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11,177 results for “identify”
Single unit electrophysiology of optogenetically identified dopamine neurons in classical conditioning with probabilistic outcome of reward and punishment
<p>We collected single-unit data (.mat file) from optogenetically identified dopamine neurons in the lateral VTA in mice. These dopamine neurons were recorded while animals performed 4 different variation of classical conditioning tasks with different ratio of probabilistic outcomes of water reward and aversive air puff. In one task, one cue was associated with both water and puff in a probabilistic manner.</p>
Real-Time Delphi data: How to identify and interpret weak signals of change in the forest bioeconomy
<p>Data from: Mauno, T., Catelo, F., Bengston, D.N., Pykäläinen, J. & Hujala, T. 2023. How to identify and interpret weak signals of change in the forest bioeconomy. Forest Policy and Economics. <a href="https://doi.org/10.1016/j.forpol.2023.103075?_gl=1*a2eeob*_ga*NDE0MDgzOTYwLjE2OTY0ODY2MTY.*_ga_NY8L5SJPMB*MTY5NjQ5NDIxNi4yLjEuMTY5NjUwMTI0Mi4wLjAuMA..">https://doi.org/10.1016/j.forpol.2023.103075</a></p> <p>This exploratory study sought to understand how to identify and interpret weak signals of change that may have an impact on the forest bioeconomy. An international panel of experts in the forest bioeconomy and in foresight provided their views through a two-stage Real-Time Delphi method which utilized the multiple rounds of traditional Delphi and the instantaneous feedback of the Real-Time Delphi method.</p> <p>The Real-Time Delphi exercise was conducted in two stages through the eDelphi software (edelphi.org); the first stage was focused on <em>Changes</em> and the second on <em>Weak Signals</em>. For the Real-Time Delphi exercise, two expert matrices (forest bioeconomy matrix and futures & foresight matrix) were prepared with the aim of obtaining diverse expertise to participate in the study.</p> <p>The first stage was conducted during a 10-day period in early September 2022 (Sep 5, 2022 – Sep 14, 2022). The second stage was also conducted during a 10-day period later in September 2022 (Sep 21, 2022 – Sep 30, 2022). A total of 11 (6 forest bioeconomy experts and 5 futures research and foresight specialists) participated in both eDelphi stages.</p> <p>This dataset includes: (i) completed Real-Time Delphi stages (including anonymized answers and discussions of 11 participants) (ii) questions for both Real-Time Delphi stages, and (iii) anonymized expert matrices (forest bioeconomy matrix and futures & foresight matrix)</p> <p>For more information, please contact the corresponding author (data controller): Tuomas Mauno (University of Eastern Finland), tuomas.mauno@uef.fi</p>
Data from: Single-cell analysis identifies conserved features of immune dysfunction in simulated microgravity and spaceflight
<p>3-dimensional super-resolution microscopy volumes of human PBMCs recorded on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope.</p> <p>Sample preparation and image capture:</p> <p>Live PBMCs were stained with 60 nM MitoTracker Red-CMX-Ros (ThermoFisher, Waltham, MA) either in 6-well plates or in the microgravity chambers for the last 2 hr of the microgravity simulation. At the end of the microgravity simulation cells were immediately fixed by 1:1 mixing the cell suspensions with 2× concentrated fixative (10% Sucrose (w/v) 120 mM KCl, 1% (w/v) glutaraldehyde, 8% (w/v) PFA pH 7.4) and incubated for 15 minutes at room temperature followed by 15 minutes on ice. Fixed cells were washed and stored in PBS until further staining for up to a week at 4 °C. 1 million fixed cells were resuspended in 1 mL of permeabilization solution (0.1% TritonX-100 in PBS) for 5 minutes. After twice washing in PBS, pellets were resuspended in 0.5 mL 1% BSA PBS containing Phalloidin-iFluor-488 (cat# ab176753, Abcam plc., Cambridge, UK) at the manufacturer’s recommended dilution, and were incubated for 90 minutes with gentle agitation. After washing in PBS, cells were stained with Hoechst 33342 (1 µg/mL in PBS) for 10 minutes. The fixed-stained cells were immobilized at 3 × 10<sup>5</sup> cells per well density in glass-bottom 96-well microplates (Greiner Bio-One, Monroe, NC), which were pre-coated with polyethyleneimine (1:15,000 (w/v)) for 16 hours in a 37 °C incubator, and washed twice with PBS. Microplates with the cell suspensions were centrifuged in a swing plate rotor centrifuge (Eppendorf 5810 R) at 400 × <em>g</em> and for 10 min and then fixed on the surface by adding an equal volume of 8% (w/v) PFA for 5 min. Finally, the fixative was replaced with 100 µL of antifade reagent (Vector Prolong Gold (ThermoFisher)). Samples were imaged immediately after this procedure on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope (Carl Zeiss Microscopy, White Plains, NY). Single PBMCs were manually selected for recording based on low-resolution preview scans showing only nuclei. All singlet cells were selected in a small neighborhood to avoid biases. In each microscopy session, 24-40 cells were selected for recording in one well for each condition. This was performed in an interleaved manner, capturing 6-8 cells at a time, and then moving to the next well and then repeating this multiple times using the Experiment Designer module for automation. Super-resolution volumes of (358 × 358 × 70 pixels, 0.035 × 0.035 × 0.13 µm/voxel resolution) were recorded in the above-determined positions using Definite Focus autofocusing. A Plan-Apochromat 63 × 1.40 Oil lens, Airyscan2 SR (super-resolution) mode with optimal sampling and frame switching between 3 fluorescence channels to minimize spectral cross-bleed were used. MitoTracker Red, iFluor488, and Hoechs33342 were excited with 561, 488, and 405 nm solid-state lasers, respectively, using the optimal emission filter for each channel. 3D Airyscan2 processing was performed with standard filtering settings.</p> <p>File naming:</p> <p>Four zip files were deposited named as <Donor#id>.zip, where id goes from 1 to 4.</p> <p>Each zip file contains the following Zeiss Microscopy format image files: <Condition>_<Donor#id>_<Stain#batch>_<Cell>.czi</p> <p><Condition>:</p> <ul> <li>1G – Control culturing in 6-well plates for 25h</li> <li>uG – simulated microgravity culturing for 25h in NASA Rotating Wall Vessels</li> <li>1G+TLR – as above, with TLR 7/8 agonist (1 μM R848)</li> <li>uG+TLR– as above, with TLR 7/8 agonist (1 μM R848)</li> <li>1G+CyD– as above, with cytochalasin D</li> <li>uG+CyD– as above, with cytochalasin D</li> <li>1G+Q – as above, with quercetin 50µM</li> <li>uG+Q – as above, with quercetin 50µM</li> </ul> <p><Donor#id>: 1-4 indicates biological replicates</p> <p><Stain#batch>: 1-2 indicates experimental replicates of phalloidin staining and imaging session</p> <p><Cell>: arbitrary number to distinguish images within the same condition/donor/stain set.</p> <p>See image analysis pipelines used with these data at: https://github.com/gerencserlab/Superresolution-actin-and-mitochondria-analysis</p>
Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds (datasets)
<p>Datafiles used to reproduce the results in the "Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds" publication. Contains input files as well as files generated by the algorithm. Details can be found in the manuscript.</p> <p>SARSCoV19_mProFiles.zip : PDB formatted inputs and top model for SARS-CoV2 mPro.</p> <p>ScaffoldMatcherBenchmarkFiles_20230410.zip : PDB formatted benchmark inputs and top models.</p>
Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials
<p><strong>Our paper has been published on Phys. Rev. Res. (doi: 10.1103/PhysRevResearch.6.013259)</strong></p> <p><strong>Our preprint paper is also avilable at arXiv(https://arxiv.org/abs/2310.06406), here is the abstract of our paper:</strong></p> <p>Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of ±1/2 defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay–Berne particles. MED performs faster than conventional defect detection method and can achieve over 90% accuracy on recognizing ±1/2 defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED can provide correct structural information about ±1 defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.</p> <p> </p> <p><strong>Repository Organization</strong></p> <p><strong>Trained Models.zip</strong></p> <p>This directory is integral for model deployment and houses all relevant pre-trained models.</p> <ul> <li><strong>plus_vit_vecUV.pt</strong>: Pre-trained model for the Plus Transformer variant.</li> <li><strong>minus_vit_theR.pt</strong>: Pre-trained model for the Minus Transformer variant.</li> <li><strong>nanodet-plus-m_416-halfenhance</strong>: A sub-directory containing all files associated with the trained Nanodet-Plus model.</li> <li><strong>configs</strong>: Configuration files for training procedures.</li> </ul> <p><strong>Training Data.zip</strong></p> <p>This directory contains all datasets used for the training of Nanodet-Plus, Plus Transformer, and Minus Transformer models.</p> <p><strong>Code.zip</strong></p> <p>This directory features the implementation details and example use-cases showcased in Figure 2 and Figure 3c of our associated paper. The directory also includes code corresponding to the specific versions of Nanodet-Plus and SAM models cited in our study.</p> <ul> <li><strong>nanodet</strong>: Code in this folder is adapted from <a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet). We have included the exact version used for compatibility.</li> <li><strong>segment_anything</strong>: Code sourced from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything). The specific version used is included for compatibility.</li> <li><strong>Fig2</strong>: Code for predicting topological defects in tissue cell images, citing the following reference: T. B. Saw et al., Nature 544, 212 (2017).</li> <li><strong>Fig3c</strong>: Code for predicting topological defects in microtubules images, citing the following reference: M. Golden et al., Sci. Adv. 9, eabq6120 (2023).</li> </ul> <p><strong>Initialization Steps</strong></p> <p>Before executing any code, please ensure the following:</p> <ul> <li>All files in the <strong>Trained Models</strong> directory must be available.</li> <li>Download the checkpoint <strong>sam_vit_l_0b3195.pth</strong> from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a>. (https://github.com/facebookresearch/segment-anything)</li> </ul> <p><strong>Acknowledgments</strong></p> <ul> <li><a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet)</li> <li><a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything)</li> </ul> <p>For further inquiries or issue reporting, you may contact us via email.</p> <p><strong>Contact Information</strong>: <a href="mailto:hrenae@connect.ust.hk">hrenae@connect.ust.hk</a></p>
Parameter estimation data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is a second data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on <a href="https://arxiv.org/abs/2306.12908">https://arxiv.org/abs/2306.12908</a>.</p> <p>This data release contains posterior samples and configuration files for parameter estimation runs performed for the paper. For the lensing hypothesis tests results see the other <a href="https://doi.org/10.5281/zenodo.8409635">data release</a> for the same paper.</p> <p>For each event that we have run parameter estimation for, we provide a tar.gz file that includes the configurations, the priors and the results for each run, typically with several different waveforms. All runs were performed with <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">parallel bilby</a> with different versions as described in section 6.1 of the paper. The input data is available from <a href="https://gwosc.org/">GWOSC</a>.</p> <p>The results for IMRPhenom* waveforms (runs with parallel bilby version 1.0.1) are in json format, while for SEOBNRv5PHM and NRSur7dq4 (run with parallel bilby version 2.0.2) some of the results are in hdf5 and others in json, depending on whether multiple runs were merged together. These should be readable with bilby or <a href="https://lscsoft.docs.ligo.org/pesummary/">pesummary</a>.</p> <p>We provide the "complete" configuration files processed by bilby. These can be used for reproducing the runs, but the prior file information needs to be added with a syntax like the following `prior-file=ProdF4.prior`. Generally most runs use the same prior file, with the labels `Prod*.prior`, taken from the LVK analyses. The priors used for the aligned-spin waveforms have the label `_AS`. For the NRSur7dq4 waveform we use a restricted prior with the label `_NR_Sur_constrMtot`. For GW190527 we also use a different prior for IMRPhenomXP and IMRPhenomTPHM, which has the label `_restricted`.</p> <p>Also note that for the result file GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_merged_result.hdf5 this was merged from the results obtained with the config GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_config_complete.ini together with the fourth chain of identical configuration.</p>
Mentoring Up De-Identified Dataset
<p>We provide here the de-identified data used to evaluate the implementations of a Mentoring Up training. This dataset is currently being used in the publishing of a manuscript, "<i>Mentoring Up for Early Career Investigators</i>: Empowering Mentees to Proactively Engage in their Mentoring Relationships"</p><p>Variables include,</p><p>Values for average self-ratings of Mentoring Competencies using the MCA scale (Likert-type scale from 1 - 7), retrospectively thinking to BEFORE the training and AFTER the training. Subscales include:</p><ul><li>Effective Communication</li><li>Aligning Expectations</li><li>Assessing Understanding</li><li>Fostering Independence</li><li>Addressing Diversity</li><li>Promoting Professional Development</li></ul><p>Values also include responses to the following quantitative questions:</p><ul><li>Overall, how effective was the facilitator in guiding discussion during the workshop? (1 - Very Ineffective, 2 - Ineffective, 3 - Neither Effective nor Ineffective, 4 - Effective, 5 - Very Effective)</li><li>Was attending this workshop a valuable use of your time? (1 - Yes, 2 - No)</li><li>How likely are you to recommend this workshop to other colleagues? (1 - Very Unlikely, 2 - Unlikely, 3 - Undecided, 4 - Likely, 5 - Very Likely)</li><li>Have you made, or do you plan to make any changes in your own practices as a mentee or in your relationship with your mentor(s) as a result of this training? (1 - Yes, 2 - No)</li></ul><p>And the following short answer responses:</p><ul><li>Please describe any changes you plan to make as a result of this workshop</li><li>What aspects of this workshop did you find most useful?</li><li>What could be done to improve this workshop</li><li>What are the strengths and weaknesses of this training? Are there things you would like to see changed or added? If so, what?</li></ul><p> </p>
Survey to help identify whether Octopus might help researchers produce high quality, open research
<h2>Survey to help identify whether Octopus might help researchers produce high quality, open research</h2><p>In order to complement in-depth, but necessarily small sample size, interviews, an online survey allowed us to reach a broader population to gain some quantitative data on the current research culture and barriers to best practice, as well as whether the Octopus platform might help overcome them.</p><p>The finalised survey was implemented on EUSurvey. It is a fully open source online survey platform developed and administered by the European Commission, adhering to relevant privacy regulations (e.g. the General Data Protection Regulation (GDPR)).</p><p>The survey was open from 17 January to 5 February 2023.</p><p>Details on the implementation of this survey is published on Octopus.</p>
Acceptability of Identifying and Managing Psychological Distress in Inflammatory Bowel Disease: the COMPASS-IBD Study
ClinicalTrials.gov study NCT05330299. IPD Sharing: YES. Countries: 1. Publications: 1.
Near Infrared Autofluorescence (NIRAF) Detection for Identifying Parathyroid Glands During Parathyroidectomy
ClinicalTrials.gov study NCT05152927. IPD Sharing: NO. Countries: 1. Publications: 3.
Study to Identify and Determine Best Implementation Practices for Injectable Cabotegravir+Rilpivirine in the United States (US)
ClinicalTrials.gov study NCT04001803. IPD Sharing: YES. Countries: 1. Publications: 2.
Developing an Imaging-Based Tool to Identify Areas for Prostate Cancer Biopsy
ClinicalTrials.gov study NCT03585660. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Study to IDEntify Patients With Advanced/Metastatic Non Small Cell Lung Cancer (NSCLC) and ALK and ROS1 Translocation and to Establish Their Therapeutic Management (IDEALK&ROS)
ClinicalTrials.gov study NCT02679170. IPD Sharing: NO. Countries: 1. Publications: 1.
Assess the Prognostic Usefulness of Flutemetamol (18F) Injection for Identifying Subjects With Amnestic Mild Cognitive Impairment Who Will Convert to Clinically Probable Alzheimer's Disease
ClinicalTrials.gov study NCT01028053. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Validation of Diagnostics to Identify Glucose-6-phosphate Dehydrogenase Activity in the US
ClinicalTrials.gov study NCT04010695. IPD Sharing: NO. Countries: 1. Publications: 1.
Can HER2 Targeted PET/CT Imaging Identify Unsuspected HER2 Positive Breast Cancer Metastases, Which Are Amenable to HER2 Targeted Therapy?
ClinicalTrials.gov study NCT02286843. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Assessing Benefits of NIRAF Detection for Identifying Parathyroid Glands During Total Thyroidectomy
ClinicalTrials.gov study NCT05579782. IPD Sharing: NO. Countries: 1. Publications: 1.
Beyond EOsinophils: proteoMICS to Identify Potential Biomarker of Organ Damage and Response to MEPOLIZUMAB in EGPA
ClinicalTrials.gov study NCT07343661. IPD Sharing: NO. Countries: 1. Publications: 6.
Identifying Ideal Reimbursement "Dose" to Reduce Clinical Trial-related Financial Toxicity
ClinicalTrials.gov study NCT05871125. IPD Sharing: NO. Countries: 1. Publications: 0.
Concept Mapping as a Scalable Method for Identifying Patient-Important Outcomes
ClinicalTrials.gov study NCT02792777. 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.