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3,172 results for “Publications”
Settlement-era public land survey data: Six Rivers National Forest
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Microsatellite genotypes and extraction plate positions from publication: Genetic structure across urban and agricultural landscapes reveals evidence of resource specialization and philopatry in the Eastern carpenter bee, Xylocopa virginica L.
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Figure 2 from: Cocquyt C, Lokele Ndjombo E, Tutu Tsamemba S, Nshimba Seya wa Malale H (2019) Freshwater diatoms in the Democratic Republic of the Congo: a historical overview of the research and publications. PhytoKeys 136: 107-125. https://doi.org/10.3897/phytokeys.136.47386
Figure 2 Pie diagram showing the relative abundance of the 21 genera (s.l.) to which the newly described species and infraspecific taxa from the DR Congo belong, Nitzschia being the most important, followed by Pinnularia and Navicula s.l.
GFF files used for Panaroo publication
<p>The GFF files used in the publication of the Panaroo pangenome analysis pipeline.</p>
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
<p>This dataset consists of 1,181,993 Tweet Ids, obtained as a result from the paper - A large-scale Twitter dataset for drug safety applications mined from publicly existing resources.</p> <p>The Tweet Ids can be hydrated using <strong>twarc</strong>, a command line tool and Python library for archiving Twitter JSON data. Please follow these instructions to install twarc - <a href="https://github.com/DocNow/twarc">https://github.com/DocNow/twarc</a></p> <p>It will take less than 4 hours to hydrate the tweet ids using twarc. </p> <p>If you intend to use the dataset, please cite the paper. </p>
Data Publication accompanying the paper "How FAIR can you get? Image Retrieval as a Use Case to calculate FAIR Metrics"
<pre>This dataset is the result of a benchmark run for a use-case-centric FAIR metric. The applied tech stack uses OAI-PMH and DataCite. The use case central to this benchmark is the retrieval of temporally and spatially annotated images. The zipped archives includes the data created during the first test run in June 2018. </pre>
Simulation data used for publication "Dilatory and downward development of 3-meter scale irregularities in the Funnel-like region of Equatorial Plasma Bubble" by Tulasi Ram et al.
<p>The dataset includes two-dimensional simulation output used in the paper.</p> <p>"altitude_eq.dat" and "zonal.dat" contains grid information.</p> <p>"read_n_2D_0.5mV.pro" and "read_n_2D_2mV.pro" are IDL files to read the dataset, with detailed description of each data.</p> <p>The original three-dimensional simulation output is too large to publish at the repository. Author (TY) is willing to share the original data.</p>
Publicly available GWAS summary statistics, harmonized and imputed to GTEx v8' variant reference
<p># harmonized and imputed GWAS summary statistics</p> <p> </p> <p>* `harmonized_imputed_gwas.tar` contains 114 publicly available GWAS traits, harmonized and imputed to GTEx v8 reference</p> <p> </p> <p>* `gwas_metadata.txt` is a table with useful information about each trait, such as:</p> <p>- Tag: trait name (also in the file name)</p> <p>- PUBMED_Paper_Link: PUBMED or publication URL (if available)</p> <p>- Portal: URL to web portal from which data was downloaded</p> <p>- Consortium: GWAS Consortium authoring the data</p> <p>- Sample_Size: number of individuals covered in the study</p> <p>- Population: individuals'ancestry (EUR, EAS, etc)</p> <p>- abbreviation: short name used for figures</p> <p>- new_abbreviation: alternative name for additional figures</p> <p>- Deflation: whether imputed summary statistics exhibited deflation (i.e. association p-values are lower than expected by chance. The summary statistics imputation method is conservative, and in public GWAS with few observed variants (<2M), the distribution of p-values lags towards lower significance spectrums.</p> <p># Data usage policy</p> <p>When using this data, you must acknowledge the source by citing the publication "Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits" (https://doi.org/10.1101/814350).</p> <p># Disclaimer</p> <p>The data is provided "as is", and the authors assume no responsibility for errors or omissions. <br> The User assumes the entire risk associated with its use of these data. <br> The authors shall not be held liable for any use or misuse of the data described and/or contained herein. <br> The User bears all responsibility in determining whether these data are fit for the User's intended use. </p> <p>The information contained in these data is not better than the original sources from which they were derived,<br> and both scale and accuracy may vary across the data set. <br> These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics<br> appropriate for applications that potential users of the data may contemplate. <br> <br> The user is responsible to comply with any data usage policy from the original GWAS studies;<br> refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1)<br> to identify their respective Consortia's requirements.</p> <p><br> THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND,<br> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.<br> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,<br> WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br> OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.</p>
Supplemental figures for the Puccinia striiformis f. sp. tritici DK0911 genome publication
<p>These are supplemental figures for the Puccinia striiformis f. sp. tritici DK0911 genome publication. This dataset is associated with the publication "Distinct life histories impact dikaryotic genome evolution in the rust fungus Puccinia striiformis causing stripe rust in wheat", the Bioproject accession number PRJNA588102, and 10.5281/zenodo.3556840. </p> <p>Update 20200212:</p> <p>We now added the updated Supplemental Figures (Supplemental_figures_DK0911_revision_01.tar.gz) which are part of the first revisions of this manuscript.</p> <p>Preprint:</p> <p>https://www.biorxiv.org/content/10.1101/859728v1</p>
Supplementary Data for Publication Titled "Sex differences in regulating the cardiac transcriptome within a murine model for hypertrophic cardiomyopathy"
<p>Supplementary data for publication titled "Sex differences in regulating the cardiac transcriptome within a murine model for hypertrophic cardiomyopathy".</p>
Public Participation, Engagement and Climate Change Adaptation: A Review of the Research Literature – Scientometric Analysis
<p>Scientometric Analysis accompanying the journal article <a href="https://doi.org/10.1002/wcc.645"><em>Public Participation, Engagement and Climate Change Adaptation: A Review of the Research Literature</em></a></p>
Data analysis scripts for the publication - Differences in the transcriptomic response of Campylobacter coli and Campylobacter lari to heat stress (Riedel et al.)
<p>Data analysis scripts and data for the publication "Differences in the transcriptomic response of Campylobacter coli and Campylobacter lari to heat stress" by Riedel <em>et al.</em>.</p>
Supporting publication for 'Prevalence sample-based guidance for reporting 2019 data'
<p>These two Excel documents help you to map terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer you examples on how prevalence data can be reported using SSD2</p>
SUSdblp Dataset for Classification of Seminal, Uninfluential and Survey Publications
<p>This data set contains citation network data for 1980 publications from dblp (<a href="https://dblp.uni-trier.de/">https://dblp.uni-trier.de/</a>) enriched with data from AMiner (<a href="https://aminer.org/">https://aminer.org/</a>) for classification of seminal and survey publications. It is an extension of the SeminalSurveyDBLP dataset (<a href="https://zenodo.org/record/3258164#.XlztuUoxmUm">https://zenodo.org/record/3258164#.XlztuUoxmUm</a>).</p> <p>Citations and references are contained for every publication. For each of the 129,442 papers, dblp key, publication year as well as stemmed and unstemmed concatenations of its title and abstract are given. For citing and referenced papers, their number of citations as well as their field and time normalised citation count are contained. Seminal papers come from A* conferences, surveys were extracted from venues specialized in publishing reviews. Uninfluential publications come from C conferences and obtained less than ten citations.</p> <p>For details, see <em>Evaluating Semantometrics from Computer Science Publications</em>, Christin Katharina Kreutz, Premtim Sahitaj, and Ralf Schenkel, to appear in Scientometrics.</p>
Dataset related to the publication "Synchronization of Optomechanical Nanobeams by Mechanical Interaction", DOI: 10.1103/PhysRevLett.123.017402
<p>This folder contains the raw data from which the graphs in paper "Synchronization of Optomechanical Nanobeams by Mechanical Interaction", DOI: 10.1103/PhysRevLett.123.017402, have been obtained.</p>
Data and Code for Publication "Testing the Utility of Dental Morphological Trait Combinations for Inferring Human Neutral Genetic Variation"
<p>Data and code for publication: H. Rathmann, H. Reyes-Centeno, Testing the utility of dental morphological trait combinations for inferring human neutral genetic variation. <em>Proc. Natl. Acad. Sci. U.S.A.</em> 117, 10769-10777 (2020). DOI: 10.1073/pnas.1914330117</p> <p>The repository contains:</p> <ul> <li>“R-code.txt”: R code for an exhaustive search algorithm testing the utility of dental morphological traits and trait combinations for inferring human neutral genetic variation.</li> <li>“dental trait frequencies.csv”: Data set with 27 dental morphological trait frequencies for 20 modern human populations worldwide used for analysis. Data from G. R. Scott, C. G. Turner, G. C. Townsend, M. Martinón-Torres, <em>The Anthropology of Modern Human Teeth</em> (Cambridge University Press, 2018). DOI: 10.1017/ 9781316795859</li> <li>“microsatellite loci mean sizes.csv”: Data set with 645 microsatellite mean allele sizes for 20 modern human populations worldwide used for analysis. Data from T. J. Pemberton, M. DeGiorgio, N. A. Rosenberg, Population structure in a comprehensive genomic data set on human microsatellite variation. <em>G3: Genes Genom. Genet.</em> 3, 891–907 (2013). DOI: 10.1534/g3.113.005728</li> <li>“utility estimates for 134217727 trait combinations.txt”: A large table with utility estimates for 27 dental morphological traits and all 134,217,700 possible trait combinations.</li> </ul> <p>Abbreviations for the 20 population names (rows) in “dental trait frequencies.csv” and “microsatellite loci mean sizes.csv” as follows:</p> <ul> <li>AUS = Australia</li> <li>CAS = Central Asia</li> <li>EAF = Eastern Africa</li> <li>EAS = East Asia</li> <li>EEU = Eastern Europe</li> <li>IND = India</li> <li>MAM = Mesoamerica</li> <li>MEL = Melanesia</li> <li>MIC = Micronesia</li> <li>NAF = North Africa</li> <li>NAM = North America</li> <li>NESI = Northeast Siberia</li> <li>NGU = New Guinea</li> <li>NWAM = Na-Dene</li> <li>POL = Polynesia</li> <li>SAM = South America</li> <li>SAN = San</li> <li>SEAS = Southeast Asia</li> <li>WEU = Western Europe</li> <li>WSAF = Sub-Saharan Africa</li> </ul> <p>Abbreviations for the 27 dental morphological trait names (columns) in “dental trait frequencies.csv” as follows:</p> <ul> <li>T1 = Winging (UI1)</li> <li>T2 = Shoveling (UI1)</li> <li>T3 = Double-Shoveling (UI1)</li> <li>T4 = Interruption Grooves (UI2)</li> <li>T5 = Tuberculum Dentale (UI2)</li> <li>T6 = Mesial Ridge (UC)</li> <li>T7 = Distal Accessory Ridge (UC)</li> <li>T8 = Hypocone (UM2)</li> <li>T9 = Carabelli Trait (UM1)</li> <li>T10 = Cusp 5 (UM1)</li> <li>T11 = Enamel Extensions (UM1)</li> <li>T12 = Peg-Reduced-Missing (UM3)</li> <li>T13 = Lingual Cusp Number (LP2)</li> <li>T14 = Groove Pattern (LM2)</li> <li>T15 = Cusp 6 (LM1)</li> <li>T16 = Cusp Number (LM2)</li> <li>T17 = Deflecting Wrinkle (LM1)</li> <li>T18 = Distal Trigonid Crest (LM1)</li> <li>T19 = Protostylid (LM1)</li> <li>T20 = Cusp 7 (LM1)</li> <li>T21 = Odontomes (UP-LP)</li> <li>T22 = Root Number (UP1)</li> <li>T23 = Root Number (UM2)</li> <li>T24 = Root Number (LC)</li> <li>T25 = Tomes’ Root (LP1)</li> <li>T26 = Root Number (LM1)</li> <li>T27 = Root Number (LM2)</li> </ul> <p>Abbreviations for the 645 microsatellite allele locus names (columns) in “microsatellite loci mean sizes.csv” as in T. J. Pemberton, M. DeGiorgio, N. A. Rosenberg, Population structure in a comprehensive genomic data set on human microsatellite variation. <em>G3: Genes Genom. Genet.</em> 3, 891–907 (2013). DOI: 10.1534/g3.113.005728</p>
Publication Panama
<p>[Sample] [Panama]</p>
Model runs generated by publication "Re-evaluating 14 C dating accuracy in deep-sea sediment archives"
<p>Model runs generated by following publication:</p> <p>B.C. Lougheed, P. Ascough, A. Dolman, L. Löwemark and B. Metcalfe, 2020. “Re-evaluating 14C dating accuracy in deep-sea sediment archives.” Geochronology, doi:10.5194/gchron-2019-10</p> <p>Contains .mat files that can be opened by the Matlab / Octave environment.</p>
Supplemental data for "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." Article published as Global Biogeochemical Cycles publication 2019B006383
<p>These are supporting data for Global Biogeochemical Cycles publication 2019B006383: "Initial land use/cover distribution substantially affects global carbon and local temperature projections in the integrated Earth System Model." They include data for all of the regular and supplemental figures.</p>
uEMEP startup configuration and data file for GMD uEMEP model description publication
<p>These zip files contain the configuration and example data files for running uEMEP to reproduce the results in the uEMEP model description GMD publication (DOI: ?). Code and make files are accessible through github or through the associated repository here (DOI: 10.5281/zenodo.3756008).</p> <p>The zip files, once installed, allow the simulation of one day 01.01.2017. The example simulation is limitted to one day due to the size of the daily EMEP files used and the NORTRIP road dust emissions files. EMEP data for the rest of the year is accessible though '<a href="https://thredds.met.no/thredds/catalog/data/fou-kl/uEMEP/EMEP4NO/EMEP4NO_rerun_2017_v2/catalog.html">https://thredds.met.no/thredds/catalog/data/fou-kl/uEMEP/EMEP4NO/EMEP4NO_rerun_2017_v2/catalog.html</a>' if a complete calculation for a year, as appears in the publication, is to be made (size 5.8 GB per day). The remaining NORTRIP files for 2017 are available on request (70GB zipped file).</p> <p>The '<a href="https://zenodo.org/api/files/9b9eda53-6e17-4c8f-b24a-866fc7e3b121/uEMEP_demo_startup_files.zip?versionId=9aa47918-2bd8-418c-85bc-fe6c1e7f1cf5">uEMEP_demo_startup_files.zip</a>' zip file contains a bash script 'uEMEP_script_demo_startup.sh' and configuration files for calculating this day. The config file 'uEMEP_EMEP_config_hourly_default_startup_v1.txt' is the main default configuration file for uEMEP which contains explanations of the variables. Two simulations are possible with additional configuration files. One that reproduces a mapping calculation for a small region of Oslo 'Oslo_100m_5km.txt' and one that will calculate concentrations only at positions of specified measurement site receptors in norway 'uEMEP_EMEP_extra_config_Norway_station_startup_v1.txt'.</p> <p>It is necessary to change the route directories in the config files and the script file after installation and to tailor the script file to own machines and que systems.</p> <p>Example log files from the two runs are included along with the resulting uEMEP output for the simulations to check reproducability.</p> <p>Also included is 'matlab_plotting_scripts.zip', which contains the matlab scripts used to plot the validation results shown in the GMD article.</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.