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6,783 results for “orientator”
The Effect of Size, Orientation and Temperature on the Deformation of Microcast Silver Crystals (Dataset)
<p>Dataset corresponding to article "The Effect of Size, Orientation and Temperature on the Deformation of Microcast Silver Crystals" (DOI:<a href="https://doi.org/10.1016/j.actamat.2023.118817">https://doi.org/10.1016/j.actamat.2023.118817</a>)</p> <p>Borasi, L., Frasca, S., Charbon, E., & Mortensen, A. (2023). The Effect of Size, Orientation and Temperature on the Deformation of Microcast Silver Crystals. Acta Materialia, 118817.</p>
Cross-education of Skill: Assessment of Overhand Throwing Using Product- and Process-oriented Assessment.
<p>Data used for Cross-education of Skill: Assessment of Overhand Throwing Using Product- and Process-oriented Assessment.</p>
Fig 60 in A review of Afrotropical Trichardis Hermann, 1906, and the description of the first Oriental representative of the genus (Diptera: Asilidae: Laphriinae)
Fig 60. Distribution of Trichardis testacea (Macquart, 1838) in southern Africa.
Fig 57 in A review of Afrotropical Trichardis Hermann, 1906, and the description of the first Oriental representative of the genus (Diptera: Asilidae: Laphriinae)
Fig 57. The distribution of Afrotropical Trichardis species.
Data about of the performance of a building attached photovoltaic panel on different orientations_Ecuador.
<p>Data about of the performance of a building attached photovoltaic panel on different orientations_Ecuador.</p>
Associated data underlying the article "Assessment of the Croatian Open Data Portal Using User-Oriented Metrics"
<p>Associated data underlying the article </p> <p>Miletić, A.; Kuveždić Divjak, A.; Welle Donker, F. Assessment of the Croatian Open Data Portal Using User-Oriented Metrics. <em>ISPRS Int. J. Geo-Inf.</em> <strong>2023</strong>, <em>12</em>, 185. <a href="https://doi.org/10.3390/ijgi12050185">https://doi.org/10.3390/ijgi12050185</a></p> <p>Article Abstract:<br> Open data portals are web services that serve as a central access point for all government-published open data and can exist at local, regional, national, and international levels. They are an important element of most open data initiatives that have enabled a large amount of government data to be widely available. However, data quantity and quality are not the only aspects that should be considered when publishing data. To improve the reusability of data and to achieve greater impact and benefits from open data, it is important to consider user-oriented aspects of the portal management, discovery, and use of data (e.g., organizing the portal in a user-centric way, providing accurate metadata, using a standardized and open data format, etc.). In this paper, we adopted the metrics proposed by the European Commission to assess compliance of the Croatian Open Data Portal with 10 user-oriented principles that open data portals should implement in terms of sustainability and added value. While the results show the government’s efforts in publishing data, some aspects such as better collaboration with data providers and other data portals, offering different visualization tools, etc. need to be improved to achieve active use and impact.</p> <p>Keywords: open data; open data portal; assessment; user experience; data reuse</p>
Borehole orientation data for STIMTEC and STIMTEC-X
<p>The data files contain the orientation information of 17 out of the 19 boreholes drilled in Reiche Zeche (Freiberg, Germany) during the projects STIMTEC and STIMTEC-X.</p>
MITgcm West Antarctic Slope Undercurrent process-oriented model (MITgcm_UC)
<p><strong>MITgcm_UC.zip</strong>: The Matlab scripts used to generate, run and analyze the MITgcm simulations.</p> <ul> <li>MITgcm_UC/newexp_uc: The Matlab scripts used to generate and run the MITgcm simulations.</li> <li>MITgcm_UC/analysis_uc: The Matlab scripts used to calculate products and make figures.</li> </ul> <p><strong>products.zip</strong>: The products calculated from MITgcm_UC model diagnostics and the data used to make figures for the manuscript.</p> <p><strong>All the raw data of the model output are available at: <a href="https://doi.org/10.15144/S4QP4N">https://doi.org/10.15144/S4QP4N</a>.</strong></p> <p>Matlab scripts for vorticity budget analysis in MITgcm_UC/analysis_uc:</p> <ul> <li>calc_all_vorticity.m: A convenient script to load experiments and calculate the vorticity budget</li> <li>functions/calc_BCvorticity_cdw_sw.m: Calculate the vorticity budget for the CDW layer and the surface layer, respectively, after interpolating the momentum budget terms onto a much finer vertical grid (3400 layers in the vertical direction).</li> <li>calc_IceShelfPressureTorque.m: Calculate the pressure torque exerted from the ice shelf to the CDW layer or the surface layer.</li> <li>functions/calc_BCvorticity_ISPT.m: Calculate the decomposition of the pressure torque in the vorticity budget, including the ice-shelf pressure torque, bottom pressure torque, and interfacial pressure torque.</li> <li>functions/calc_BCvorticity_PVint.m: Calculate the potential vorticity of the CDW layer, select PV contours, and then cumulatively integrate the vorticity budget terms for the selected area. </li> <li>plots/fig4_new.m: Plot the vorticity budget (Extended Data Figs. 3 and 6 in the manuscript)</li> <li>plots/fig5_addCDWflux.m: Plot the area-integrated vorticity budget (Fig. 2 of the manuscript)</li> <li>plots/plot_stretch_new.m: Estimated pressure torques of the CDW layer using bottom vertical velocity, the vertical velocity across the upper bound of the CDW layer and the diapycnal velocity. (Extended Data Fig. 4 of the manuscript)</li> </ul> <p><br>Feel free to contact Yidongfang Si via y_si@mit.edu if you have any questions.</p>
Crop orientations over 10 french departments
<p>The dataset contains the data that have been used and generated in the frame of the publication "Determination of Crop Orientation from Very High Resolution Images" submitted by Lonjou V. et al in 2023 to Remote Sensing MDPI. It includes, for 10 french departments (1 folder per department), the plot definition with the associated crop type based on GPR, the parcel centroids and orientations (> 190,000 records), and the ground truth that we manually established over more 1000 plots.</p>
Canasteras, canastos y paisaje en el macizo de los páramos de Mamapacha y Bijagual en el sur oriente de Boyacá
<p> </p> <p>Normalmente, en la vida cotidiana, el arte o en algunas perspectivas académicas la trayectoria que se emplea para comprender el paisaje se ha enfocado desde el afuera: el paisaje que se contempla –a la distancia–, se plasma y se describe. A través de esta presentación, se hará un recorrido para entenderlo desde otra perspectiva: desde las mujeres que, con la gaita (Rhipidocladium geminatum), planta que nace entre los páramos de Mamapacha-Bijagual tejen canastos en las montañas altoandinas boyacenses. El oficio de hacer canastos se denomina en el espacio rural como “canastear”, en diferentes lugares al interior de las veredas de Colombia se canastea pero canastear con la gaita, se hace de una manera particular y alberga toda una serie de prácticas que son indivisibles al espacio, al páramo, las montañas y los caminos. Es en este sentido, que se presenta al paisaje como un medio para el pensamiento antropológico y para comprender que los objetos o la vida material lo construyen a través de la forma en que acoplan de diferentes espacialidades, prácticas y tecnologías. Es así que compartiré los caminos que se trazaron al seguir la invitación a explorar la ecología de los materiales e incluir la sutileza de la forma de vida orgánica para comprender las conexiones, lugares o memorias, por los cuales se transforma la materia para convertirse en un canasto. Así mismo, cómo a través de estas trayectorias se plantea un marco de ideas desde los cuales se conceptualizan los objetos que nacen en el campo como el encuentro de ciertos atributos espaciales que tienen un lugar y un marco de relaciones especificas con elementos vivos, con naturalezas y tecnologías que fluyen a través del cuerpo y del habitar que nos permiten cuestionar los límites de lo humano, las relaciones que establecemos con la naturaleza y sobre cómo otras especies nos constituyen.</p>
Supplemental; Materials - Current state of genomics in nursing: A scoping review of healthcare provider oriented (clinical and educational) outcomes (2012-2022)
<p>Supplemental Table S1 "Scoping Review Data Extraction Table"</p>
Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing
<p><strong>Description:</strong></p> <p>The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip. </p> <p><strong>(A) data_features_and_metric.zip:</strong></p> <p>The following are the contents of this folder</p> <p>(i) <em>grainTheta.csv file</em> : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:</p> <p> <strong>gnid </strong>= grain id number "n", <strong>ntheta = </strong>orientation angle of n<sup>th</sup> grain (<sup>o</sup>); <strong>nltheta </strong>= orientation angle of grain to the left of n<sup>th</sup> grain (<sup>o</sup>); <strong>nrtheta </strong>= orientation angle of grain to the right of n<sup>th</sup> grain (<sup>o</sup>); <strong>j </strong>= current density (A/m<sup>2</sup>);<strong> t =</strong> time (s); <strong>area</strong> = area of n<sup>th</sup> grain (m<sup>2</sup>);<strong> tl</strong> = horizontal length of the top edge of grain "n" (m) ; <strong>bl </strong>= horizontal length of the bottom edge of grain "n" (m) </p> <p>The features gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations. </p> <p>(ii) <em>imc_theta.ipynb, imc_theta.py and imc_theta.html files</em>: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file. </p> <p>(iii) <em>comparison_mse.csv</em>: This data file includes the information about mean square error for training data (tmse) and mean square error for validation data (vmse) at Epoch = 199 resulting from 10 different artificial neural network (ANN) models distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are <strong>modelno</strong>, <strong>lr</strong>, <strong>tmse</strong> and <strong>vmse</strong>. </p> <p>(iv) <em>mse_comparison.gnu</em>: This file consists the codes required to output a png image from the data provided in comparison_mse.csv<em>. </em></p> <p>(v) train_loss.csv and val_loss.csv: These files consist of the data of tmse and vmse at all points of Epochs for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file. </p> <p> </p> <p>(vi) <em>mse_lr2p5e-4.gnu</em> : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv<em>. </em></p> <p><strong>(B) grain_area_prediction.zip:</strong></p> <p>Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files:</p> <p><em>initial_area.csv file</em>: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle.</p> <p><em>predicted_result_00_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_00_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>area_00_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_00_5e4.csv </em>and<em> predicted_result_00_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p><em>area_9090_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_9090_5e4.csv </em>and<em> predicted_result_9090_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p> </p>
Data-driven characterization and correction of the orientation dependence of magnetization transfer measures using diffusion MRI
<p>These are the produced plots for every subjects and every sessions of the study. Here are the characterization plots, the corrected plots, and the tractometry plots.</p>
Self-monitoring Activity: a Randomized Trial of Game-oriented Applications
ClinicalTrials.gov study NCT02341235. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mindfulness-oriented Recovery Enhancement (MORE) for Cancer Pain Relief
ClinicalTrials.gov study NCT05877521. IPD Sharing: YES. Countries: 1. Publications: 1.
Pilot of Mindfulness Oriented Recovery Enhancement in MethadoneTreatment
ClinicalTrials.gov study NCT03894501. IPD Sharing: YES. Countries: 1. Publications: 1.
Developing SUPPORT, a Community-Driven, Recovery-Oriented System of Care
ClinicalTrials.gov study NCT03132753. IPD Sharing: NO. Countries: 1. Publications: 1.
A Behavioral Activation Intervention Administered in a College Freshman Orientation Course
ClinicalTrials.gov study NCT04038190. IPD Sharing: NO. Countries: 1. Publications: 3.
Effect of Structured Progressive Task-Oriented Circuit Class Training With Motor Imagery on Gait in Stroke
ClinicalTrials.gov study NCT03436810. IPD Sharing: NO. Countries: 1. Publications: 18.
Goal Oriented Activity for Latinos With Spine Pain
ClinicalTrials.gov study NCT05005416. IPD Sharing: YES. Countries: 1. Publications: 22.
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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.