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761 results for “data journal”
Figure 3. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 3. - Habitus of the holotype female of P. hardyi in dorsal view.
Figure 1. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 1. - Habitus of a paratype male of P. hardyi in lateral view.
Figure 2. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 2. - Habitus of the holotype female of P. hardyi in lateral view.
Figure 13. from: Studies in Hawaiian Diptera III: New Distributional Records for Canacidae and a New Endemic Species of Procanace - Biodiversity Data Journal 4: e5611 (08 April 2016) https://doi.org/10.3897/BDJ.4.e5611
Figure 13. - Distribution of P. constricta (Molokai, Maui, Hawaii) and P. williamsi (Oahu).
Supplementary material 2: Seu Nico Community Dynamics from: Tree Diversity and Dynamics of the Forest of Seu Nico, Viçosa, Minas Gerais, Brazil - Biodiversity Data Journal 3: e5425 (31 July 2015) https://doi.org/10.3897/BDJ.3.e5425
2868 tree occurrences from two census within 100 plots of 10x10 m in the Forest of Seu Nico (FSN), Viçosa municipality, Minas Gerais, Brazil, including measurements of each tree as well as environmental data from all 100 plots. Dataset consists of seven independent files
Figure 1. from: Tree Diversity and Dynamics of the Forest of Seu Nico, Viçosa, Minas Gerais, Brazil - Biodiversity Data Journal 3: e5425 (31 July 2015) https://doi.org/10.3897/BDJ.3.e5425
Figure 1. - The Forest of Seu Nico (FSN) covers the bottom and the slopes of a small valley on the Bom Sucesso Farm in Viçosa, Minas Gerais, Brazil. Photograph by M. Gastauer from northeastern direction.
Figure 3b. from: Two new species of Scymnini (Coleoptera: Coccinellidae) from Karnataka, India - Biodiversity Data Journal 3: e5296 (22 June 2015) https://doi.org/10.3897/BDJ.3.e5296
Figure 3b. - Horniolussororius sp. n.Figure 3a.Dorsal viewFigure 3b.Lateral view <br> Lateral view
Figure 3a. from: Two new species of Scymnini (Coleoptera: Coccinellidae) from Karnataka, India - Biodiversity Data Journal 3: e5296 (22 June 2015) https://doi.org/10.3897/BDJ.3.e5296
Figure 3a. - Horniolussororius sp. n.Figure 3a.Dorsal viewFigure 3b.Lateral view <br> Dorsal view
Biomedical Journal Data Sharing Policies
<p>Raw data of data sharing policies in over 300 journals, supporting the article currently under review: "Reproducible and reusable research: Are journal data sharing policies meeting the mark?". </p> <p>Raw data and analysis of data sharing policies of 318 biomedical journals. The study authors manually reviewed the author instructions and editorial policies to analyze the each journal's data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal.</p> <p>We evaluated journals included in Thomson Reuter’s InCites 2013 Journal Citations Reports (JCR) classified within the following World of Science schema categories: Biochemistry and Molecular Biology, Biology, Cell Biology, Crystallography, Developmental Biology, Biomedical Engineering, Immunology, Medical Informatics, Microbiology, Microscopy, Multidisciplinary Sciences, and Neurosciences. These categories were selected to capture the journals publishing the majority of peer-reviewed biomedical research. The original data pull included 1,166 journals, collectively publishing 213,449 articles. We filtered this list to the journals in the top quartiles by impact factor (IF) or number of articles published 2013. Additionally, the list was manually reviewed to exclude short report and review journals, and titles determined to be outside the fields of basic medical science or clinical research. The final study set included 318 journals, which published 130,330 articles in 2013. The study set represented 27% of the original Journal Citation Report list and 61% of the original citable articles. Prior to our analysis, the 2014 Journal Citations Reports was released. After our initial analyses and first preprint submission, the 2015 Journal Citations Reports was released. While we did not use the 2014 or 2015 data to amend the journals in the study set, we did employ data from all three reports in our analyses. In our data pull from JCR, we included the journal title, International Standard Serial Number (ISSN), the total citable items for 2013, 2014, and 2015, the total citations to the journal for 2013/14/15, the impact factors for 2013/14/15, and the publisher.</p>
Data supplementing the article: Schultz, N.M., Lawrence P.J, Lee X., Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research - Biogeosciences
<p>These data supplement the article: Schultz, N.M., Lawrence P.J, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation, under review at the Journal of Geophysical Research - Biogeosciences.</p> <p>contact: Natalie M. Schultz, natalie.schultz@yale.edu</p> <p>Below are descriptions of the data files included here:</p> <p><br> (1) Global LST data: globalLST_Forest_Open.YYYY.nc [2003-2013]</p> <p>- DayLST1/NightLST1 and DayLST2/NightLST2 are the final (after the DEM correction) LST values for forest, and open land cover classes, respectively.<br> - Count variables show the number of pixels of each land cover class in each 0.5 degree grid<br> - The average elevation of each class is given by the DEM1 and DEM2 vars<br> - The DEM correction is the dLSTdDEM vars</p> <p>(2) Global fluxes data: globalFluxes_Forest_Open.YYYY.nc [2003-2013]<br> - Again, forest class = var1, open class = var2<br> - SWRABS is absorbed solar radiation<br> - LE is the latent heat flux<br> - HP is the heating potential term, as defined in the manuscript<br> - As described for the LST data, class pixel counts and DEM data are included</p> <p>(3) climzones3.nc<br> - The delineation of the three climate zones defined in this paper</p> <p>(4) MERRA inversion data: MERRA_11yr_TS_T10M.mat [2003-2013]<br> - 11 years of daily 1am local data averaged over 8-day intervals for 2003-2013<br> - TS = surface temperature<br> - T10M = 10M air temperature (above d)</p> <p> </p> <p> </p>
Data supplementing the article "Assessing ecological status with diatoms DNA metabarcoding : scaling-up on a WFD monitoring network (Mayotte island, France)" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal
<p>These data supplement the article"Assessing ecological status with DNA metabarcoding or microscopy? Comparison using benthic diatoms in tropical rivers" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal.</p> <p>The directory contains the following files:</p> <p><strong>80 PGM sequencing libraries (raw data, fastq files).rar </strong>- contains the 80 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>80 fastq files information.xlsx</strong> - contains the information relative to the 80 samples including: the ID used in Mothur analyses (corresponding to the name of the fastq files), the sample name, the sampling site code, the name of the river, the monitoring network to which rivers belong, the year of sampling and the GPS coordinates of sampling sites.</p> <p><strong>OTU (95 percent of similarity) list of 80 Mayotte samples.xlsx</strong> - contains the final OTU list obtained after applying all the bioinformatics treatments (trimming, clustering,...): OTUs created at 95% of similarity, the number of DNA reads per sample was normalized at 5710 reads (the smallest values obtained in one sample). A DNA representative sequence and the taxonomic assignment determined using Mothur (using classify.otu command) are also provided for each OTU.</p>
Figure 21. 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 21. - Eupolybothrus cavernicolus Komerički & Stoev sp. n., paratype, 3D model, volume rendering, created with CTVox, virtual rotation and dissection. Movie available at: YouTube.
Figure 22. 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 22. - Movie of Eupolybothrus cavernicolus Komerički & Stoev sp. n., holotype, filmed ex-situ in a plastic container. Movie available at: YouTube.
Data availability to ensure the reproducibility of the results of Rivaes et al. (2017) in the journal HESSD.
<p>These data is provided to ensure the reproducibility of the results presented in the publication authored by Rivaes et al. (2017) in the Journal of Hydrology and Earth System Sciences Discussion.</p>
AI/ML mentions in AGU abstracts 2012-2023 and Data DOIs in AGU journals 2019-2022
<p>Dataset published in <a href="https://ui.adsabs.harvard.edu/link_gateway/2023Natur.623...28H/doi:10.1038/d41586-023-03316-8">10.1038/d41586-023-03316-8</a></p> <p>Updated with 2023 abstract data from AGU 23 annual meeting in San Francisco.</p>
Data referring to the Article "Harmonized Skies: A Survey on Drone Acceptance across Europe" published in Drones Journal
<p><span>The material provided is part of the article "Harmonized Skies: A Survey on Drone Acceptance across Europe," published in the Drones Journal (https://doi.org/10.3390/drones8030107). This article describes a study investigating civil drone acceptance in six different EU countries. This study was part of the USpace4UAM project (Grant Agreement No 101017643). The material includes the data set for the study described and the Python codes for the random forest analysis carried out to investigate the influence of demographic and personnel factors on drone acceptance.</span></p>
Raw data for Figures 5-8 for journal article: "Experimental investigation of the effect of middle ear in bone conduction"
<p>This is a data set contaning the raw data for figures 5-6 from the journal article:</p> <p>"Experimental investigation of the effect of middle ear in bone conduction"</p> <p>Original article DOI: 10.1016/j.heares.2020.108041</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/32810722/</p> <p> </p> <p>The data is contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p> </p>
Raw data for Figures 1-4 for journal article: "Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"
<p>This is a data set containing the raw data for figures 1-4 from the journal article:</p> <p>"Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"</p> <p>Original article DOI: 10.1080/14992027.2021.1903586</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/34097554/</p> <p> </p> <p>The data is contained within plots in word files, created with Microsofft Office (v18).</p>
Raw data for journal article: "Wave propagation across the skull under bone conduction: Dependence on coupling methods"
<p>This is a data set containing the raw data for figures 3-6 from the journal article:</p> <p>"Wave propagation across the skull under bone conduction: Dependence on coupling<br>methods"</p> <p>Original article DOI: 10.1121/10.0009676</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/35364950/</p> <p> </p> <p>The Fig 3 and 4 data are contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p>Fig 5 and 6 data are 3D velocity data for 5 cadaver heads (CH1-5) and FEM predictions.</p> <p>This data are stored within a folder structure indicating the stimulation condition (defined in the journal article). For example "Cadaver head data\CH1\Attract" contains cadaver head data for cadaver head 1 (CH1) with stimulation "Attract", as defined in the journal article above.</p> <p>For each combination of cadaver head (or FEM) and stimulation condition there is a TXT file (comma delimited) for the real and imaginary data at each stimulation frequency, and orthogonal velocity axis (X,Y,Z based on the anatomical coordinate system defined in the journal article) as well as the combined (maximum) velocity vector. The data set also includes a TXT file with the position (in same coordinate system the velocity data) of each measurement point and a list of stimulation frequencies.</p>
Raw data for journal article: "Intracochlear pressure in cadaver heads under bone conduction and intracranial fluid stimulation"
<p>This is a data set containing the raw data for figures 7-14 from the journal article:</p> <p>"Intracochlear pressure in cadaver heads under bone conduction and intracranial fluid stimulation"</p> <p>Original article DOI: 10.1016/j.heares.2022.108506</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/35459531/</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.