Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,172
datasets available to search
ShareScore release 0.9.0
Dataset results
3,172 results for “public”
Data from paper: Large carbon sink potential of Secondary Forests in Brazilian Amazon to mitigate climate change (public)
<p><strong>Title</strong>: Large carbon sink potential of Secondary Forests in the Brazilian Amazon to mitigate climate change</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>This repository contains</strong>:</p> <ol> <li>Zipped folder:<strong> Fig1_data_input.zip</strong> - all the files needed to produce Figure 1a-e of the main paper. Set the working directory to folder containing the file and use the script "Fig1a_f_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 1 - these files are in the format "<strong><driver>_assessment_v2.csv</strong>". The columns in the files are: A: age of secondary forest; B: 50th percentile (median) of the modal Aboveground Biomass (AGB) value for the given age (note, units are in biomass not carbon: Mg/ha/yr); C: The bias-corrected AGB value, calculated by subtracting the lowest AGB value in column B such that the AGB data starts at or near 0Mg/ha/yr at age 1. D: the number of secondary forest pixels observed to have the given age, E: "Threshold" : the threshold limits of the given driver e.g. 0 Fires in fire_assessmentv2.csv implies the corresponding secondary forest pixels experienced 0 fires throughout the analysis period. The folder also contains the output regrowth models seen in Figure 1 in the format "<strong>regrowth_model_<driver_threshold>.RData" </strong>where driver_threshold refers to the driving variable name and the associated threshold limit for the given driver.</li> <li>Zipped folder:<strong> Fig2_regions_outline.zip</strong> - contains the boundaries of the 4 regions identified in Figure 2a of the main paper in a shapefile (.shp) format and the corresponding file formats needed to produce and load a shapefile. </li> <li>Zipped folder: <strong>Fig1g_2b_e_variable_importance.zip</strong> - contains the output files of the random forest analysis assessing the variable importance for the whole Amazon ("whole_Amazon" subfolder) and for the different regions identified in Figure2a. Files are given as .RDS files that can be loaded in R and the corresponding figures produced using the script "Fig1g_2b_e_plot.R". Files start with the region of interest e.g. "whole_Amazon" or "NE_sector". Middle part of the filename - importance_conditionalTrue/False - this determines whether the importance was calculated using the conditional permutation (True) or not (False). The end of the file name - seed<NUM> - denotes the number of the random seed that was set to extract the sample data. e.g. whole_Amazon_2500_cforest_important_conditionalTrue_seed200.RDS - shows the conditional permutation importance assessment using a sample size of 2500 when the setseed parameter was set to 200 to extract a random sample representing the whole Amazon. The remaining files are the random forest output - as .RDS file. Please note the code to produce the random forest model and the importance assessment has not been included here - this code takes multiple days to run, so only the input and outputs have been included here. Please contact the corresponding author (see end) for more information on this. </li> <li>Zipped folder: <strong>Fig3_data_input.zip</strong> - all the files needed to produce Figure 3a-d of the main paper. Set the working directory to folder containing the file and use the script "Fig3_plot.R" to run (see below). The folder contains the input files of the 6 driving variables used to build regrowth models seen in Figure 3 - these files are in the format "<strong><REGION>-Group.csv</strong>". See bullet point 1 for explanations for the columns in the file. Again column E -"threshold" denotes the code used to identify the the 4 subclasses of regrowth seen in the Figure. Where 11 = No disturbance; 12 = Only burning; 21 = Only (multiple) deforestations; 22 = Both burning and multiple deforestations as disturbance. The code takes data in AGB and converts to AGC. The folder also contains the output regrowth models seen in Figure 3 in the format <strong>"regrowth_model_<region_disturbance_type>.RData" </strong>where region_disturbance refers to the region and the type of disturbance experienced. </li> <li> Zipped folder: <strong>Fig4_5_carbon_sink_2017.zip </strong>- Contains two subfolders: a) <strong>Map_aggre_0.1deg</strong> -this folder contains .tiff files (and associated files) of the losses, gains and net change in AGC between 2016 - 2017 in secondary forests in Amazonia - this has been aggregated to 0.1 degree grid cells so each cell contains the total sum of the losses/gains experienced by secondary forests in that 0.1degree grid cell. b) <strong>secondary_forest_by_region_and_disturbance </strong>- this folder contains .tiff files (and associated files) of the secondary forest data at the original resolution (30m) for 2016 and 2017 split up according to the regions identified in Figure 2, and the type of disturbance (if any). The associated files include a .dbf file which includes additional data [read "README.txt" file in folder] - upon loading the data in a GIS software - the age of the secondary forest pixel will be displayed - open the attribute table to see more data associated with that given pixel e.g. modelled associated AGB for a given pixel. Files in this folder can be used to make Figure 4d and Figure 5 - see script "Fig4_Fig5_plot.R" in the code repository (see below). </li> </ol> <p><strong>Code: </strong>The corresponding code mentioned here can be access here: <a href="https://github.com/heinrichTrees/secondary-forest-regrowth-amazon-public">heinrichTrees/secondary-forest-regrowth-amazon-public (github.com)</a></p> <p><strong>Data usage: </strong>When using any code or data in this repository or another related to this study please cite Heinrich et al.2021 and the original paper as well as the DOI of this repository. </p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Data and code for the publication "Tracing the horizontal transport of microplastics on rough surfaces"
<p><strong>Background</strong></p> <p>The data set contains images of fluorescent PMMA (Polymethyl methacrylate) particles that are moved by water on rough surfaces in an irrigation experiment. The experiments were done in the laboratory at the Institute of Geography, University of Cologne, Germany, in Septembre 2020. The images were taken with an sCMOS (advanced scientific complementary metal-oxide-semiconductor) high resolution pco.panda 4.2 camera (PCO AG, Kehlheim, Germany).</p> <p>The data set was analysed in the publication: Laermanns, H., Lehmann, M., Klee, M., Löder, M.G.J., Gekle, S. and Bogner, C., 2021, “Tracing the horizontal transport of microplastics on rough surfaces,” Microplastics and Nanoplastics, <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a></p> <p>Additionally to the data, this collection of files contains the Python and R scripts/notebooks used to analyse the images and create graphics for the publication. The code for the simulation of flow patterns can be obtained from the authors upon request.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p>Experimental parameters</p> <ul> <li> <p>Surface roughness: two levels, fine and course</p> </li> <li> <p>Inclination: 6 levels, 2.5°, 5°, 7.5°, 10°, 12.5° and 15°</p> </li> <li> <p>Irrigation: three levels, 4.8, 7.2 and 10.44 L/h</p> </li> <li> <p>Repetitions: three</p> </li> </ul> <p>More details on the experimental setup are given in the publication.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The folder <strong>images.zip</strong> contains the images. They are organized as follows:</p> <ul> <li><strong>Feinsand_10_Partikel</strong>: images of PMMA particles on the fine surface</li> <li><strong>Grobsand_10_Partikel</strong>: images of PMMA particles on the rough surface <ul> <li> <p>Both folders contain six subfolders <strong>_XX_Grad_Gefaelle</strong>, XX being 2_5, 5, 7_5, 10, 12_5, 15. These folders refer to inclinations of 2.5°, 5°, 7.5°, 10°, 12.5° and 15° of the rough surfaces, respectively.</p> </li> <li> <p>every folder _XX_Grad_Gefaelle contains three subfolders <strong>Fliessgeschwindigkeit_YY</strong>, with YY being 20mlx4, 30mlx4 and 43_5mlx4, the parameters of the peristaltic pump, corresponding to irrigation rates of 4.8, 7.2 or 10.44 L/h, respectively.</p> </li> <li> <p>every folder Fliessgeschwindigkeit_YY contains three subfolders <strong>Z_Durchgang</strong> with Z being 1, 2 or 3 corresponding to the tree repetitions of the experiment.</p> </li> </ul> </li> <li><strong>stained_flow_patterns</strong>: images of flow patterns of the fluorescent dye Nile Red (in methanol), an mp4 video and a text file with parameters to produce the video based on the images. The images were produced for the following experimental parameters: <ul> <li> <p><strong>Feinsand_2_5_Grad_20_ml</strong>: fine surface, inclined by 2.5° and irrigated with 7.2 L/h</p> </li> <li> <p><strong>Grobsand_7_5_Grad_20_ml</strong>: coarse surface, inclined by 7.5° and irrigated with 7.2 L/h</p> </li> </ul> </li> </ul> <p>The file <strong>experimental_data.csv</strong> links the concatenated folder names to experimental parameters.</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The images were first processed in Python to locate the PMMA particles and calculate particle sizes. The Python code is located in the <strong>py_scripts.zip</strong> folder. It contains the following files:</p> <ul> <li> <p><strong>find_XYZ</strong>: locates PMMA particles. XYZ stands for different experimental parameters (see above). Scripts containing the string <strong>_problems</strong> locate PMMA particles for images with possible artefacts (smeared particles, residual light etc.). You need to uncomment the appropriate lines in the files to rerun the code because it was run piece by piece.</p> </li> <li> <p><strong>pickle_to_csv.py</strong>: converts pickle files to csv files</p> </li> <li> <p><strong>calculate_sizes.py</strong>: calculates the sizes of PMMA particles from the first image of each experiment</p> </li> <li> <p><strong>py_functions_new.py</strong>: contains custom functions</p> </li> </ul> <p>Further analysis run in a mixture of R and Pyhton in one working document (R Notebook):</p> <ul> <li> <p><strong>Analysis_with_loops.Rmd</strong>: tracking of the PMMA particles by PtrakPy version 0.4.2 (Allan et al. 2019). Python 3.8 (Python Software Foundation, <a href="https://www.python.org/">https://www.python.org/</a>) was called directly from R using the R package reticulate (<a href="https://rstudio.github.io/reticulate/">https://rstudio.github.io/reticulate/</a>) in RStudio (<a href="https://www.rstudio.com/">https://www.rstudio.com/</a>).</p> </li> <li> <p><strong>Analysis_for_paper.Rmd</strong>: R code for analysis of tracking, statistical analysis, plotting. We used the R version 4.0.3 (R Core Team 2020).</p> </li> <li> <p><strong>helper_function.R</strong>: contains custom R functions for the analysis</p> </li> </ul> <p> </p> <p><strong>Results</strong></p> <p>The file <strong>results.zip</strong> contains the folders:</p> <ul> <li> <p><strong>data</strong>: *.pickle files produced by Python containing the trajectories of PMMA particles</p> </li> <li> <p><strong>data_csv</strong>: *.pickle files converted to *.csv files</p> </li> <li> <p><strong>figures</strong>: figures produced by the code during the analysis, organized in different subfolders</p> </li> <li> <p><strong>RData</strong>: large computational results produced and saved during analysis</p> </li> <li> <p><strong>sizes_csv</strong>: *.csv files containing PMMA particle sizes and further morphological characteristics; produced during analysis</p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Julia Horn for support in the laboratory and Florian Steininger for technical assistance.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357, subprojects B04 and B06.</p> <p> </p> <p><strong>References</strong></p> <p>Allan, Dan, Casper van der Wel, Nathan Keim, Thomas A Caswell, Devin Wieker, Ruben Verweij, Chaz Reid, et al. 2019. <em>Soft-Matter/Trackpy: Trackpy V0.4.2</em> (version v0.4.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.3492186">https://doi.org/10.5281/zenodo.3492186</a>.</p> <p>Laermanns, Hannes, Moritz Lehmann, Marcel Klee, Martin GJ Löder, Stephan Gekle, and Christina Bogner. 2021. “Tracing the Horizontal Transport of Microplastics on Rough Surfaces.” <em>Microplastics and Nanoplastics</em>. <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a>.</p> <p>R Core Team. 2020. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</p>
Dataset for publication Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718
<p>Dataset consisting of G-code for 3D mortar specimen's printing path and particle size distribution (Origin file) of materials used in the study Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718. <a href="https://doi.org/10.1016/j.jobe.2021.102718">https://doi.org/10.1016/j.jobe.2021.102718</a></p>
Nanosized Cu-SSZ-13 and Its Application in NH3-SCR. Dataset related to publication. Version: 1
<ul> <li><strong>Data type</strong>: tabular data, experimental measurements.</li> <li><strong>Data format</strong>: plain text (.txt), Bruker data (.spc and .par), Origin data (.opju)</li> <li><strong>Origin of the data</strong>: <ul> <li>Experimental spectroscopic measurements</li> <li>Computer modelling</li> </ul> </li> <li>The data were generated by a Bruker EMXmicro X-band spectrometer purchased on 2008</li> <li><strong>Information on data collection and elaboration</strong>: After collecting the data from the spectrometer, they were processed and analysed by using the Easyspin toolbox. Therefore the plain text data obtained from Easyspin were elaborated on Origin software.</li> <li><strong>Availability of the tools used to elaborate the data:</strong> Easyspin (5.2.28) and Origin 2019 software are required to read part of the data. The former is an open-source MATLAB toolbox (link to download it: http://www.easyspin.org/). The latter requires a license. The link for acquiring the Origin license is: https://www.originlab.com/index.aspx?go=Purchase/PricingAndOrdering&pid=1780 </li> </ul>
Shares of Open Access journals charging publication fees per country
<p>The files contains information on the ten countries that publish the most Open Access journals and the share of Open Access journals charging their authors with article processing charges (APCs) per country. The data was gathered at June 14, 2014 using the Directory of Open Access Journals web interface.</p>
US Department of Energy funded publications from Astrophysics Data System
<p>The John G. Wolbach Library, in collaboration with the NASA Astrophysics Data System (ADS), has compiled this bibliography of United States Department of Energy (DOE) funded publications. Grants were identified within the ADS by regular expression matching against article full text. There are over 70,000 articles in the bibliography, with bibliographic information for each publication. This bibliography is being released to explore its possible usage.</p> <p>Please note:</p> <p>Papers may include arXiv preprints (as non-refereed, with arXiv bibcode) and their subsequent journal articles (as refereed, with journal bibcode) as separate and unlinked records</p> <p>This compilation is a snapshot in time for DOE grants and ADS papers</p> <p>The method of data collection does not differentiate between grants credited with supporting projects and those supporting individuals contributing to projects.</p> <p>Results are based on available metadata which may be incomplete. This may include incomplete records of grant identifiers, due to limitations within the ADS. However, all articles in the bibliography acknowledge the support of the DOE in some way.</p> <p>Results have not been individually verified</p> <p>All abstracts and articles in the ADS are copyrighted by the publisher, and their use is free for personal use only. For more information, please read the Terms and Conditions regulating usage of resources.</p> <p>This dataset contains bibliographic information only, no information on the grants themselves is included. A potential application of the data could be to link the bibliographic information to grant information.</p> <p><br /> Links</p> <p>Harvard-Smithsonian Center for Astrophysics John G. Wolbach Library http://www.cfa.harvard.edu/lib/information/about.html</p> <p>NASA ADS http://labs.adsabs.harvard.edu/</p> <p>USA Spending: information on US government spending, including the Department of Energy: http://www.usaspending.gov</p> <p>Grants.gov, more information on US government grants: http://www.grants.gov</p> <p>Department of Energy: http://energy.gov/</p>
studyforrest-data-phase2: First public release
<p>Extension of the dataset published in Hanke et al. (2014; doi:10.1038/sdata.2014.3) with additional acquisitions for 15 of the original 20 particpants. These additions include: retinotopic mapping, a localizer paradigm for higher visual areas (FFA, EBA, PPA), and another 2h movie recording with 3T full-brain BOLD fMRI with simultaneous 1000 Hz eyetracking.</p> <p>Only open-source software was employed in this study. We thank their respective authors for making it publicly available.</p> <p>Please follow good scientific practice by citing the most appropriate publication(s) describing the aspects of this datasets that were used in a study.</p> <p> </p>
SNSF Open Access Monitoring: Data Publications 2013-2014
<p>This spreadsheet contains supplemental data to the report: "Open Access to Publications: SNSF monitoring report 2013 - 2015" (http://doi.org/10.5281/zenodo.584131), where it was found that at least 39% of publications out of SNSF funded grants are either Gold or Green Open Access.</p> <p>The initial data is based on the reported publications to the Swiss National Science Foundation via the grant administration system mySNF as of <strong>11. September 2015.</strong> This data is also available as open data on p3.snf.ch. Please be aware, that grant beneficiaries can update the metadata to the reported publications, including the Open Access status anytime during and after the end of a grant.</p> <p>Prior to validation, the actual publication date of publications with “in press/accepted” status was verified insofar as possible, and the details updated (approx. 3'000 publications). For further processing only publications with <strong>publication date 2013-2014</strong> were considered. Publications with the status “in press/accepted” status were excluded. Duplicates, i.e. publications cited as output for more than one project (approx. 2'500) are also filtered out. Where identifiable as such, pure abstracts (e.g. in journal supplements), working papers and pre-prints were also excluded.</p> <p><br> The basic goal was to locate a DOI for the publication and update the metadata. 78% of the 17'420 publications scrutinised have in the meantime been assigned a DOI. Using the DOI and the linked metadata available in CrossRef, unique searches could be performed in additional sources such as DOAJ, Pubmed and Pubmed Central, OpenAIRE (repository aggregator) and ADS (Astrophysics Data System). After the initial search for open access full text in these external sources, a search was made in Google Scholar using the DOI of the remaining publications still to be validated and links to any open access full text were extracted. Where even the Google Scholar search failed to turn up open access full text, the status was set to “closed access”. The OA status of 4'212 publications from the period 2013 to 2014 remained unclear, but it is more likely that they are in closed rather than open access mode.</p>
Austrian Science Fund (FWF) Publication Cost Data 2015
<p>Following the approach for the datasets in 2013 (http://dx.doi.org/10.6084/m9.figshare.988754) and 2014 (https://dx.doi.org/10.6084/m9.figshare.1378610.v14), the Austrian Science Fund (FWF) is making the publication costs spent in 2015 (esp. for Open Access) publically available.</p> <p>The dataset includes payments for publications of authors funded by the Austrian Science Fund (FWF) via following programmes:</p> <p>"Peer-Reviewed Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/</p> <p>"Stand-Alone Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</p>
Austrian Science Fund (FWF) Publication Cost Data 2013
<p>Following the Wellcome Trust, the Austrian Science Fund (FWF) makes its publication costs spent in 2013 (esp. for Open Access) publically available.</p> <p>An aggregated version of this dataset is already published in FWF's annual report 2013 (p. 86), http://www.fwf.ac.at/de/public_relations/publikationen/jahresberichte/fwf-jahresbericht-2013.pdf</p> <p>The dataset includes payments for journal articles of authors funded by the Austrian Science Fund (FWF) via the program ‘Peer Reviewed Publications’, http://www.fwf.ac.at/en/projects/peer-reviewed_publications.html</p> <p>The dataset distinguishes between three publication cost categories:</p> <p>(1) Gold Open Access and (2) Hybrid Open Access are defined by FWF's Open Access Policy, see: http://www.fwf.ac.at/en/public_relations/oai/index.html</p> <p>(3) ‘Other publication costs’ are defined as additional costs to subscription prices charged by subscription journals (i.e. submission fees, colour charges, page charges, figure charges, table charges, supplemental charges).</p> <p>Based on this data and the data of Web of Science (WoS), we roughly estimate for 2013 a share for Gold and Hybrid OA of around 33% of all articles (incl. reviews) that result from projects supported by the FWF and which are listed in Web of Science.</p> <p>The analysis of this dataset together with the report “Developing an Effective Market for Open Access Article Processing Charges” (http://www.wellcome.ac.uk/About-us/Policy/Spotlight-issues/Open-access/Guides/WTP054773.htm) will lead to an adaption of the FWF’s Open Access policy within the next months. One model, for example, for a cost-neutral Hybrid Open Access is already in place: http://ioppublishing.org/newsDetails/Austria-open-acces</p>
Data and modeling results for publication: Landscape genetics indicate recently increased habitat fragmentation in African forest-associated chafers
<ul> <li>DNA sequences: <em>cox1</em> and ITS1 alignments</li> <li>spatial records (in hypervolume archive)</li> <li>spatial principal component 1-3 used for <em>hypervolume</em> models (in hypervolume archive)</li> <li>Present and past species distribution models (SDMs): <ul> <li><em>biomod2</em> ensemble SDMs <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li><em>biomod2</em> SDMs for single PMIP3 models <ul> <li>Present</li> <li>Holocene Altithermal</li> <li>Last Glacial Maximum</li> </ul> </li> <li><em>hypervolume</em> SDMs</li> </ul> </li> <li>landscape connectivity models <ul> <li>circuitscape (for F0, F1, and F2)</li> <li>least cost corridors and paths (for F0, F1, and F2)</li> </ul> </li> </ul>
Facts Extracted from EuPMC OA publications 2016-06-01 - 2016-06-05
<p>Named entities were extracted from the fulltext Open Access articles in EuropePMC by loading the text into ElasticSearch and querying with the content of the ContentMine dictionaries (http://github.com/ContentMine/dictionaries)</p>
CIG Publications 2010-2015 Dataset
<p>In the geosciences as in other scientific areas, computation has become a core component of research, complementing field observation, laboratory analysis, experiment, and theory. Computational tools for data analysis, mapping, visualization, modeling, and simulation are essential for all aspects of the scientific workflow. Specialized scientific software is often developed by geoscientists for their own use, and this effort represents a distinctive intellectual contribution that can be on par with data collection and publications. Thus it is important to be able to properly attribute this effort, both to assign credit to the software authors and contributors, and to establish the provenance of software, promote its reuse, and support efforts to ensure reproducibility of scientific results.<em> </em>Drawing on a geoscience community that focuses on developing and disseminating scientific software, we assess the current practices of software attribution.</p> <p>The recent citation practices of the Computational Infrastructure for Geodynamics (CIG), geodynamics.org, community were assessed by examining its publication list from 2010-2015 [Article List.csv]. In addition, for all CIG held codes, we examine the citation requests by developers in their user's manual [Citation Request.CSV].</p> <p>Please see README.rtf for data descriptions</p>
Dataset for the `Performance of the EUDET-type beam telescopes' publication (EPJ TI)
<p>This repository holds the data on which the publication `Performance of the EUDET-type beam telescopes' is based on.</p> <p>The substructure is as follows:</p> <p>Files in the range of run000060.raw until run000112.raw belong to a geometry with plane distance dz = 150 mm.<br> Files in the range of run000114.raw until run000171.raw belong to a geometry with plane distance dz = 20 mm.<br> A run corresponds to a doublet of beam energy and sensor threshold settings. The numbers are detailed in the README file.</p>
PsPM-BAER: Skin conductance fluctuations in a public speaking paradigm with a repeated-measures design
<p>This dataset contains four skin conductance response (SCR) measurements of 60 s duration from each of 40 healthy male university students (18-35 years) who participated in a public speaking anticipatory anxiety paradigm with a repeated-measures factorial design.</p>
PsPM-NK: Skin conductance fluctuations in a public speaking paradigm with a between-subjects design
<p>This dataset includes four skin conductance response (SCR) measurements of 60 s duration for each of 32 healthy female university students (19-29 years) who participated in a public speaking anticipatory anxiety paradigm with a between-subjects design. Half of the participants were to deliver a public speech, and the other half a speech without an audience.</p>
Data of the publication: Nuclear spin coherence properties of 151Eu3+ and 153Eu3+ in a Y2O3 transparent ceramic by J. Karlsson et al.
<p>Data corresponding to the figures of the publication "Nuclear spin coherence properties of 151Eu3+ and 153Eu3+ in a Y2O3 transparent ceramic" by J. Karlsson et al., (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Data of the publication: Optical Line Width Broadening Mechanisms at the 10 kHz Level in Eu3+:Y2O3 Nanoparticles by J.G. Bartholomew et al.
<p>Data corresponding to the figures of the publication "Optical Line Width Broadening Mechanisms at the 10 kHz Level in Eu3+:Y2O3 Nanoparticles" by J.G. Bartholomew et al. (https://doi.org/10.1021/acs.nanolett.6b03949). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </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>
Protein Structure Initiative Publications, 2000-2016
<p>These files contain the full list of the 2313 publications and book chapters written as part of the Protein Structure Initiative, from 2000-2016. This data was collected from PubMed and by manual entry by the PSI Structural Biology Knowledgebase's Publication Portal, managed by Wladek Minor at University of Virginia. These files were created at the end of the PSI project on June 30, 2017.</p> <p>The references are provided as lists in two formats:</p> <ul> <li>in CSV (comma-separated variables) format that can be read in Excel or other spreadsheet application, or</li> <li>an Endnote Library Import file (store-endnote-pubs). To import this library into Endnote, select File --> Import... and then under Options, select the Import Option "Endnote Library Import". Then this text file will be processed and loaded into the library.</li> </ul> <p>--created by the Structural Biology Knowledgebase, July 5, 2017. (sbkb.org)</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.