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4,578 results for “Assistance”
NASA Standards and and Technical Assistance Resource Tool
The NASA Technical Standards Program provides access to NASA developed standards and handbooks. The NASA Standards and Technical Assistance Resource Tool, or START, is a Web tool sponsored by the NASA Technical Standards Program. The idea is to make it simpler for technical people to access great NASA technical information quickly and conveniently.
An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis
GEO Series GSE246557. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Genome variation profiling by high throughput sequencing.
Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE) sequencing of msh1 memory line
GEO Series GSE114664. Arabidopsis thaliana. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
SFPQ is a FTO binding protein that assists specific m6A demethylation
GEO Series GSE128443. Homo sapiens. 12 samples. Type: Methylation profiling by high throughput sequencing.
An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis [RNA-seq and ATAC-seq]
GEO Series GSE243881. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Electro-assisted cell printing directs in vitro gastrulation and epiblast-like morphogenesis from human pluripotent stem cells
GEO Series GSE243362. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
To compare the transcriptome expression of cumulus granulosa cells between PCOS patients with insulin resistance and women undergoing assisted reproduction for fallopian tube reasons
GEO Series GSE216609. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
Dll4 inhibition promotes angiogenesis and graft volume retention in cell-assisted lipotransfer
GEO Series GSE190243. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
NANOG assists in the establishment and maintenance of pluripotency by amplifying multiple signaling pathways
GEO Series GSE107505. Mus musculus. 22 samples. Type: Expression profiling by high throughput sequencing.
Mouse cortex PV interneuron transcriptome changes assessed with FACS-assisted bulk RNA-seq
GEO Series GSE262872. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Identifying METTL5-bound RNA by crosslinking-assisted immunoprecipitation
GEO Series GSE174435. Homo sapiens. 6 samples. Type: Other.
An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis [WGS]
GEO Series GSE246556. Homo sapiens. 6 samples. Type: Genome variation profiling by high throughput sequencing.
Machine learning-assisted classification of responders and non-responders to bortezomib treatment regimens PAD and VCD using new experimental dataset of 58 multiple myeloma RNA sequencing profiles
GEO Series GSE159426. Homo sapiens. 58 samples. Type: Expression profiling by high throughput sequencing.
Exonuclease assisted mapping of protein-RNA interactions (ePRINT)
GEO Series GSE266924. Homo sapiens. 4 samples. Type: Other.
Functional genomics-assisted identification of the Ypel5 gene as a novel negative regulator of the TBK1/IKBKE kinases and interferon-beta
GEO Series GSE81150. Mus musculus. 8 samples. Type: Expression profiling by array.
LLMs as Teaching Assistants? Exploring New Horizons Through pTA - Dataset
<h1>LLMs as Teaching Assistants? Exploring New Horizons Through pTA - Dataset</h1> <p> </p> <h2>The Dataset</h2> <p> </p> <div>The dataset consists of logs of tests run by students through the pTA on their submissions. The collection, named <code>commandFinished</code> is a json file made up of a list of Mongodb objects. Each object is of the following format:</div> <div> </div> <div><code>{</code></div> <div><code> "_id": {</code></div> <div><code> "$oid": "6175e2d0e0375c2f95bd25c5"</code></div> <div><code> },</code></div> <div><code> "submissionGitRepoId": "gr0-ms1",</code></div> <div><code> "category": "git clone/pull",</code></div> <div><code> "finished": {</code></div> <div><code> "stdio": "",</code></div> <div><code> "stdError": "Cloning into '.'...\n",</code></div> <div><code> "wholeInteraction": "Running the process: docker run --network host --interactive --name ms1-gr0-echo-client ms1/gr0/echo-client.jar</code></div> <div><code> Testing if put works correctly... Client 1 puts\n-------Input@KV at localhost/127.0.0.1:33121> put dog one two three\n-------Output@KV at localhost/127.0.0.1:33121> put_success dog",</code></div> <div><code> "exitValue": 0</code></div> <div><code> },</code></div> <div><code> "finishedTime": {</code></div> <div><code> "$date": {</code></div> <div><code> "$numberLong": "1635122928975"</code></div> <div><code> }</code></div> <div><code> },</code></div> <div><code> "durationmillis": 884,</code></div> <div><code> "isFinal": false</code></div> <div><code>}</code></div> <p> </p> <div>where</div> <p> </p> <div>- <code>submissionGitRepoId</code> - An ID created of the format <code><gr>-<ms></code>, where <code><gr></code> is the group id and <code><ms></code> is the milestone number</div> <div>- <code>finished</code> - The object containing details of the test</div> <p> </p> <h2>How to import</h2> <p> </p> <div>- Install <a title="MongoDB and MongoDB Compass" href="https://www.mongodb.com/">MongoDB and MongoDB Compass</a> .</div> <div>- Run a MongoDB deployment.</div> <div>- Open MongoDB Compass and connect to the deployment. Do so by entering the <code>URI</code> and clicking on <code>Connect</code>.</div> <div>- Create a new database and navigate to it.</div> <div>- Create a new collection and navigate to it.</div> <div>- On the resulting page, click on the <code>ADD DATA</code> dropdown and select <code>Import JSON or CSV file</code>.</div> <div>- Confirm the <code>commandFinished.json</code> file that you would like to import.</div> <p> </p>
Dataset for "Hydrogen-atom-assisted tautomerization on solid surfaces – A case study of thioacetamide"
<p><span>20221122_thioacetamide_amorph-blank: IR spectral files, QMS data, and lab note of the blank experiment</span></p> <p><span>20221125_thioacetamide_amorph-HABS: IR spectral files, QMS data, and lab note of the 'real' experiment</span></p>
Dataset related to article "Robotic surgery, video-assisted thoracic surgery, and open surgery for early stage lung cancer comparison of costs and outcomes at a single institute"
<p>This record contains raw data related to article "Robotic surgery, video-assisted thoracic surgery, and open surgery for early stage lung cancer comparison of costs and outcomes at a single institute"</p> <p>Background:</p> <p>Robotic surgery is increasingly used to resect lung cancer. However costs are high. We compared costs and outcomes for robotic surgery, video-assisted thoracic surgery (VATS), and open surgery, to treat non-small cell lung cancer (NSCLC).</p> <p>Methods:</p> <p>We retrospectively assessed 103 consecutive patients given lobectomy or segmentectomy for clinical stage I or II NSCLC. Three surgeons could choose VATS or open, the fourth could choose between all three techniques. Between-group differences were assessed by Fisher's exact, two-way analysis of variance (ANOVA), and Wilcoxon-Mann-Whitney test. P values <0.05 were considered significant.</p> <p>Results:</p> <p>Twenty-three patients were treated by robot, 41 by VATS, and 39 by open surgery. Age, physical status, pulmonary function, comorbidities, stage, and perioperative complications did not differ between the groups. Pathological tumor size was greater in the open than VATS and robotic groups (P=0.025). Duration of surgery was 150, 191 and 116 minutes, by robotic, VATS and open approaches, respectively (P<0.001). Significantly more lymph node stations were removed (P<0.001), and median length of stay was shorter (4, 5 and 6 days, respectively; P<0.001) in the robotic than VATS and open groups. Estimated costs were 82%, 68% and 69%, respectively, of the regional health service reimbursement for robotic, VATS and open approaches.</p> <p>Discussion:</p> <p>Robotic surgery for early lung cancer was associated with shorter stay and more extensive lymph node dissection than VATS and open surgery. Duration of surgery was shorter for robotic than VATS. Although the cost of robotic thoracic surgery is high, the hospital makes a profit.</p>
Datasets of Chatterjee et al., ACS Appl. Electron. Mater. 4, 5317 (2022), "Impact of polymer-assisted epitaxial graphene growth on various types of SiC substrates"
<p>Compilation of the datasets used to generate the figures in the following journal publication:</p> <p>"Impact of polymer-assisted epitaxial graphene growth on various types of SiC substrates"</p> <p>by Atasi Chatterjee, Mattias Kruskopf, Stefan Wundrack, Peter Hinze, Klaus Pierz, Rainer Stosch, and Hansjoerg Scherer,</p> <p>ACS Appl. Electron. Mater. 4, 5317 (2022),</p> <p>DOI: 10.1021/acsaelm.2c00989.</p>
Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "
<p>This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era"</p> <p>Abstract</p> <p><strong>Introduction: </strong> To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).</p> <p><strong>Materials and methods: </strong> After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis.</p> <p><strong>Results: </strong> A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (<em>p</em> = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA.</p> <p><strong>Conclusion: </strong> The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.</p>
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.