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14,447 results for “Identification”
In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1)
<p>Modern methods of mass spectrometry have emerged recently allowing reliable, fast and cost-effective identification of pathogenic microorganisms. For example, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has revolutionized the way pathogenic microorganisms are identified in today’s routine clinical microbiology. Furthermore, recent years have witnessed also substantial progress in the development of liquid chromatography-mass spectrometry (LC-MS) based proteomics for microbiological applications.</p> <p>In this context, we introduce a new concept for microbial identification by mass spectrometry. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against <em>in silico</em> libraries of peptide mass data. The first version of such a database has been computed from UniProt Knowledgebase [Swiss-Prot and TrEMBL] and contains more than 12,000 strain-specific synthetic mass profiles. The database is stored in the pkf data format which is interpretable by the MicrobeMS software package (requires MicrobeMS version 0.82, or later).</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089.</em></p>
Identification of emotional facial expressions in a lab and over the internet
<p>This dataset includes the data used for the analyses presented in the paper published (under the same title as this dataset) in the journal Psychology, Journal of the Higher School of Economics. </p> <p>Abstract of the publication:</p> <p>Collecting data over the internet is an approach that allows researchers to vastly expand the possible sample sizes of their studies, and enables the study of populations that may otherwise be difficult to access. However, to ensure that data collected over the internet is of the same level of quality as data collected in a lab, the comparability of internet-collected data with lab-collected data must first be assessed for individual areas of research and experimental approaches. To answer the question of whether internet data collection is suitable for experiments involving facial expressions, we conducted a deliberately difficult facial emotion-identification experiment where participants completed the same task either under supervision in our lab, or at an unsupervised location over the internet. Stimuli consisted of sad faces that participants were asked to identify as resembling either anger, fear, or disgust. Regardless of belonging to either the group tested in the lab or over the internet, participants showed highly similar response distributions, while differences between the groups were non-significant and of very low magnitude. We can therefore conclude from our findings that internet data collection is a viable method for experiments requiring the identification of emotional facial expressions, being able to produce similar results to those which can be obtained in a lab.</p>
InterFlex WP3 data set_ SGAM diagrams_interface data base_service identification
<p>This data set contains the InterFlex demonstration use case descriptions in the form of SGAM diagrams as well as the interface data base which was used for different deliverables and the repective results within work package 3 "Impact and deployment analysis of the innovative solutions". There has also been one publication in this regard ( <a href="https://doi.org/10.1109/INDIN.2018.8472053">10.1109/INDIN.2018.8472053</a>)</p> <p>Furthermore, it includes the service mappings for the InterFlex (under GA 731289) demonstrators as an input for different WP3 3.1 subtasks, deliverbale (D3.2) as well as a scientific publication (ICRERA 2019, ID 239, online ISSN: 2572-6013)</p>
Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins
<p>This data repository contains all previously unpublished raw data files for the manuscript “Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins” by Wolfgang Esser-Skala, Marius Segl, Therese Wohlschlager, Veronika Reisinger, Johann Holzmann, and Christian G. Huber. See <em>readme.md</em> for further information.</p>
Dynamics of SARS-CoV-2 spike protein in open and closed states and identification of key structural perturbations upon mutations
<p>The SARS-Cov-2 spike protein resides on the exterior surface of the coronavirus, and therefore, acts as the first point of contact that mediates cell attachment and fusion. During this process, it undergoes dramatic conformational changes upon host receptor binding. We are leveraging high-performance computing to identify these structural perturbations in wildtype and mutant spike protein models. The files contain structures from molecular dynamics simulations of closed SARS-Cov-2 spike protein embedded in POPC membrane.</p>
Adele 3D seismic survey segy format used in the FORCE 2020 machine learning competition for fault identification
<p>Adele seismic 3D survey segy format used in the FORCE 2020 machine learning competition for fault identification.</p> <p>Dataset is courtesy of GEOSCIENCE Australia who need to be acknowledged in each publication</p> <p> </p>
Trained convolutional neural network for the identification of long-duration mixed precipitation in Montréal (Canada)
<p>In this dataset the trained convolutional neural network is published that accompanies the paper "A deep learning approach for the identification of long-duration mixed precipitation in Montréal (Canada)" submitted to the special issue on "Machine-Learning Applications in the Atmospheric and Oceanic Sciences" by the journal Atmosphere&Ocean.</p> <p>The files were created using tensorflow in python. The trained network is available in .h5-format the history as numpy-file (npy).</p>
1QIsaa data collection (binarized images, feature files, and plotting scripts) for writer identification test using artificial intelligence and image-based pattern recognition techniques
<p><strong>The Great Isaiah Scroll (1QIsa<sup>a</sup>) data set for writer identification</strong></p> <p>This data set is collected for the ERC project:<br> The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br> PI: Mladen Popović<br> Grant agreement ID: 640497</p> <p>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a><br> <br> <strong>Copyright (c) </strong> University of Groningen, 2021. All rights reserved.<br> <strong>Disclaimer and copyright notice for all data contained on this .tar.gz file:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article on this data set:<br> <br> <em>Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.</em><br> <br> BibTeX:</p> <pre>@article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} }</pre> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy.</p> <p><strong>Organisation of the data:</strong></p> <p>The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions.</p> <p>The 'images' directory contains NetPBM images of the columns of 1QIsa<sup>a</sup>. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (<em>arxiv.org/abs/1911.07930</em>) system, and the other one is the manually cleaned version of the binarized output. The file names for the direct binarized output are of the format '1QIsaa_col<columnnr>.pbm', for example, '1QIsaa_col15.pbm'. And, for the cleaned version, the format is '1QIsaa_col<columnnr>_cleaned.pbm', for example, '1QIsaa_col15_cleaned.pbm'. Note: the image files are not in a separate directory; they will be extracted in the same place. However, due to the unique naming, there is no problem extracting them in one single directory.</p> <p>The 'features' directory contains feature files computed for each of the column images. There are two types of feature files: Hinge and Adjoined. They are distinguishable by their extension, for example, '1QIsaa_col15_cleaned.hinge' and '1QIsaa_col15_cleaned.adjoined'. They are also arranged in separate directories for ease of use.</p> <p>The 'plots' directory contains a simple python script to perform PCA on the feature files and then visualize them in a 3D plot. The file takes the location of feature files as an input. The 'README_plot' file contains examples of how-to-run in the terminal.</p> <p><strong>Brief description:</strong><br> According to ImageMagick's' identify' tool, the original images are in grayscale (.jpg) from Brill collection, in '8-bit Gray 256c'. These images pass through multiple preprocessing measures to become suitable for pattern recognition-based techniques. The first step in preprocessing is the image-binarization technique. In order to prevent any classification of the text-column images based on irrelevant background patterns, a specific binarization technique (BiNet) was applied, keeping the original ink traces intact. After performing the binarization, the images were cleaned further by removing the adjacent columns that partially appear on the target columns' images. Finally, few minor affine transformations and stretching corrections were performed in a restrictive manner. These corrections are also targeted for aligning the texts where the text lines get twisted due to the leather writing surface's degradation. Hence, the clean images are there in the directory along with the direct binarized images. No effort has been made to obtain a balanced set in any way.</p> <p><strong>Tools:</strong><br> <strong>Binarization:</strong><br> The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl)</p> <p><strong>Image Morphing:</strong><br> In the original article, data augmentation was performed using image morphing. The tool is available on GitHub:<br> https://github.com/GrHound/imagemorph.c</p> <p><strong>Features for writer identification:</strong><br> Lambert Schomaker<br> http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html<br> http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html<br> <em><strong>1. </strong>L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.<br> <strong>2. </strong>Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.</em><br> <br> The features (hinge, fraglets) have been combined in a single MS Windows application, GIWIS, which is available for scientific use upon request (l.r.b.schomaker(at)rug.nl)</p> <p><strong>If you have any question, please contact us:</strong><br> Maruf A. Dhali <m.a.dhali(at)rug.nl><br> Lambert Schomaker <l.r.b.schomaker(at)rug.nl><br> Mladen Popović <m.popovic(at)rug.nl></p> <p><strong>Please cite our papers if you use this data set:</strong><br> <em><strong>1.</strong> Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.<br> <strong>2. </strong>Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930.</em></p>
Agonum sordidum, Fig_6 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum sordidum,</p> <p>Fig_6 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum rugicolle, Fig_5 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum rugicolle,</p> <p>Fig_5 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum_nigrum, Fig_4 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum_nigrum,</p> <p>Fig_4 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum_mesostictum, Fig_2 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum_mesostictum,</p> <p>Fig_2 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum monachum syriacum, Fig_3 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum monachum syriacum,</p> <p>Fig_3 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Agonum_marginatum, Fig_1 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum_marginatum,</p> <p>Fig_1 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p> <p> </p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a network approach
<p>Dataset associated to Ramírez-Ramírez RD, Montilla LM, Cavada-Blanco F and Croquer A. Identification of strengths and weaknesses of cooperative efforts within the wider Caribbean using a collaboration network approach [version 1; not peer reviewed]. <em>F1000Research</em> 2016, <strong>5</strong>:799 (poster) (doi: 10.7490/f1000research.1111809.1)</p>
MOSID (Microcontroller On-chip Sensor IDentification): A dataset of readings from the internal monitoring sensors of STM32L152RTXX microcontrollers during the stimulation of their electronic activity
<p>The MOSID (Microcontroller On-chip Sensor IDentification) dataset consists of 5 acquired data subsets (6,72 GB total, compressed into 560 MB), each collected during different experiments and periods using various equipment (HMP4040, DF1731SB & HM305) and acquisition strategies. These subsets contain readings from the temperature and voltage sensors embedded in 20 STM32L-DISCOVERY devices. The data was captured during the execution of 5 different workloads as stimuli, repeated over 20 iterations. The stimuli employed are as follows:</p><ol><li>20x20 Long-type matrix product.</li><li>20x20 Float-type matrix product.</li><li>Algorithm for ascending sorting, Bubble Sort.</li><li>Algorithm for 2D-point clustering, Convex Hull.</li><li>Encryption algorithm AES 128-bit.</li></ol><p>The subsets are structured according to the folder format "X_Y," where X is the manually assigned number to the board, and Y is the corresponding number for the executed algorithm. Within each of these folders, files are present in the format "data_Z.txt," where Z represents the iteration number to which the file belongs. In total, the dataset comprises 9600 files with a final size of approximately 7 GB. The different presented subsets are as follows:</p><ul><li>ACQ1: Derived from the experiment named "Automatic Acquisition 1 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ2: Derived from the experiment named "Automatic Acquisition 2 (HMP4040)" conducted using a daisy-chain topology (20 out of 20 boards, 2000 files).</li><li>ACQ3: Derived from the experiment named "Individual Acquisitions (HMP4040)", performed board by board from idle conditions (20 out of 20 boards, 2000 files).</li><li>ACQ4: Derived from the experiment named "GOLD SOURCE DF1731SB Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards excluding boards , 1800 files).</li><li>ACQ5: Derived from the experiment named "HANMATEK HM305 Acquisitions" conducted using a partial daisy-chain setup (2 devices at a time, 18 out of 20 boards, 1800 files).</li></ul><p>In each "data_Z.txt" file, starting from the 5th line, temperature and voltage raw ADC conversions from the sensors are provided, captured during the execution of the stimulus in successive lines. Additionally, a table (Table_UIDS.csv) with metadata for each of the boards used in the experiments is included, which is needed in order to normalize the data in terms of ºC and Volts.</p><ul><li>BOARD_NUM, which contains the manually assigned board number.</li><li>UID, which contains the Unique Identifier of the board assigned by the manufacturer.</li><li>T_CAL_1, which holds the calibration value of the board's temperature sensor at 30ºC.</li><li>T_CAL_2, which holds the calibration value of the board's temperature sensor at 100ºC.</li><li>VREFINT_CAL, which contains the calibration value of the board's voltage sensor.</li></ul>
Dynobench - extended Strogatz benchmark for system identification methods
<p>The dynobench repository contains a benchmark for system identification methods. Currently includes models of 10 dynamical systems: Bacterial respiration, Bar magnets, Glider, Lotka-Volterra, Predator-Prey, Shearflow and Van der Pol from the Strogatz dataset, as well as Lorenz, Coupled phase oscillators and Stuart-Landau models for dynamical systems that often appear in the research community. They also add variety to the benchmark as the Lorenz oscillator model introduces a larger set of state variables (three compared to two), and the coupled phase oscillators model is non-autonomous, which is reflected in the explicit incorporation of time in its equations.</p><p>The repository contains the 'data' folder, where the simulations of ten dynamical systems are stored, simulated under 6 different configurations of data quality. The first dimention modifies the data length and coarseness, where a 'small' dataset includes simulations of 10 seconds with a 0.1 sampling step, and a 'large' dataset includes simulations of 20 seconds with a 0.01 sampling step. The second dimention of data quality modifies the amount of noise in the data, where there are three levels of noise (no noise, moderate levels with 30 dB signal-to-noise ratio and high levels of noise with 13 dB signal-to-noise ratio). The data can be used by itself, without the need to look at the python code.</p><p>The repository also contains the main.py script by which the data can be generated. The 'src' folder contains additional python scripts that are needed to generate the data. The data were created by first randomly setting the initial values for one category, in particular a configuration of 'small', 'noise-free' and 'train' data (using inits_type = "random"). Then, all the other configurations were generated by using the same initial values. Inside the script main.py there is more information about the settings and how to run the script. </p><p>The benchmark was created as a part of the research described in the paper titled <i>Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data, </i>written by Omejc et al.<i> </i>(in submission).</p>
Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis
<p>We sought to identify the best biomarkers for the early diagnosis of LC, using a systematic review of seven databases. We identified 79 articles that focused on the identification and assessment of diagnostic biomarkers and then performed a meta-analysis. This work has been submitted for publication.</p>
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</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.