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151 results for “Reference dataset”
Photon Versus Particle Therapy for Recurrent Lung Cancer; a Planning Study Based on a Reference Dataset of Patients.
ClinicalTrials.gov study NCT02029222. IPD Sharing: Not stated. Countries: 1. Publications: 0.
whole reference dataset (cell wall chip)
GEO Series GSE2105. Saccharomyces cerevisiae. 20 samples. Type: Expression profiling by array.
Reference datasets for in-flight emergency situations
<p><strong>Motivation</strong></p> <p>The data in this dataset is derived and cleaned from the full OpenSky dataset in order to illustrate in-flight emergency situations triggering the 7700 transponder code. It spans flights seen by the network's more than 2500 members between 1 January 2018 and 29 January 2020.</p> <p>The dataset complements the following publication:</p> <p>Xavier Olive, Axel Tanner, Martin Strohmeier, Matthias Schäfer, Metin Feridun, Allan Tart, Ivan Martinovic and Vincent Lenders.<br> "OpenSky Report 2020: Analysing in-flight emergencies using big data".<br> In <em>2020 IEEE/AIAA 39th Digital Avionics Systems Conference (DASC)</em><em>,</em> October 2020</p> <p><strong>License</strong></p> <p>See LICENSE.txt</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p>Most aircraft information come from the OpenSky aircraft database and have been filled with manual research from various sources on the Internet. Most information about flight plans has been automatically fetched and processed using open APIs; some manual processing was required to cross-check, correct erroneous and fill missing information.</p> <p><strong>Description of the dataset</strong></p> <p>Two files are provided in the dataset:</p> <ul> <li>one compressed parquet file with trajectory information;</li> <li>one metadata CSV file with the following features: <ul> <li><strong>flight_id</strong>: a unique identifier for each trajectory;</li> <li><strong>callsign</strong>: ICAO flight callsign information;</li> <li><strong>number</strong>: IATA flight number, when available;</li> <li><strong>icao24</strong>, <strong>registration</strong>, <strong>typecode</strong>: information about the aircraft;</li> <li><strong>origin</strong>: the origin airport for the aircraft, when available;</li> <li><strong>landing</strong>: the airport where the aircraft actually landed, when available;</li> <li><strong>destination</strong>: the intended destination airport, when available;</li> <li><strong>diverted</strong>: the diversion airport, if applicable, when available;</li> <li><strong>tweet_problem</strong>, <strong>tweet_result</strong>, <strong>tweet_fueldump</strong>: information extracted from Twitter accounts, about the nature of the issue, the consequence of the emergency and whether the aircraft is known to have dumped fuel;</li> <li><strong>avh_id</strong>, <strong>avh_problem</strong>, <strong>avh_result</strong>, <strong>avh_fueldump</strong>: information extracted from The Aviation Herald, about the nature of the issue, the consequence of the emergency and whether the aircraft is known to have dumped fuel.<br> The complete URL for each event is https://avherald.com/h?article={avh_id}&opt=1 (replace avh_id by the actual value)</li> </ul> </li> </ul> <p><strong>Examples</strong></p> <p>Additional analyses and visualisations of the data are available at the following page:<br> <<a href="https://traffic-viz.github.io/paper/squawk7700.html">https://traffic-viz.github.io/paper/squawk7700.html</a>></p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the original OpenSky paper:</p> <p>Xavier Olive, Axel Tanner, Martin Strohmeier, Matthias Schäfer, Metin Feridun, Allan Tart, Ivan Martinovic and Vincent Lenders.<br> "OpenSky Report 2020: Analysing in-flight emergencies using big data".<br> In <em>2020 IEEE/AIAA 39th Digital Avionics Systems Conference (DASC)</em><em>,</em> October 2020</p> <p>Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm.<br> "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research".<br> In<em> Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN)</em>, pages 83-94, April 2014.</p> <p>and the traffic library used to derive the data:</p> <p>Xavier Olive.<br> "traffic, a toolbox for processing and analysing air traffic data."<br> <em>Journal of Open Source Software</em> 4(39), July 2019.</p>
Benchmarking long-read RNA-sequencing technologies with LongBench: a cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches
GEO Series GSE303762. Homo sapiens. 38 samples. Type: Expression profiling by high throughput sequencing.
BirdScan Community Reference Dataset
<p>This repository contains the reference dataset of labelled (or otherwise useful) echo samples acquired with a<a href="https://swiss-birdradar.com/birdscan-mr.html"> BirdScan MR1 radar</a>. This dataset enables classification of the radar echoes acquired with a BirdScan MR1 into a number of biological groups of interests (as well as the removal of non-biological targets).</p> <p>Files and their contents:</p> <ul> <li><em>TrainingData.csv</em> and<em> TrainingData.rds</em> files:<br> These files contain the actual training dataset, with the labels and all radar-derived features. We use a .csv file as this is compatible cross-platform and programming languages. We also include an .rds file for the R users.</li> <li><em>ReclassTable.csv</em>:<br> This file contains the table that is used to sort the original ‘labelHuman’ labels into the hierarchical label system, i.e., columns ‘labelHa1’ to ‘labelHa6’.</li> <li><em>MR1_ML_Tool_Documentation_v1.2.pdf</em>:<br> This file contains a description of the features in the dataset, as well as the classification and wingbeat frequency estimation algorithms and parameter settings that are currently being used in the SBRS software that is delivered along with the BirdScan radar.</li> <li><em>Signatures.zip</em>:<br> This zip file contains a folder with the echo signature and north files associated to each of the samples in the TrainingData.csv (and .rds) file.<br> Files in the folder follow a strict directory and filenaming structure, i.e., <em>%Y_%m_%d\siteCode-echoID_sig.da</em>t and <em>%Y_%m_%d\siteCode-echoID_north.dat</em><br> For example: 2015_09_07\MOL-echo324_0000001606_sig.dat and 2015_09_07\MOL-echo324_0000001606_north.dat.</li> <li><em>license.txt</em>:<br> This file contains the license applying to the data.</li> </ul>
Validation dataset and reference code for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022.
<p>Validation dataset and reference code for <strong>Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022</strong>. </p> <p>Please refer to our website: <strong>https://vessel-wall-segmentation-2022.grand-challenge.org/</strong>.</p>
Dataset related to article "Is testis sparing surgery safe in patients with incidental small testicular lesions referring to a fertility center? A retrospective analysis reporting factors correlated to malignancy and long-term oncological outcomes "
<p>This record contains raw data related to article “Is testis sparing surgery safe in patients with incidental small testicular lesions referring to a fertility center? A retrospective analysis reporting factors correlated to malignancy and long-term oncological outcomes"</p> <p>Abstract</p> <p><strong>Purpose: </strong> To define predictors of malignancy after Testis sparing surgery (TSS) in patients referring to a fertility center with incidental small testicular lesions. Sub analyses were performed to assess predictors of Leydig cell hyperplasia and Leydig cell tumor.</p> <p><strong>Materials and methods: </strong> We performed a retrospective analysis of a single institutional database including patients treated with TSS between 2002 and 2020. All patients who underwent TSS as a first line surgical approach for incidentally detected lesions found during fertility evaluation were included.</p> <p><strong>Results: </strong> Data of 64 patients were collected. The median follow up was 58 months and no recurrences were observed. At univariable logistic regression multifocal lesions, hypervascularization, microlithiasis, age and lesion size were significantly associated with malignancy. At multivariable logistic regression lesion dimension, hypervascularization and multifocal lesions were predictors of malignancy. Lesions smaller than 5 mm proved to be benign in 96.6% of the cases (32/33). Intraoperative color of the lesion and US pattern of vascularization were predictors at multivariable logistic regression for Leydig cell hyperplasia and Leydig cell tumor.</p> <p><strong>Conclusion: </strong> Ultrasonographic characteristics and intraoperative appearance of the lesion can predict the malignant nature of small testicular lesions, guiding their surgical management in patients referring to a fertility center. Based on our experience, clinicians may safely perform TSS in carefully selected patients.</p> <p> </p>
Supplemental datasets for GeneticsMakie.jl — ancestry LD reference panels
<p>This datasets contains the LD reference panels for the following ancestry groups that can be used for the <code>GeneticsMakie.jl</code> (<a href="https://www.biorxiv.org/content/10.1101/2022.04.18.488573v1">doi: https://doi.org/10.1101/2022.04.18.488573</a>). The folder also contains the scripts that demonstrates the process of preparing the LD panels from the downloading step. The original LD panels were downloaded from the <a href="https://www.internationalgenome.org/">1000 genomes project (Phase3, GRCh37)</a> website.</p> <p>The ancestry notation is as follow:</p> <ul> <li>AFR: African</li> <li>AMR: American</li> <li>EAS: East Asian</li> <li>EUR: European</li> <li>SAS: South Asian</li> </ul> <p>Short descriptions of the scripts and the logs directory</p> <ul> <li><code>1.download_and_prep_1kg_ld_panels.sh</code>: downloads and removes unnecessary SNPs (e.g. duplicated SNPs, SNPs have long indels)</li> <li><code>2.filter_ld_panels_for_each_ancestry.ipynb</code>: prepares LD reference panels for each ancestry group with maf > 0.05 and mac > 1 using <code>Julia</code></li> <li><code>3.optional_sort_files.sh</code>: optionally, this script sorts the files by ancestry groups</li> <li><code>logs</code> directory: for each chromosome, the log file shows the number of samples and SNPs before and after the process for each ancestry group</li> </ul>
A reference airborne LiDAR dataset for forest research
<p>This repository contains the dataset presented in Parkan et al. (2018).</p> <p>Abstract:</p> <p>The benefits of Airborne Laser Scanning (ALS) to efficiently monitor and manage forests are widely accepted. Products derived from ALS have been successfully used in a range of different domains including ecosystem characterization, habitat modeling, timber volume estimation, forest fire management and territorial planning. Many of these applications are dependent on the estimation of biophysical parameters at the canopy and/or individual tree scale. These parameters are generally computed with area (stand) or object (tree) centric approaches. In particular, the development of processing chains directly or indirectly involving individual tree crown segmentation, tree species classification and allometric modeling constitute the bulk of scientific activity in the domain. However, the diversity of ALS data characteristics and non-standard error assessment procedures means that the results reported in different studies are often difficult to compare. In order to support standardization and benchmark studies, this article presents a reference ALS dataset, an error assessment framework and provides several example workflows to illustrate its potential use in forest research.</p> <p> </p>
Dataset related to article: "Full Interchangeability in Regard to Immunogenicity Between the Infliximab Reference Biologic and Biosimilars CT-P13 and SB2 in Inflammatory Bowel Disease"
<p>This record contains raw data related to article "Full Interchangeability in Regard to Immunogenicity Between the Infliximab Reference Biologic and Biosimilars CT-P13 and SB2 in Inflammatory Bowel Disease"</p> <p>Infliximab (IFX) biosimilars CT-P13 and SB2 have comparable efficacy, safety, and immunogenicity to the originator Remicade (RMC). However, concerns about cross-switching patients between the 3 brands were raised in the absence of cross reactivity data between them. We aimed to determine whether antibodies to infliximab (ATI) in inflammatory bowel disease (IBD) patients cross-react with RMC, CT-P13, and SB2.</p> <p>Methods:</p> <p>Based on previous ATI status, samples from 34 patients participating in the BIOSIM01 study (13 RMC, 9 CT-P13, and 12 switchers) were selected. Patients were treated with either RMC only, or CT-P13 only, or with RMC switched to CT-P13. Additionally, 28 IFX-naïve patients were assayed as controls. In total, 180 samples were analyzed. ATI trough levels were measured in parallel with 3 different bridging Enzyme Linked Immunosorbent Assays constructed using the 3 drugs. Spearman's coefficient and percentages of agreement were used to study the correlation between each assay.</p> <p>Results:</p> <p>In total, 76 samples out of 152 IFX-treated patient samples were ATI-positive (30 RMC, 14 CT-P13, and 32 switchers). All resulted ATI-positive when either CT-P13 or SB2 bridging assays were used. The overall percentage of agreement was 100% when compared either with CT-P13 or SB2 assays. No significant differences were found among ATI levels and coefficients (Spearman's 0.98 to 1.0, P < 0.0001).</p> <p>Conclusions:</p> <p>ATI of RMC-treated, CT-P13-treated or RMC to CT-P13 switched patients show full cross-reactivity with CT-P13 and SB2. Findings suggest that immunodominant epitopes in the reference and CT-P13 drugs are equally present in SB2. Data support full interchangeability between biosimilars in regard to immunogenicity.</p>
Dataset related to article "Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy"
<p>DATASET #1</p> <p>Il data set è composto da 174 osservazioni riferite ad un campione di n=174 pazienti.</p> <p>Le variabili prese in considerazione per lo studio del data set sono 21:</p> <ul> <li> <p>ID_Pazient: variabile quantitativa continua, indica il numero di identificazione del paziente</p> </li> <li> <p>Sex: variabile dicotomica, indica il sesso del paziente (Maschio=0, Femmina=1)</p> </li> <li> <p>Age: variabile quantitativa continua, indica l'età del paziente nel momento in cui è stata effettuata la valutazione</p> </li> <li> <p>EMG_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del soggetto di controllare il dispositivo con i propri segnali elettromiografici</p> </li> <li> <p>Force_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del paziente di controllare il dispositivo con la propria forza</p> </li> <li> <p>Month_Injury: variabile quantitativa continua, indica i mesi trascorsi dalla data in cui è avvenuto l'ictus</p> </li> <li> <p>Diagnosis: variabile dicotomica, indica la tipologia di ictus: (Ischemico=0, Emorragico =1)</p> </li> <li> <p>Hemisphere: variabile dicotomica, indica quale emisfero cerebrale è stato colpito dall'ictus (Destro=0, Sinistro=1)</p> </li> <li> <p>FM_UE: variabile quantitativa discreta, indica la misura della funzione motoria dell'arto superiore determinata somministrando la scala Fugl-Meyer Upper Extremity</p> </li> <li> <p>Sensitivity: variabile quantitativa discreta, indica la sezione per la misura della sensibilità della scala Fugl-Meyer</p> </li> <li> <p>Pain_ROM: variabile quantitativa discreta, indica la sezione per la misura di articolarità e dolore della scala Fugl-Meyer</p> </li> <li> <p>FIM: variabile quantitativa discreta, indica la misura di autonomia della persona nelle attività della vita quotidiana, determinata dalla somministrazione della scala Functional Independence Measure</p> </li> <li> <p>RPS: variabile quantitativa discreta, indica la misura della funzione di raggiungimento di un oggetto</p> </li> <li> <p>Peg_Sec: variabile quantitativa continua, indica la misura della destrezza manuale fine e coincide con il rapporto tra il numero di pioli e i secondi impiegati per inserirli in uno specifico supporto</p> </li> <li> <p>PectMaj: variabile qualitativa ordinata, indica la misura della spasticità del pettorale, secondo la Modified Ashworth Scale</p> </li> <li> <p>BicBrach: variabile qualitativa ordinata, indica la misura della spasticità del bicipite, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexCarp: variabile qualitativa ordinata, indica la misura della spasticità del flessore del carpo, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexProfDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore profondo delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexSupDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore superficiale delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>Ashworth_TOT: variabile quantitativa discreta, indica la misura totale della Modified Ashworth Scale, data dalla somma delle 5 variabili precedenti</p> </li> <li> <p>BB_par: variabile quantitativa discreta, indica la misura della destrezza manuale grossolana dell'arto paretico</p> </li> </ul>
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.