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587 results for “guidance”
Supporting publication for 'Prevalence sample-based guidance for reporting 2022 data'
<p>The record is aimed at helping the reporting countries to submit their sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, and we give below specific information on their use.</p> <p>The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.</p>
Modelling Guidance in Software Engineering: A Systematic Literature Review
<p>The dataset contains three supporting documents covering the paper selection details (including selection criteria and extracted papers after each round) from three rounds of selection for the literature review titled- Modelling Guidance in Software Engineering: A Systematic Literature Review. There are three excel sheets named: <strong>search_s1</strong>,<strong> search_s2</strong> and <strong>snowballing_bpm</strong>. The file "<strong>search_s1</strong>" contains details of the search with the search string: {("modeling" OR "modelling" OR "model-driven" OR "model-based") AND ("guidelines" OR "training" OR "styles" OR "creation") AND ("software engineering")}. The second file, "<strong>search_s2</strong>", contains details of the search with the string: {("modeling" OR "modelling" OR "model-driven" OR "model-based") AND ("approach" OR "process" OR "method" OR "template") AND ("software engineering")}. The third file, "<strong>snowballing_bpm</strong>", contains the backward and forwards snowballing search results. </p>
Fig. 4. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 4. A variogram for the ciliate Pleuronema sp. (inset) abundance. The best fit to the data (points) provided a pure nugget model; i.e. the distribution is random at the measured scale (40 m), with no observed patchiness.
Fig. 3. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 3. A time series of Cyrtostrombidium sp. (inset) abundance during ~ 1 year at a fix point in a coastal lagoon. The autocorrelation function indicates positive spikes for weeks 2, 3 and 4 suggesting a persistence of Cyrtostrombidium bloom for ~ 1 month. Horizontal dashed lines indicate the ~ 95% confidence interval for the signifi- cance of each autocorrelation value.
Fig. 2 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 2. Geostatistical analysis of Lohmaniella oviformis (inset in a) abundance (cells ml–1) produces: a) the variogram, b) the kriging map, and c) a map of the coefficient of variation (CV). A spherical model (a, line) is fit to the empirical variogram (a, points); the points account for different number of pairs of abundance averaged on a class distance (lag). Only half of the maximum distance was calculated and represented to avoid the edge effect, where there are fewer sampling points (see text). The model (a, line) is used to predict abundance at unsampled points and to assess characteristics of patches. The model is also used to map patches of L. oviformis abundance (b, grey areas) using the kriging interpolator; a patch is operationally defined as abundance in the upper quartile. On the CV map (c), grey areas (with lower abundance and closer to edges) have the highest coefficient of variation of the estimated distribution.
Fig. 1. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 1. A schematic description of establishing a variogram, modelling a function, and producing maps by kriging. Samples (e.g. to determine ciliate abundance) are collected at points of a sampling grid (a). Variance estimates of ciliate abundances at points separated by a common distance (lag, h) are calculated using the equation (explanations in the text); this is repeated for each lag (three examples of lags are illustrated in a). Each variance estimate is then plotted against its respective lag to produce an empirical variogram (points in b). Then, a model is fit to the variogram data (lines in b), and the model is used to predict abundance at unsampled points and to characterize patches. The parameters of the variogram models are the nugget, the range, and the sill (see text for their interpretation). Three models are the most common: the Gaussian, spherical and exponential (thick, medium, and thin lines, respectively, in b). Models are used to map ciliate abundance by the kriging procedure, with each model producing different predicted distributions (c, d, e): the spherical and exponential produce "fuzzier" images than the Gaussian.
Fig. 6 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 6. Patches of total phytoplankton biomass (ng C ml–1, left) and total ciliate abundance (cells ml–1, right) in the Irminger Sea, North Atlantic. The spatial coincidence indicates a potential prey-predator relationship.
Sonography display demonstrating intra-operative ultrasound imaging guidance for the localization of the foreign body (dental implant) in the soft tissues of the floor of the mouth via navigation with a spinal needle.
<p>This video demonstrates the intraoperative navigation system with using sonography to localize foreign bodies in the soft tissues of the floor of the mouth with the help of a spinal needle</p>
Supporting publication for 'Prevalence sample-based guidance for reporting 2020 data'
<p>These two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer examples on how prevalence data can be reported using SSD2.</p>
Project Colossus - Apollo Guidance Computer
A model of the Apollo Guidance Computer (block II) main body, the DSKEY input unit and a sample logic board module for the computer. The name, 'Colossus' refers to the in-house name for the program the computer runs on. This project's aim was to focus on my presentation, and I decided to set it out like a museum exhibit. All the info for the annotations sourced from: https://en.wikipedia.org/wiki/Apollo_Guidance_Computer https://en.wikipedia.org/wiki/Core_rope_memory This isn't meant to be an exhaustively studied or accurate rendition, so take it with a grain of salt. Source: Objaverse 1.0 / Sketchfab
Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks
<p>During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53±0.39%, and the average testing relative error in distance estimation reached 4.42±0.56% (36.09±4.92 μm).</p> <p>The dataset is split into two parts:<br> (1) <strong>Classification</strong>. The zip file <em>veress_classification_raw_images.zip</em> contains 40K images from 8 swine samples where there are 1K images per layer (skin, fat, muscle, abdominal space, and small intestine)<br> (2) <strong>Regression</strong>. The zip file <em>veress_regression_raw_images.zip</em><strong> </strong>contains 8K images of the abdominal space from the same 8 swine samples, and the ground truth distance labels for each sample are found in the Excel files <em>S[1-8]_distance_measurement_20210803.xlsx.</em></p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Video]
<p>Video of the paper submitted at RO-MAN 2022 </p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p> <p>Code for trainings and test available at:</p> <p>https://github.com/giorgionicola/SMAHRCO</p>
2022_Shayya_UPR_Guidance
<p>This repository contains raw, intermediate and final supplementary data associated with the following study:</p> <p><strong>ER stress transforms stochastic olfactory receptor gene choice into stereotypic axon guidance programs</strong></p> <p>Hani J. Shayya<sup>1,2,3</sup>, Jerome K. Kahiapo<sup>1,3</sup>, Rachel Duffié<sup>1</sup>, Katherine S. Lehmann<sup>4</sup>, Lisa Bashkirova<sup>1</sup>, Kevin Monahan<sup>1</sup>, Ryan P. Dalton<sup>5</sup>, Joanna Gao<sup>6</sup>, Song Jiao<sup>4</sup>, Ira Schieren<sup>1</sup>, Leonardo Belluscio<sup>4</sup>, and Stavros Lomvardas<sup>1,7,8</sup></p> <p><sup>1</sup>Mortimer B. Zuckerman Mind, Brain and Behavior Institute, Columbia University, New York, NY 10027, USA<br> <sup>2</sup>Medical Scientist Training Program, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA<br> <sup>3</sup>Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA<br> <sup>4</sup>Developmental Plasticity Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892 USA<br> <sup>5</sup>The Miller Institute for Basic Research in Science, University of California Berkeley, Berkeley, CA 94720 USA<br> <sup>6</sup>Barnard College, New York, NY, 10025 USA<br> <sup>7</sup>Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA<br> <sup>8</sup>Department of Neuroscience, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA</p> <p>See https://github.com/hshayya/2022_Shayya_UPR_Guidance for code and additional information on organization of the dataset.</p> <p>Unzip the file here to recover the folder structure referenced in the Github README.</p>
Fig. 5 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 5. Summarized species richness map (see text for explanation).
Feedback on the draft implementation guidance of Plan S
<p>Launched in September 2018, cOAlition S is an initiative of an international consortium of research funders to make full and immediate Open Access to research publications a reality. It is built around Plan S, which consists of one target and 10 principles. In November 2018, cOAlition S released a draft guidance on the implementation of Plan S for public feedback. This process, which lasted from November 2018 to February 2019, collected more than 600 feedback statements from universities, learned societies, publishers, scholarly associations, and individual scholars from more than 40 countries. The documents include all responses and accompanying statements received during the process. Responses have been analysed and an updated guidance on implementation was adopted and published in May 2019. In order to facilitate the download of the full results, a Zip file (002_Plan S_Feedback_all_files) has been added in version 3. Please visit cOAlition S website for more information: <a href="https://www.coalition-s.org/">https://www.coalition-s.org/</a></p>
Geometric and mechanical guidance: role of stigmatic epidermis in early pollen tube pathfinding in Arabidopsis
<div> <p>Geometry and cell wall mechanics guide early pollen tube growth in <em>Arabidopsis thaliana</em></p> </div> <div><span><span><span>Lucie</span> <span>Riglet</span></span>, <span><span>Catherine</span> <span>Quilliet</span></span>, C<span><span>hristophe</span> <span>Godin</span></span>, <span><span>Karin</span> <span>John</span></span>, I<span><span>sabelle</span> <span>Fobis-Loisy</span></span></span></div> <div> </div> <div><span><span>preprint available under</span></span></div> <div><span><span>doi:</span> https://doi.org/10.1101/2024.02.05.578915</span></div> <p>---</p> <p>contained are the source codes and scripts to reproduce the results shown in Figs. 3,4 and Supplementary Figures S2, S3</p> <p>(the results of Fig. S3 D,E is not contained in the preprint)</p> <p>---</p> <p>Numerical code and script files to reproduce pollen tube trajectories on papilla surfaces</p> <p>tested on:</p> <p>*) macOS Catalina 10.15.6 </p> <p><span>*) Apple clang version 11.0.3 (clang-1103.0.32.62)</span></p> <p><span>Target: x86_64-apple-darwin19.6.0</span></p> <p><span>*) R 4.0.0 GUI 1.71 Catalina build (7827)</span></p> <p>---</p> <p></p>
Data for: Visual guidance of honeybees approaching a vertical landing surface
<p>Landing is a critical phase for flying animals, whereby many rely on visual cues to perform controlled touchdown. Foraging honeybees rely on regular landings on flowers to collect food, crucial for colony survival and reproduction. Here, we explore how honeybees utilize optical-expansion cues to regulate approach flight speed when landing on vertical surfaces. Three sensory-motor control models have been proposed for landings of natural flyers. Landing honeybees maintain a constant optical-expansion-rate set-point, resulting in a gradual decrease in approach velocity and gentile touchdown. Bumblebees exhibit a similar strategy, but they regularly switch to a new constant optic-expansion-rate set-point. Meanwhile, landing birds fly at a constant time-to-contact to achieve faster landings. Here, we re-examined the landing strategy of honeybee by fitting the three models to individual approach flights of honeybees landing on platforms with varying optic-expansion cues. Surprisingly, the landing model identified in bumblebees proves to be the most suitable for these honeybees. This reveals that honeybees adjust their optic-expansion-rate in a stepwise manner. Bees flying at low optic-expansion-rates tended to stepwise increase their set-point, while those flying at high optic-expansion-rates tend to stepwise decrease it. This modular landing control system enables honeybees to land rapidly and reliably under a wide range of initial flight conditions and visual landing platform patterns. The remarkable similarity between the landing strategies of honeybees and bumblebees suggests that this may also be prevalent among other flying insects. Furthermore, these findings hold promising potential for bioinspired guidance systems in flying robots.</p>
Supporting Data and Guidance: Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS
<p>This repository contains data files and guidance documents that are supplementary materials to accompany the policy paper "Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS" available on Research Square here: <a href="https://www.researchsquare.com/article/rs-2702275/v1">https://www.researchsquare.com/article/rs-2702275/v1</a></p>
Accompanying data for: Local negative feedback of Rac activity at the leading edge underlies a pilot pseudopod-like program for amoeboid cell guidance
<p>This is the data underlying all of the figures in: Local negative feedback of Rac activity at the leading edge underlies a pilot pseudopod-like program for amoeboid cell guidance by Jason P. Town and Orion D. Weiner</p> <p>Paper Abstract: To migrate efficiently, neutrophils must polarize their cytoskeletal regulators along a single axis of motion. This polarization process is thought to be mediated through local positive feedback that amplifies leading edge signals and global negative feedback that enables sites of positive feedback to compete for dominance. Though this two-component model efficiently establishes cell polarity, it has potential limitations, including a tendency to “lock” onto a particular direction, limiting the ability of cells to reorient. We use spatially-defined optogenetic control of a leading edge organizer (PI3K) to probe how neutrophil-like HL-60 cells balance “decisiveness” needed to polarize in a single direction with the flexibility needed to respond to new cues. Underlying this balancing act is a local Rac inhibition process that destabilizes the leading edge to promote exploration. We show that this local inhibition enables cells to process input signal dynamics, linking front stability and orientation to local temporal increases in input signals. </p> <p>All data can be processed using standard scientific python packages using the Jupyter Notebooks located <a href="https://zenodo.org/badge/latestdoi/672328942">here</a>. To run the notebooks and reproduce the figures in the paper, download all of the zipped data files, unzip them, and arrange them into the following directory structure:</p> <ul> <li>projectfolder <ul> <li>data <ul> <li>fig03data</li> <li>fig04data</li> <li>fig05data</li> <li>figS01data</li> <li>figS08data</li> <li>figS10data</li> <li>simpleStimdata</li> </ul> </li> <li>figure01.ipynb</li> <li>figure02.ipynb</li> <li>...</li> </ul> </li> </ul> <p>Microscopy data was collected using an automated, Pycromanager-based pipeline. The most recent version of the pipeline can be found <a href="https://zenodo.org/badge/latestdoi/583790048">here</a>. </p> <p>This zipped folders contains microscopy data (images & metadata) in several directories:</p> <ul> <li>"simpleStimData" contains data used in several figures in the paper. It shows the dynamic behaviors of neutrophils as they are subjected to local optogenetic activation of optoPI3K. <ul> <li>File folders are not entirely standardized, but typically contain <ul> <li>The data of the experiment (YYMMDD format)</li> <li>The cell line used and the constructs expressed (35 is the optogenetic anchor, 52 is the optogenetic cargo, 97 is the PH-Akt-Halo PIP<sub>3</sub> reporter, and mcPak is the Pak-PBD-mCherry Rac reporter)</li> <li>A generic phrase 'Assays', 'VariousFrontStimulations', 'simpleStimScreen' that has no experimental meaning</li> <li>An indicator of the pre-programmed assay "type" - either 'none, 'front', 'side', 'back', 'global', 'lateral45', 'lateral90', '0Rad', '0.2 Rad', '0.39Rad', '0.79Rad' or '1.57Rad'</li> <li>The intensity of blue LED light used in the experiment ('50000'). This did not turn out to be very important for the paper in this particular dataset, but cells do not display an all-or-none response to optogenetic PI3K acitvation.</li> <li>And finally a replicate number for that assay that day (i.e. '000', '001', ...)</li> </ul> </li> <li>Within each file folder, there is a collection of images (Note that each image has a lot of extra information in the associated metadata) <ul> <li>Channel 0 is the PHAkt PIP<sub>3</sub> reporter fluorescence data</li> <li>Channel 1 is the Pak-PBD Rac reporter fluorescence data</li> <li>Channel 2 is a reflected blue light dataset. A small degree of blue light reflected off the bottoms of coverslips and could be collected on the camera</li> <li>Channel 3 is a binary segmentation channel generated on-the-fly during acquisition by our automated microscopy software</li> <li>Channel 4 is another binary segmentation channel, but this one is a prediction of the real image of the optogenetic stimulus based on a calibrated affine transformation of the projected image onto the microscopy field of view. </li> <li>if present, Channels 5 and 6 are control images taken at the end of the experiment to verify the presence of optogenetic constructs 35 (405 channel) and 52 (488 channel)</li> </ul> </li> <li>There is a small amount of data here that was not directly used in the paper. For example, the 'frontToGlobal' experiments did not produce interesting results, but actually led us to develop the method shown in Figure 5 in our paper where we did something closer to a 'sideToGlobal' assay (See Fig 5 in the paper). </li> </ul> </li> <li>"fig03Data" contains data used to generate Fig 3 and Fig S7B in the paper. This data is structured identically to the data in simpleStimData described above</li> <li>"fig04Data" contains data used to generate Fig 4 and Fig S9D in the paper. Some of this is from older software, so the metadata (i.e. LED intensities) are stored in a separate file called intensities.txt. This text file contains time stamps and LED intensity data that can be matched with the temporal metadata from the microscopy images. </li> <li>"fig05Data" contains data used to generate Fig 5 and Fig S11. These are structured identically to the simpleStimData structure described above. (Though these files are stored in more organized subdirectories compared to simpleStimData). </li> <li>"figS01Data" contains the microscopy data used to generate Fig S1</li> <li>"figS08Data" contains data used to generate Fig S8 in the paper. This is from older microscopy automation software, so the metadata (i.e. LED intensities) are stored in a separate file called metadata.txt. This text file contains time stamps and LED intensity changes that can be matched with the temporal metadata from the microscopy images. </li> <li>"figS10Data" contains data used to generate Fig S10 in the paper. This is a combination of data structures similar to that described above in "fig04Data" for the step inputs and "simpleStimData" for the turning assays.</li> </ul> <p> </p> <p> </p>
Prospective Exploratory Study on rTMS for Migraine Under the Guidance of MEG
ClinicalTrials.gov study NCT06796725. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
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.