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9,302 results for “DETECT”
Data and Results for: Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise
<p>This collection of data and results contains everything needed to reproduce the results in the paper:</p> <p>Comparing Apples with Apples: Robust Detection Limits for Exoplanet \\ High-Contrast Imaging in the Presence of non-Gaussian Noise</p> <p>The <a href="/api/files/53bfc05e-f632-443a-9c07-590b9bf860e1/apples_root_dir.zip?versionId=d41f6d1e-ef44-497c-ae2a-a5b291a8c9b9">apples_root_dir.zip</a> is further needed to run the examples of the python package Applefy.</p>
Detecting frequency-dependent selection through the effects of genotype similarity on fitness components
<p>Frequency-dependent selection (FDS) is an evolutionary regime that can maintain or reduce polymorphisms. Despite the increasing availability of polymorphism data, few effective methods are available for estimating the gradient of FDS from the observed fitness components. We modeled the effects of genotype similarity on individual fitness to develop a selection gradient analysis of FDS. This modeling enabled us to estimate FDS by regressing fitness components on the genotype similarity among individuals. We detected known negative FDS on the visible polymorphism in a wild <em>Arabidopsis</em> and damselfly by applying this analysis to single-locus data. Further, we simulated genome-wide polymorphisms and fitness components to modify the single-locus analysis as a genome-wide association study (GWAS). The simulation showed that negative or positive FDS could be distinguished through the estimated effects of genotype similarity on simulated fitness. Moreover, we conducted the GWAS of the reproductive branch number in <em>Arabidopsis thaliana</em> and found that negative FDS was enriched among the top-associated polymorphisms of FDS. These results showed the potential applicability of the proposed method for FDS on both visible polymorphism and genome-wide polymorphisms. Overall, our study provides an effective method for selection gradient analysis to understand the maintenance or loss of polymorphism.</p>
HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
<p>Add citation file.</p>
FALP Radiology Reports: Annotated corpus for distant metastasis detection
<p>A critical task in oncology is extracting information related to cancer metastasis from electronic health records. Metastasis-related information is crucial for planning treatment, evaluating patient prognoses, and conducting cancer research. Unfortunately, findings of distant metastasis are written in radiology reports, often unstructured, making it difficult to extract relevant information automatically. In this study, we created a manually annotated clinical corpus using radiology reports of prostate, colorectal, and breast cancer patients. We developed a named entity recognition model to capture entities of distant metastasis. The entities were subsequently employed in automatically classifying the reports according to the presence or absence of metastasis. The NER model detected distant metastasis mentions with a weighted average F1 score performance of 0.84. Whole reports were finally classified with an F1 score of 0.92 for documents without distant metastasis (M0) and 0.90 for documents with distant metastasis (M1). These results show the model's usefulness in detecting distant metastasis entities in three different types of cancer and the consequent classification of reports.</p> <p>The manually annotated corpus (FALP Radiology Reports Corpus) and annotation guidelines are freely released to the research community.</p> <p>We are releasing the dataset in 2 formats:</p> <ol> <li>conll_files.zip: Contains the annotated corpus in IOB2 format. This corpus is separated into train, text, and development subsets.</li> <li>text_ann_files.zip: Contains the raw text files for each document along with its annotation file in Standoff format</li> </ol> <p>Annotation guidelines can be found in:</p> <p>Ricardo Ahumada, Pablo Báez, Gisselle Caamaño, Jocelyn Garay, & Inti Paredes. (2023). Annotation Guidelines for FALP radiology reports annotated corpus for distant metastasis detection (1.1). Zenodo. https://doi.org/10.5281/zenodo.7623509</p> <p>This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">http://creativecommons.org/licenses/by-nc-sa/4.0/</a>.</p>
Imagery dataset for rooftop detection and classification
<p>The dataset consists of 3617 GeoTIFF images, clipped by a buffer of 2 m around the roof outline with a mask around it, stored in four different folders by roof type: flat, gable, complex and bug. The bugs category includes all images which do not represent buildings, such as construction sites, unclear images, small parts of roofs or simply impossible to recognize with a human eye shapes.<br> The orthophoto used for preparing the dataset is in TIFF format. It was obtained in 2020 through aerial photography with an ultra-wide range digital camera (UltraCam Eаgle Mark 3). The orthophoto has the following characteristics:</p> <ul> <li>Height of flight above the terrain: 2850-3200 m;</li> <li>Longitudinal overlap: 60%;</li> <li>Transverse overlap: 30%;</li> <li>Aerial imaged area – 1961 sq. km, of which 1342 sq. km is the territory of the Metropolitan Municipality Sofia. For this project, the study area of district Lozents is 9.2 sq. km;</li> <li>Resolution: 10 cm/pix for the urban area;</li> <li>Bands: RGBA numberer of tiles (georeferenced JPG files): 39.</li> </ul> <p>Other applications of the dataset, in addition to rooftop detection and classification, are as follows::</p> <ul> <li>roof recognition model, distinguishing roofs from other urban objects such as streets, trees, cars, etc.; </li> <li>roof segmentation model;</li> <li>recognition and classification of roof elements (chimneys, skylights, dormers, terrace, antennas, solar panels etc.);</li> <li>recognition and classification of roof materials (tiles, metal, asphalt, wood, vinyl, etc.);</li> <li>roof solar potential analysis (the characteristics of the roof regarding the requirements for installation of solar panels).</li> </ul>
Data for Widespread detection of chlorine oxyacids in the Arctic atmosphere: Villum Research Station and Ny-Ålesund observations
<p>The data includes:</p> <p>1) Data for the time series of HClO3 and HClO4 together with relevant data from the Villum Research Station observations.</p> <p>2) Data for the time series of HClO3 from Ny-Ålesund observation.</p> <p>3) Data of the estimated cross-section and photolysis rate of HClO3 and HClO4.</p> <p>Data are also available from the corresponding authors upon request. </p>
name lists for 'Detecting intersectionality in NER models: A data-driven approach'
<p>Name lists used for data augmentation for testing biases (in terms of error disparities) of Name Entity Recognition in Danish NLP pipelines.</p> <p>The following lists are from <a href="https://www.dst.dk/da/Statistik/emner/borgere/navne/navne-i-hele-befolkningen">Statistics Denmark</a>:</p> <ul> <li>majority_first_names_2023_men.csv</li> <li>majority_first_names_2023_women.csv</li> <li>majority_last_names_2023.csv</li> </ul> <p>The following lists are from Eva Villarsen Meldgaard. 2005. <a href="https://nors.ku.dk/publikationer/webpublikationer/muslimske_fornavne/">Muslimske fornavne i danmark</a>. Publisher: Københavns Universitet</p> <ul> <li>minority_first_names_men.csv</li> <li>minority_first_names_men.csv</li> </ul> <p>The list majority_unisex_names.csv is retrieved from <a href="https://familieretshuset.dk/navne/navne/godkendte-fornavne">The Agency of Family Law</a> in Denmark, and the numbers are retrieved from the above lists from Statistics Denmark.</p> <p>The list minority_last_names.csv is retrieved from <a href="https://www.familyeducation.com/baby-names/surname/origin/muslim">FamilyEducation</a>.</p> <p>The list overlapping_names.csv contains first names, which both occur on the list of majority names and the list of minority names.</p>
e-MERLIN images in Kool et al. 2023 A radio-detected thermonuclear supernova from a single-degenerate progenitor with a helium star donor
<p>Fits images obtained with e-MERLIN on Nov 19 2021 and April 6-11 2022 on SN2020eyj. Results are published in Kool et al. 2023 "A radio-detected thermonuclear supernova from a single-degenerate progenitor with a helium star donor".</p> <p>Project RR12002<br> Target name: 1111+2923<br> Original observation correlation centre: 11:11:47.180000 +29.23.06.36000<br> Run code: RR12002_C_001_20211119</p> <p>Project RR13005<br> Target name: 1111+2923<br> Original observation correlation centre: 11:11:43.271400 +29.23.37.83200<br> Run codes (combined):<br> RR13005_C_001_20220406<br> RR13005_C_002_20220407<br> RR13005_C_003_20220408<br> RR13005_C_004_20220409<br> RR13005_C_005_20220410<br> RR13005_C_006_20220411</p> <p> </p>
dataset related to article: "PMCA-Based Detection of Prions in the Olfactory Mucosa of Patients With Sporadic Creutzfeldt-Jakob Disease"
<ol> <li>ESTIMATING PRION CONCENTRATION IN THE OM OF SCJD MM1, MV2 AND VV2 PATIENTS BY MEANS OF QUANTITATIVE PMCA (QPMCA)</li> <li>EVALUATION OF THE BIOCHEMICAL PROPERTIES OF PMCA GENERATED PRODUCTS</li> <li>PMCA ANALYSIS OF SCJD BRAIN HOMOGENATES</li> <li>PMCA ANALYSIS OF SCJD OLFACTORY MUCOSA</li> <li>RT-QuIC ANALYSIS OF OM AND BH OF SCJD PATIENTS AND CONTROLS</li> <li>WESTERN BLOT ANALYSIS OF BRAIN HOMOGENATES</li> <li>STATISTICAL ANALYSIS</li> </ol>
Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments
<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Dataset - Experimental study of frost detectability on planetary surfaces using multicolor photometry and polarimetry
<p>This dataset contains the polarimetric measurements performed for the paper "Experimental study of frost detectability on planetary surfaces using multicolor photometry and polarimetry" (Spadaccia et al., 2023 published on Icarus). The experimental work is aimed at measuring the changes of polarimetric and photomatric signal of a regolith when frost is deposited on top, at different temperatures and different wavelengths (450, 550, 750 nm).<br> <br> The .txt files are divided on regolith simulant (CR or MGS-1) and polarimetric phase angle (5 or 16°). The reflectance is measured at phase angle 50 or 61°. Each .txt file contains the averaged information of 5 experiments for CR at temperature T=-150°C, three experiments for MGS-1 at T=-130°C ("cold") and three experiments with MGS-1 at T=-120°C. </p> <p>The columns in the .txt files contain information on:</p> <p>#time[s] #temperatures[°C] #stdv_temperatures[°C] #Qblue #stdvQblue #Rblue #stdvRblue #Qgreen #stdvQgreen #Rgreen #stdvRgreen #Qred #stdvQred #Rred #stdvRred</p> <p>Where "Q" is the measurement of Q/I, "stdv" means standard deviation of the different experiments, "R" is the reflectance measured with the monochromatic camera, "blue", "green" and "red" are the three wavelengths of the incident light (450, 550, 750 nm). </p>
Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application - DATASET
<p>Data to support the findings reported in the journal paper</p> <p>Lomurno, E., Dui, L. G., Gatto, M., Bollettino, M., Matteucci, M., & Ferrante, S. (2023). Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. <em>Life</em>, <em>13</em>(3), 598.</p> <ul> <li>Caratterizzazione_y3b_pulito.xls: <ul> <li>Subject ID</li> <li>Age at the beginning of the study</li> <li>Handedness</li> <li>Sex</li> </ul> </li> <li>Score_BVSCO_pulito.xls: <ul> <li>Subject ID</li> <li>columns B-D: graphemes in the three tests of the BVSCO-2</li> <li>columns E-G: z-score computed on normative data for the three tests of the BVSCO-2</li> <li>median z-score: computed on columns E-G</li> <li>at risk: if median z-score is under the -2 threshold (0=not at risk, 1=at risk)</li> </ul> </li> <li>executions.mat: a matrix for each time point (y1, y2a, y2b, y3a, y3b) and game (copa_q = copy square, copia_seq = copy sequence, tunnel_q = tunnel square, tunnel_ele = tunnel word). <ul> <li>first column of all the cell arrays: subject ID</li> <li>second column for the copy games: three sub-matrices with an execution for each level (spontaneous, big, small)</li> <li>columns 2:N for the tunnel games: an average execution for each index of difficulty</li> <li>for each execution, the columns of the sub-matrices are normalized time, normalized x, normalized y and pressure</li> </ul> </li> </ul> <p> </p> <p>Copy games</p> <ul> <li>A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to 2: <ul> <li>First column: array with school and child code</li> <li>Second column: executions</li> </ul> </li> <li>For the two copy games, the data has a dimension of 3 x 1, where 3 are the modalities (spontaneous, big, small) and 1 is the data matrix.</li> <li>By accessing the data matrix we have four variables, i.e., normalized time, normalized x, normalized y and pressure.</li> </ul> <p> </p> <p>Tunnel games</p> <ul> <li>A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to index of difficulty number +1: <ul> <li>First column: array with school and child code</li> <li>From the second column onwards: executions, each column corresponds to the "typical" execution (the average) of an ID (5 for square tunnel and 8 for ele tunnel). Sub-matrices contain the data matrix with its four variables. i.e., normalized time, normalized x, normalized y and pressure.</li> </ul> </li> </ul>
Speciation and Structures in Pt Surface Sites Stabilized by N-Heterocyclic Carbene Ligands Revealed by DNP Enhanced Indirect-ly Detected 195Pt NMR Spectroscopic Signatures and Fingerprint Analysis
<p>Raw NMR data for the paper published under DOI: 10.1021/jacs.2c08300</p>
LILBID spectra and geochemical data shown in article "Detection of Phosphates Originating from Enceladus' Ocean" by Frank Postberg et al. (2023)
<p>Laser Induced Liquid Beam Ion Desorption (LILBID) mass spectra of phosphates and data of geochemical experiments, shown in article "Detection of Phosphates Originating from Enceladus’ Ocean" by Frank Postberg et al. (2023), published in Nature.</p>
Food Survey for the detection of high salt intakes
<p><em><strong>Food consumption frequency survey for detection of high salt intakes. (Foods correlated with salt intake).</strong></em></p> <p> </p>
Key triggers of adaptive genetic variability of sessile oak [Q. petraea (Matt.) Liebl.] from the Balkan refugia: outlier detection and association of SNP loci from ddRAD-seq data
<p>Knowledge on the genetic composition of <em>Quercus petraea</em> in south-eastern Europe is limited despite the species' significant role in the re-colonisation of Europe during the Holocene, and the diverse climate and physical geography of the region. Therefore, it is imperative to conduct research on adaptation in sessile oak to better understand its ecological significance in the region. While large sets of SNPs have been developed for the species, there is a continued need for smaller sets of SNPs that are highly informative about the possible adaptation to this varied landscape. By using double digest restriction site associated DNA sequencing data from our previous study, we mapped RAD-tag sequences to the <em>Quercus robur</em> reference genome and identified a set of SNPs putatively related to drought stress-response. A total of 179 individuals from eighteen natural populations at sites covering heterogeneous climatic conditions in the southeastern natural distribution range of <em>Q. petraea</em> were genotyped. The detected highly polymorphic variant sites revealed three genetic clusters with a generally low level of genetic differentiation and balanced diversity among them but showed a north–southeast gradient. Selection tests showed nine outlier SNPs positioned in different functional regions. Genotype-environment association analysis of these markers yielded a total of 53 significant associations, explaining 2.4–16.6% of the total genetic variation. Our work exemplifies that adaptation to drought may be under natural selection in the examined <em>Q. petraea</em> populations.</p>
Abell 1201: Detection of an Ultramassive Black Hole in a Strong Gravitational Lens
<p>A preprint of the paper "Abell 1201: Detection of an Ultramassive Black Hole in a Strong Gravitational Lens".</p> <p>This repository also contains lens analysis scripts, plotting routines and dynesty chains of the paper.</p>
Object detection annotations on images from the Rijksmuseum
<p>A dataset containing annotations made on images of cultural heritage digital objects from the Rijksmuseum . The annotations resulted from the application of automatic object detection techniques. This dataset was used for the evaluation of the object detection model developed by the project Saint George on a Bike. The service achieved a precision of 79.4% and a recall of 65.7% in this dataset. The dataset contains:</p> <ul> <li>Object detection annotations (SgoaB-Rijksmuseum-objectDetection.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. It contains 792 annotations on 315 images.</li> <li>Human-validated object detection annotations (SgoaB-Rijksmuseum-objectDetection-validatedSubset.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. This data dump includes only the subset of the annotations that were considered correct after human validation. It contains 506 annotations on 283 images.</li> </ul>
Figure data of "Remote Detection of a Lunar Granitic Batholith at Compton-Belkovich"
<p>These are figure data for the paper "Remote Detection of a Lunar Granitic Batholith at Compton-Belkovich"</p>
Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate
Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).
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.