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9,300 results for “detection”
Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review
<p>This database contains all the bibliographic information about the 8673 records found after applying the Search Strategy used for the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Such strategy consisted on using seven initial keywords and similar terms of interest (namely: bridge and bridges, etc.): </p> <ul> <li>Bridge.</li> <li>Digital twin.</li> <li>Bridge information modelling.</li> <li>Finite elements.</li> <li>Bridge health monitoring.</li> <li>Anomaly detection algorithm.</li> <li>Cultural heritage.</li> </ul> <p>Six initial queries were done combining the first keyword with the rest of them:</p> <ul> <li>bridge* AND "digital twin*"</li> <li>bridge* AND (BrIM OR "bridge information model*")</li> <li>bridge* AND (FEM OR FEA OR "finite element method*" OR "finite element analy*")</li> <li>bridge* AND ("bridge health monitoring" OR "structural health monitoring")</li> <li>bridge* AND (ADA OR "anomaly detection algorithm*")</li> <li>bridge* AND ("cultural heritage" OR "monument* bridge*" OR "old bridge*" OR "ancient bridge*" OR "historic* bridge*")</li> </ul> <p>As a first screening step, the combination of these 6 initial searches was done to obtain relevant works containing at least three of the main keywords of interest:</p> <ul> <li>#1 AND #2</li> <li>#1 AND #3</li> <li>#1 AND #4</li> <li>#1 AND #5</li> <li>#1 AND #6</li> <li>#2 AND #3</li> <li>#2 AND #4</li> <li>#2 AND #5</li> <li>#2 AND #6</li> <li>#3 AND #4</li> <li>#3 AND #5</li> <li>#3 AND #6</li> <li>#4 AND #5</li> <li>#4 AND #6</li> <li>#5 AND #6</li> </ul> <p>All records found in Scopus where downloaded both in .ris and .csv format and are included in this database. The search was conducted on 10/12/2022.</p> <p>Note: Searches 10, 14, 17 and 21 did not return any records.</p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
ICELEARNING - Detection of ice core particles via deep neural networks
<p>This dataset refers to the ICELEARNING project - Detection of ice core particles via deep neural networks, by Maffezzoli N. et al., <em>The Cryosphere</em>, 10.5194/tc-17-539-2023, 2023.</p> <p>The main folder contains all TRAINING data. </p> <p>The TEST data are contained in the folder /test. </p> <p>Please refer to the <a href="https://github.com/nmaffe/icelearning">icelearning GitHub</a> repository for instructions. </p>
Small Object Aerial Person Detection Dataset
<p><strong>Small Object Aerial Person Detection Dataset:</strong></p> <p>The aerial dataset publication comprises a collection of frames captured from unmanned aerial vehicles (UAVs) during flights over the University of Cyprus campus and Civil Defense exercises. The dataset is primarily intended for people detection, with a focus on detecting small objects due to the top-view perspective of the images. The dataset includes annotations generated in popular formats such as YOLO, COCO, and VOC, making it highly versatile and accessible for a wide range of applications. Overall, this aerial dataset publication represents a valuable resource for researchers and practitioners working in the field of computer vision and machine learning, particularly those focused on people detection and related applications.</p> <p> </p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>People</td> </tr> <tr> <td>Training</td> <td>2092</td> <td>40687</td> </tr> <tr> <td>Validation</td> <td>523</td> <td>10589</td> </tr> <tr> <td>Testing</td> <td>521</td> <td>10432</td> </tr> </tbody> </table> <p> </p> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p>
Aerial Power Infrastructure Detection Dataset
<p><strong>Aerial Power Infrastructure Detection Dataset:</strong></p> <p>Autonomous inspection of power networks with Unmanned Aerial Vehicles (UAVs) has recently gained significant scientific attention mainly due to rapid advances in UAV technology. In this context, the UAV must autonomously navigate across the network acquiring high resolution data in a safe and fast manner, which poses challenges especially in cases where the location of infrastructure components, i.e. poles, is not known. The Aerial Power Infrastructure Detection Dataset is constructed aiming to create a repository available to the research community, which can be used for training online detection models, which in turn facilitate UAV localization across the network.</p> <p>Specifically, this dataset is used for implementing the “Pole Detection” process of ICARUS toolkit, which is a vision-based UAV monitoring platform for autonomous inspection of Medium Voltage (MV) power distribution network. Specifically, the UAV is supplied with the best-known coordinates of poles and navigates to the designated location searching for the pole. As soon as the pole is detected, using an one-class detection model, the UAV applies a control procedure to correct its position by aligning directly above the pole. For the training, we used samples containing the T-shaped bar of the pole with the insulators and the top of pole [1].</p> <p>The dataset consists of top-view images of MV poles from various locations across Cyprus. Images were captured across different seasons to account for a variety of background conditions, such as grass or ground, as well as at different heights to account for variations in the UAV’s height during inspection. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (t-bars):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>T-Bars</td> </tr> <tr> <td>Training</td> <td>10760</td> <td>10012</td> </tr> <tr> <td>Validation</td> <td>2587</td> <td>2370</td> </tr> <tr> <td>Testing</td> <td>1572</td> <td>1449</td> </tr> </tbody> </table> <p> </p> <p>Reference:</p> <p>[1] A. Savva et al., "ICARUS: Automatic Autonomous Power Infrastructure Inspection with UAVs," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 918-926, doi: 10.1109/ICUAS51884.2021.9476742.</p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong></p> <blockquote> <p>Antonis Savva, Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2023). Aerial Power Infrastructure Detection Dataset (2.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7781388</p> </blockquote>
Data and model for detecting spam activity on academic articles
<p>With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. This paper analyzes how Twitter bots interact with scholarly articles on the platform. Spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world. In this paper, we determined whether bots are disseminating a given scholarly article based on analyzing the relationship between Twitter bots and several research factors. We developed and tested several supervised machine-learning classification models to tackle this problem. Through our analysis, we also identified that scholarly articles in health and human science are more prone to bot activity than other research areas.</p>
Detecting small changes in tropical forests from space... data and code for thesis chapter 4
<p>SAR and UAV-LiDAR data used in chapter 4 of my thesis <em>Detecting small changes in tropical forests from space: experiments using synthetic aperture radar. </em>This content has also been submitted for peer review in Frontiers in Remote Sensing.</p> <p>DEM_timeseries_3m contains phase height and coherence from TanDEM-X InSAR high-resolution spolight images, processed by Jose-Luis Bueso-Bello at DLR. NetCDF format, dimensions latitude, longitude, time.</p> <p>TDX_descending_intensity contains intensity from the same TanDEM-X time series, covering an area of the Madre de Dios region in Peru. These data were processed by Harry Carstairs using ESA's SNAP software.</p> <p>UAV_change_1m_mask is a raster showing the change in canopy height at the study site between June 2019 and July 2021, according to two UAV LiDAR campaigns, with 1m pixels, and with areas with low point density masked out.</p> <p>CODE.zip contains python scripts and notebooks used to collate the data, create change detection metrics, develop SAR models of canopy height, and produce the figures.</p> <p>Funded by European Research Council (ERC) grant to the Tropical Forest Degradation Experiment (FODEX).</p>
Wikipedia Multilingual Vandalism Detection Dataset
<p>This dataset accompanies a research paper that introduces a novel system designed to support the Wikipedia community in combating vandalism on the platform. The dataset has been prepared to enhance the accuracy and efficiency of Wikipedia patrolling in multiple languages.</p> <p>The release of this comprehensive dataset aims to encourage further research and development in vandalism detection techniques, fostering a safer and more inclusive environment for the Wikipedia community. Researchers and practitioners can utilize this dataset to train and validate their models for vandalism detection and contribute to improving online platforms' content moderation strategies.</p> <p><strong>Dataset Details:</strong></p> <ul> <li><strong>Number of Languages:</strong> 47</li> <li><strong>Observation period: </strong>6 months training, one week hold-out testing</li> <li><strong>Use Case:</strong> The dataset is primarily intended for training and evaluating vandalism detection systems.</li> <li><strong>Features:</strong> Each record characterizes the corresponding revision of the Wikipedia page, including revision metadata, user details, text inserted, removed, or changed, and corresponding MLMs-based features. </li> <li><strong>Data Filtering and Feature Engineering:</strong> Advanced filtering and feature engineering techniques were applied to ensure the dataset's quality and relevance for effectively training the vandalism detection system.</li> <li><strong>Files: </strong>Training and hold-out testing datasets of anonymous and all users. </li> </ul> <p> </p> <p><strong>Related paper citation:</strong></p> <pre><code>@inproceedings{10.1145/3580305.3599823, author = {Trokhymovych, Mykola and Aslam, Muniza and Chou, Ai-Jou and Baeza-Yates, Ricardo and Saez-Trumper, Diego}, title = {Fair Multilingual Vandalism Detection System for Wikipedia}, year = {2023}, isbn = {9798400701030}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3580305.3599823}, doi = {10.1145/3580305.3599823}, abstract = {This paper presents a novel design of the system aimed at supporting the Wikipedia community in addressing vandalism on the platform. To achieve this, we collected a massive dataset of 47 languages, and applied advanced filtering and feature engineering techniques, including multilingual masked language modeling to build the training dataset from human-generated data. The performance of the system was evaluated through comparison with the one used in production in Wikipedia, known as ORES. Our research results in a significant increase in the number of languages covered, making Wikipedia patrolling more efficient to a wider range of communities. Furthermore, our model outperforms ORES, ensuring that the results provided are not only more accurate but also less biased against certain groups of contributors.}, booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, pages = {4981–4990}, numpages = {10}, location = {Long Beach, CA, USA}, series = {KDD '23} }</code></pre> <p> </p>
Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN
<p>This repository contains the data released in the paper 'Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN' <em>(DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>).</em></p> <p>We release a detailed catalogue of Giant Star-forming Clumps (GSFCs), detected for the full set of Galaxy Zoo: Clump Scout galaxies observed by SDSS using the Faster R-CNN architecture with the Zoobot classification-CNN as a feature extraction backbone.</p> <p>The final models and code are made publicly available via Github: <a href="https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout">https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout</a>.</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>) when using the data in this repository.</p> <p>The csv-file <em>FRCNN_Zoobot_SDSS_GZCS_detections.csv</em> has the following columns. Alternatively, the file <em>FRCNN_Zoobot_SDSS_GZCS_detections.gzip</em> contains the same data but stored as a parquet-file.</p> <table> <tbody><tr> <th>Column name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>specobjid</td> <td>SDSS spec object ID</td> </tr> <tr> <td>dr7objid</td> <td>SDSS DR7 object ID</td> </tr> <tr> <td>clump_id</td> <td>Clump index</td> </tr> <tr> <td>clump_label_id</td> <td>Clump label ID (1 or 2)</td> </tr> <tr> <td>clump_label_name</td> <td>Clump label name</td> </tr> <tr> <td>clump_score</td> <td>Detection score for the clump</td> </tr> <tr> <td>clump_centre_ra</td> <td>Clump centroid RA in degrees</td> </tr> <tr> <td>clump_centre_dec</td> <td>Clump centroid dec in degrees</td> </tr> <tr> <td>clump_flux_u</td> <td>Clump u-band flux in Jy</td> </tr> <tr> <td>clump_flux_g</td> <td>Clump g-band flux in Jy</td> </tr> <tr> <td>clump_flux_r</td> <td>Clump r-band flux in Jy</td> </tr> <tr> <td>clump_flux_i</td> <td>Clump i-band flux in Jy</td> </tr> <tr> <td>clump_flux_z</td> <td>Clump z-band flux in Jy</td> </tr> <tr> <td>clump_flux_err_u</td> <td>Clump u-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_g</td> <td>Clump g-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_r</td> <td>Clump r-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_i</td> <td>Clump i-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_z</td> <td>Clump z-band flux error in Jy</td> </tr> <tr> <td>clump_mag_u</td> <td>Clump u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_g</td> <td>Clump g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_r</td> <td>Clump r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_i</td> <td>Clump i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_z</td> <td>Clump z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_ext_mag_u</td> <td>Clump u-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_g</td> <td>Clump g-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_r</td> <td>Clump r-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_i</td> <td>Clump i-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_z</td> <td>Clump z-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u</td> <td>Clump corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_g</td> <td>Clump corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_r</td> <td>Clump corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_i</td> <td>Clump corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_z</td> <td>Clump corrected z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u_g</td> <td>Clump colour (u-g)</td> </tr> <tr> <td>clump_mag_corr_g_r</td> <td>Clump colour (g-r)</td> </tr> <tr> <td>clump_mag_corr_r_i</td> <td>Clump colour (r-i)</td> </tr> <tr> <td>clump_mag_corr_i_z</td> <td>Clump colour (i-z)</td> </tr> <tr> <td>clump_flux_ratio</td> <td>Est. clump/galaxy near-UV flux ratio (u-band)</td> </tr> <tr> <td>is_clump_3pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >3%</td> </tr> <tr> <td>is_clump_8pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >8%</td> </tr> <tr> <td>galaxy_ra</td> <td>Host galaxy RA in degrees</td> </tr> <tr> <td>galaxy_dec</td> <td>Host galaxy dec in degrees</td> </tr> <tr> <td>galaxy_z</td> <td>Host galaxy redshift</td> </tr> <tr> <td>galaxy_mag_u</td> <td>Host galaxy u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_g</td> <td>Host galaxy g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_r</td> <td>Host galaxy r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_i</td> <td>Host galaxy i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_z</td> <td>Host galaxy z-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_u</td> <td>Host galaxy u-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_g</td> <td>Host galaxy g-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_r</td> <td>Host galaxy r-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_i</td> <td>Host galaxy i-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_z</td> <td>Host galaxy z-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_flux_u</td> <td>Host galaxy u-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_g</td> <td>Host galaxy g-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_r</td> <td>Host galaxy r-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_i</td> <td>Host galaxy i-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_z</td> <td>Host galaxy z-band flux in Jy</td> </tr> <tr> <td>galaxy_expAB_r</td> <td>Host galaxy axis ratio from SDSS</td> </tr> <tr> <td>galaxy_expRad_r</td> <td>Host galaxy exponential fit scale radius from SDSS</td> </tr> <tr> <td>galaxy_lmass</td> <td>Host galaxy log mass in MSun</td> </tr> <tr> <td>galaxy_lssfr</td> <td>Host galaxy log specific SFR</td> </tr> <tr> <td>galaxy_mag_corr_u</td> <td>Host galaxy corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_g</td> <td>Host galaxy corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_r</td> <td>Host galaxy corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_i</td> <td>Host galaxy corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_z</td> <td>Host galaxy corrected z-band magnitude (AB-mag)</td> </tr> </tbody> </table> <p> </p>
Workflow for detecting biomedical articles with openly available underlying datasets - Datasets and extraction forms
<p>The open data screening datasets contain both automatically detected (TRUE) Open Data statements by <a href="https://github.com/quest-bih/oddpub">ODDPub</a>, and its manual validation using <a href="https://github.com/bgcarlisle/Numbat">Numbat</a> extraction tool. Furthermore, extraction forms for both screenings – 2020 and 2021 – are included. The manually processed dataset for the calculation of the inter-rater reliability of manual validation can be also found here. </p> <p>(i) Data from articles published in 2020 (file ‘<em>charite_open_data_2020.csv</em>’) have been collected applying a slightly different sequence of questions in the extraction workflow than the articles published in 2021 (file ‘<em>charite_open_data_2021.csv</em>’). Both datasets were cleaned for any personal data or internal comments. Thus, they do not contain the default columns which in the raw export from Numbat contained commentaries regarding different question. Also, in another regard these files do not represent raw outputs of the Numbat extraction tool, but a processed version. This means that articles validated by more than two raters were first reconciled in Numbat, resulting in one final decision (output of extractions <strong>after reconciliation</strong>). Then from the output of extractions <strong>before reconciliation</strong> those articles validated by only 1 rater (and thus not part of the inter-rater reliability calculation) were selected, which were afterwards joined with the already reconciled dataset. </p> <p>The actual decision about Openness of validated dataset can be analysed in various ways: </p> <ol> <li>Column ‘<em>open_data_assessment</em>’/’<em>assessment</em>’ shows a binary decision between Open Data TRUE and FALSE. </li> <li>If that column indicates ‘<em>NULL</em>’, the dataset was classified into ‘non’-open category, and the result can be found on one of the following ways: <ul> <li>Column ‘<em>reference_to_data</em>’ as ‘<em>n_a</em>’ for excluded articles, e.g. not producing any data.</li> <li>Column ‘<em>data_access</em>’ as ‘<em>restricted</em>’. </li> <li>Column ‘<em>own_or_reuse_data</em>’ as ‘<em>open_data_reuse</em>’. </li> </ul> </li> </ol> <p>The original extraction form contains an option ‘unsure_open_data’ besides ‘<em>open_data</em>’/’<em>no_open_data</em>’ which was resolved either during reconciliation between multiple raters or by case-related consultation with a second rater in case of doubt, and is not included here. </p> <p>(ii) The inter-rater reliability calculation was made on randomly selected 100 articles for 2 raters. The third rater screened 20 articles sample, which is part of 100 sample. The tables provided here include both article-level data, and dataset-level data. </p> <p>(iii) The Numbat extarction forms used for the screenings in 2020 and 2021 are included in two formats - JSON and Markdown.</p> <p>(iv) ‘<em>data_dictionary_open_data.csv</em>’ table documents all variables of each data file containing here. </p>
Data and Models from the study entitled, "Large-area automatic detection of shoreline stranded marine debris using deep learning"
<p>This repository contains data and models used in the study entitled, "Large-area automatic detection of shoreline stranded marine debris using deep learning". This study can be accessed as an open access publication at the following location: https://doi.org/10.1016/j.jag.2023.103515.</p> <p>The data set is comprised of 1,587 images (512 pixels x 512 pixels) which contains 10,703 individual bounding box labels of marine debris objects. The imagery was collected over the State of Hawai'i in 2015 at 2 centimeter resolution (ground spacing distance).</p> <p>The classification scheme consists of 8 labeled classes: unidentified object, processed wood, metal, vessel, net/cloth, buoy, tire, and line fragments.</p>
Protist Dispersal Detection: University of Michigan Biological Station, July 2024
This dataset contains the results of a field dispersal array assembled in Gates Bog, Pellston, Michigan. The data were collected by a graduate student, and consist of measurements of protist presence or absence in 1mL fluid samples taken from pitcher plants and centrifuge tubes in the array. The dataset contains both initial protist detection from the fluid samples, as well as detection after a 24 hour incubation period. The dataset also contains the positions of each plant and tube used for sample collection and their distances from the established source population at the center of the array. We used the purple pitcher plant, Sarracenia purpurea, as a model system to explore questions of specialist protist dispersal. Newly opened pitchers are sterile, providing virgin habitat open to community assembly of highly specialized protist species (Peterson 2008). The placement of a known community of protists at the center of an uncolonized array of habitat patches allows us to identify both sources and destinations of dispersing microbes in the array. The purpose of this study is to measure dispersal rates for a subset of pitcher plant protist species.
Wood to Soil 0-10 cm data and Wood to Soil 10-20 cm data to detect the imprint of decaying logs (30-80 cm diameter) from two hurricane cohorts (Hugo, 1989, and Georges, 1998)
Many trees fell during Hurricanes Hugo (1989) and Georges (1998) in Puerto Rico. A debris removal experiment suggested that coarse woody hurricane debris slowed canopy recovery by fueling microbial nitrogen immobilization. We analyzed C, N, microbial biomass C and root length in paired soil samples taken under versus 20-50 cm away from large trunks of two species felled by Hugo and Georges three times during wet and dry seasons during the two years after Georges. Data on soil P and other nutrients have not yet been analyzed. Soil microbial biomass, C and N were higher under than near logs of both age cohorts. Frass from wood boring beetles may induce the early effects. Root length was greater under logs at 0-10 cm depth during the dry season, and away from logs in the wet season, but varied independently of microbial biomass. Thus decaying wood can provide resources exploited by tree roots. Percent soil C and N were significantly higher under than near logs in both the 0-10 and 10-20 cm samples. Microbial biomass C varied significantly among seasons at 0-10 cm depth but differences between positions (under vs away) were only suggestive. Surface soil on the upslope side of the logs had significantly more N and microbial biomass, likely from accumulation of leaf litter above the logs on steep slopes. This study shows that C and N accumulate significantly more in soil under than near decaying logs, even in logs that had only decayed for 7 months, and thus contributes to soil heterogeneity. Tree roots track and exploit resource and nutrient hotspots as they change locations between seasons, so the soil heterogeneity in soil fertility is important for forest productivity. Soil phosphorus (P) availability is most often the most limiting nutrient in wet tropical forests. Total soil P was measured by complete digestion in samples from the upper 10 cm; Olsen extractable P (available) was also measured. Total soil P concentrations were significantly greater under than away fr
Datasets with and without deliberate head movements for detection and imputation of dropout in diffusion MRI
Open the record for dataset details and reuse information.
Figure data for "Detection of tar brown carbon with the single particle soot photometer (SP2)"
<p>Data contained in Figures 2 and 4 of Corbin and Gysel-Beer 2019. https://doi.org/10.5194/acp-2019-568</p>
S63 | UBADWGW | REACH Registered Substances Detected in Drinking (DW) or Groundwater (GW)
<p><strong>REACH Registered Substances Detected in Drinking (DW) or Groundwater (GW)</strong></p> <p>This is a literature review and a subsequent persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) assessment published by the German Environment Agency (UBA) in 2019. All substances are reported in literature to have been detected in drinking water and groundwater. This list contains only those substance that are registered under REACH (EC No 1907/2006). It is assessed whether a substance meets the PMT/vPvM criteria as proposed by the German Environment Agency (UBA) in 2019 <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">(UBA TEXTE 126/2019)</a>.</p> <p><em>Reference</em>: Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804, 131 pages.</p> <p><em>Acknowledgement:</em> Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG</p> <p>This is the collection associated with list S63 UBADWGW on the <a href="https://www.norman-network.com/nds/SLE/">NORMAN Suspect List Exchange</a>.</p>
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
Seals from the Staatsbibliothek zu Berlin and their automated detection
<p>This repository is a first step towards the compilation of an Islamic seals database comprising the following components:</p> <p>- digital photos of pages with seals, retraceable to the original artifact.</p> <p>- cut-outs of seals.</p> <p>- scripts for aggregating these resources.</p> <p>- scripts for automatically identifying similar/same seals.</p> <p>This repository took the collection at the StaBi as a first case. As such, this repository can be used as a data set for training purposes.</p> <p><strong>Scripts for seal detection</strong></p> <p>This is very basic still, and the examples show how it can be build out in different directions. The seal was actually taken from an entirely different collection. It seems the seal itself is not in this collection (or not in this orientation?) but clearly it can already function somewhat as an archetype to catch any stamps. Note that it only highlights the most likely candidate on a page, hence its singling out of only one seal.</p> <p><strong>Images from the Staatsbibliothek zu Berlin</strong></p> <p>These images were extracted from the Staatsbibliothek zu Berlin digital collections website. They can be retraced to their origin as follows: the PPN number is an identification for the object. The following number identifies the page.</p> <p>For a direct verification of the image, use the IIIF server by reconstructing the URL with this formula: `https://content.staatsbibliothek-berlin.de/dms/` then the PPN number, then `/full/0/` then the page number ending with `.jpg`</p> <p>The associated catalog page and manuscript viewer can be found by reconstruction the URL with this formula: `https://digital.staatsbibliothek-berlin.de/werkansicht?PPN=` followed by the PPN number.</p> <p>These images were assumed to be published by the StaBi and/or Stiftung Preußischer Kulturbesitz in the public domain per https://digital.staatsbibliothek-berlin.de/nutzungsbedingungen They have been brought together here strictly for research purposes with no further rights claimed.</p> <p><strong>Scripts for the StaBi</strong></p> <p>I used two scripts to automatically get 570 pages that supposedly contain Islamic seals. I also did some additional things to get everything working, for getting the right URLs and massaging the URLs into usable shapes.</p>
Hypernym-LIBre: A free Web-based corpus from Hypernym Detection [ Hearst Pattern extractions from Hypernym-LIBre]
<p>Hypernym-LIBre ( DOI: 10.5281/zenodo.3662204 ) is a free Web-based corpus for Hypernym detection.</p> <p>Its part-of-speech tagged and dependency annotated version is present at this: (DOI: 10.5281/zenodo.3689303)</p> <p>Here we provide the hypernym-hyponym pairs that were extracted from Hypernym-LIBre using Hearst patterns. This is to further the usage of these extractions with more techniques and methods. We also provide the counts of each pattern in a separate file.</p> <p> </p> <p>Format:</p> <p>hyponym \t hypernym</p> <p> </p> <p>Format for the counts file:</p> <p>pair \t frequency of extraction</p> <p> </p> <p>There are 2 files, one with the pairs, one with unique pair and its counts. Both total ~430MB.</p>
Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification
<p>Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.</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.