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6,059 results for “Journale”
Figure 13. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 13. - Entrance of cave Miljacka II, type locality of Eupolybothrus cavernicolus Komerički & Stoev sp. n.
Figure 18a. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 18a. - Prefemur of male leg 15. From Stoev et al. (2010). Figure 18a. Eupolybothrus caesar Figure 18b. Eupolybothrus spiniger <br> Eupolybothrus caesar
Figure 19. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 19. - Delineation of Eupolybothrus species – Neighbor joining tree K2P distances. Visualised are the clusters obtained from the reversed Statistical Parsimony (SP) method and the Automatic Barcoding Gap Discovery (ABGD) procedure. Bootstrap support for the identified lineages are given above. The intraspecific genetic variability is given for each cluster. Source data is available in Suppl. material 1.
Figure 20b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 20b. - Gene annotation. Original data available from GigaScience GigaDB (Stoev et al. 2013). Figure 20a. E-value, identity and species distribution statistics of the sequences that can find homologs on Nr database Figure 20b. COG functional classification of the transcripts Figure 20c. GO categories of the transcripts <br> COG functional classification of the transcripts
Figure 17b. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 17b. - Prefemur of male leg 15. From Stoev et al. (2010). Figure 17a. Eupolybothrus tabularum Figure 17b. Eupolybothrus excellens <br> Eupolybothrus excellens
A list with recommended journals by Russian VAK
<p>Processed list of recommended journals in social science and computer science by VAK (source of raw data: http://vak.ed.gov.ru/87, date: 19.04.2017).</p>
Data supplementing article "Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution" under review at the Journal of Geophysical Research - Biogeoscience
<p>These data supplement the article: Du, J. and J. Shen, Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution, under review at the Journal Of Geophysical Research: Biogeoscience</p> <p>contact: Jiabi Du, jiabi@vims.edu</p> <p>Below are descriptions of the data files included here:</p> <p>1. Monthly mean tracer output [1985-2014]</p> <p>-netCDF format results for monthly mean tracer concentrations from different sources (Susquehanna, Potomac, Rappahannock, York, James Rivers, and Coastal Ocean)</p> <p>-grid information are also included</p> <p>2. Matlab Scripts For Plotting.zip:</p> <p>-Matlab scripts used to plot the horizontal map, the vertical profile for the along channel section, the vertical profile for cross-channel sections. The script enables users to define the period and section no to plot. </p> <p>3. tracer influx and outflux ratio at 9 cross-section.xls:</p> <p>-an excel file contains the bottom tracer influx ratio and surface tracer outflux ratio for different rivers at different sections. </p>
Dataset supplementing the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19.
<p>These files supplement the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19. The data are free for scientific use, provided this reference is appropriately cited.</p> <p>exp1_data.mat contains all the data of experiment 1 as cell arrays of size 8x16x8 (subject x block x trial) or 8x16 (subject x block). Specifically:<br> xEye: the horizontal eye position in raw (pixel coordinates)<br> gain: the OKN slow phase gain computed from the xEye data as described in the paper; in audio-visual blocks the sign is chosen such that positive gain corresponds to the direction of the grating associated with the low tone; in unambiguous visual blocks (1,16) positive sign corresponds to the direction of the grating.<br> ixLow, ixHigh, ixNone, ixBoth: indices for xEye and gain of the same subject and block for which the button corresponding to the low tone, the high tone, both buttons or no button was pressed.</p> <p>exp2_data.mat and exp3_data.mat contain the data of experiment 2 and experiment 3, respectively, and are organized analogously to exp1_data.mat.</p> <p>figure3.m through figure6.m use these data to plot the respective paper figures to exemplify usage of the data.</p> <p> </p>
Dataset supplementing B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)
<p>Dataset presented in B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)</p>
Dataset supplementing "Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11."
<p>These data supplement the publication</p> <p>Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11.</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>exp1_data.mat contains data of experiment 1</p> <p>exp2_data.mat contains data of experiment 2</p> <p>figure2_3.m and figure4_5.m exemplify usage of the data and reproduce the figures 2-5 of the aforementioned article.</p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
Semantic annotation of PLoS journal citation contexts
<p>Dataset </p>
Scientometric Indexes of Scientific Journals
<p>Scientometric data from 62,856 journals consolidated from Scopus, SJC, Diamond Journals, ISSN database, Google Scholar and Brazilian QUALIS (2022). Its purpose is to support researchers who want to analyze and compare various scientometric attributes and also the economic model used by scientific journals around the world.</p><p>CSV UTF-8 File, separated by semicolon.</p><p><strong>Metadata:</strong></p><p>ISSN-L: Journal Identified used by ISSN</p><p>ISSN: Secondary Journal identifier (used for Print or other medium versions);</p><p>Name is in the journal origin language;</p><p>"ASJC" is the Knowledge Area Code based on All Science Journal Category used by Citescore/Scopus;</p><p>"Citescore" is based on 2022 Scopus;</p><p>"Google H5" are based on 2020 database;</p><p>"Qualis" is the Brazilian score calculated for the 2017-2021 quadrienal;</p><p>Country Name (País) is in English merged from all databases;</p><p>"Modelo" can have 3 values: D-Diamond Journals (No money involved), T-Closed (You have to pay to access), A-APC (You have to pay to publish);</p><p>This is a working in progress and we intend to add more atributes and fill some gaps on knowledge areas field (ASJC) and other empty attributes.</p>
Simulation dataset and plotting scripts used for journal article "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024)
<p>Simulation data underlying all figures presented in "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024). DOI: 10.5194/acp-24-1467-2024</p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
SCOPING REVIEW OF EMPIRICAL LITERATURE ON EVALUATION OF EDITORIAL POLICIES SUPPORTING OPEN SCIENCE PRACTICES IN SCHOLARLY JOURNALS
<p>Excel file containing the description of methods & materials used in the scoping review, the whole dataset of included studies, and respective descriptive metadata.</p>
Simulations used in the Ocean Science Journal submission titled "Internal and forced ocean variability in the Mediterranean Sea " by Benincasa et al., 2024
<p>Temperature (votemper) and current speed datasets from the EAS5 (Clementi et al., <em>Mediterranean Sea Analysis and Forecast (CMEMS MED-Currents, EAS5 system),</em> 2019; Coppini et al., <em>The Mediterranean forecasting system. Part I: evolution and performance</em>, EGUsphere, pp. 1–50, 2023) simulations used in the manuscript titled "<em>Internal and forced ocean variability in the Mediterranean Sea"</em> and submitted to the journal Ocean Science by Benincasa et al. </p> <p>The daily fields are at 2 depth levels ( 0 = 0 m, 2 = 30 m) and in the 2 seasons (JFMA = winter, JASO = summer) for the entire Mediterranean Sea. The vertical profile of the temperature field up to about 950 m depth is available at 8 locations distributed over the basin. The depth levels are found in <em>depth.pkl</em>: the first column represents the depth of the various levels, whereas the second is the increments between 2 consecutive depth levels. </p>
Simulation dataset and plotting scripts used for journal article "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024)
<p>Simulation data underlying all figures presented in "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024) <a href="http://dx.doi.org/10.1080/02786826.2024.2323641" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.1080/02786826.2024.2323641</a>. </p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
The Editors-in-Chief of SOIL ORGANISMS: Prof. Dr. Willi Xylander (Görlitz) and Prof. Dr. Nico Eisenhauer (Leipzig). in SOIL ORGANISMS - an international open access journal on the taxonomic and functional biodiversity in the soil
The Editors-in-Chief of SOIL ORGANISMS: Prof. Dr. Willi Xylander (Görlitz) and Prof. Dr. Nico Eisenhauer (Leipzig).
Raw data for Figures 2-4 for journal article: "Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid""
<p>This is a data set containing the raw data for figures 2-4 from the journal article:</p> <p>"Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid"</p> <p>Original article DOI: 10.1055/a-1308-3888</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/33260222/</p> <p> </p> <p>The data is contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p> </p>
F I G U R E 1 The relationship between 10 in Characteristics of papers that affect citations in the Journal of Fish Biology
F I G U R E 1 The relationship between 10 of the most influential variables extracted from papers published in the Journal of Fish Biology (between January 2010 and March 2021) and paper impact (high, medium, low) (±95% confidence interval). Papers were categorized as high impact if they fell into the top quartile of normalized citation counts and low impact if they fell into the bottom quartile of normalized citation counts; all other papers were classified as medium impact.
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.