P.J. (2022) <em>A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action</em>. arXiv:2201.02006</p>
FIGURE 4 in Outline analysis as a new method for investigating development in fossil crabs
FIGURE 4. Morphospace of the data created by the principal component analysis on the elliptic Fourier analysis of the Liocarcinus data set, created by plotting PC1 and PC2 of the PCA against each other. Included shapes depict graphical component loadings for each PC.
FIGURE 3 in Outline analysis as a new method for investigating development in fossil crabs
FIGURE 3. Morphospace of the data created by the principal component analysis on the elliptic Fourier analysis of the Carcinus maenas data set, created by plotting PC1 and PC2 of the PCA against each other. Included shapes depict graphical component loadings for each PC.
FIGURE 1 in Outline analysis as a new method for investigating development in fossil crabs
FIGURE 1. Different developmental stages of Carcinus maenas under fluorescent and natural light, modified after Braig et al. 2023b. A: Dorsal view of megalopa under fluorescent light (car_29G; Appendix 1). B: Dorsal view of juvenile under fluorescent light (car_2C; Appendix 1). C: Dorsal view of adult under natural light (Oliver Mengedoht/Panzerwelten.de, Recklinghausen), source image did not contain a scale. D: Dorsal view of young adult under natural light (Oliver Mengedoht/Panzerwelten.de, Recklinghausen), source image did not contain a scale.
FIGURE 2 in Outline analysis as a new method for investigating development in fossil crabs
FIGURE 2. Scheme of the methodology. Step 1 (s1): Half of the shield outline is reconstructed from source image in dorsal view. Step 2 (s2): The half of the shield is then duplicated and mirrored in anterior-posterior axis and stitched together to form an entire symmetric shield. Step 3 (s3): The shield is registered in R using the Momocs package with 1493 +/- 259 coordinates for the Carcinus data set and 3053 +/- 1736 coordinates for the Liocarcinus data set.
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </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>
Survey and Interview Data from Mixed-Method Survey of Serverless Computing and Function-as-a-Service Software Development in Industrial Practice
<p>This dataset contains the almost-raw data resulting from two out of the three methods chosen by the researchers for their namesake study «A Mixed-Method Empirical Study of Function-as-a-Service Software Development in Industrial Practice». Among the files are web survey questions, anonymised survey results, and interview guidelines. We encourage other researchers to perform open coding and other analysis techniques on the data to verify our claims and to generate new insights.</p>
Test data set for macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins
<p>This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.</p> <p>These data sets can be used to run the following macros that can be found on GitHub:</p> <ol> <li><a href="https://github.com/molcyto/MC-Ratio-96-wells">https://github.com/molcyto/MC-Ratio-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Ratio-Petri-dish">https://github.com/molcyto/MC-Ratio-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-Petri-dish">https://github.com/molcyto/MC-FLIM-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-Bleach-96-wells">https://github.com/molcyto/MC-Bleach-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Scatter5D">https://github.com/molcyto/MC-Scatter5D</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-96-wells">https://github.com/molcyto/MC-FLIM-96-wells</a></li> </ol> <p>Funding:<br> This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands</p> <p> </p>
Figure 17 in Morphometrics: History, development methods and prospects
Figure 17. Major structure of shape variation for the fish sample as assessed the image pixel data (Fig. 5C). See text for discussion.
ScienceDex guides
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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.