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.
50
datasets available to search
ShareScore release 0.7.1
Dataset results
50 results for “Raw data for publication”
Raw data collection for the publication P. Pötschke, T. Villmow, B. Krause and B. Kretzschmar, Influence of Twin-screw Extrusion Conditions on MWCNT Length and Dispersion and Resulting Electrical and Mechanical Properties of Polycarbonate Composites
<p>This data collection contains the raw data for the publication <br>Petra Pötschke, Tobias Villmow, Beate Krause and Bernd Kretzschmar, Influence of Twin-screw Extrusion Conditions on MWCNT Length and Dispersion and Resulting Electrical and Mechanical Properties of Polycarbonate Composites, <strong>polymers </strong>2024, 16(19), 2694. <a href="https://doi.org/10.3390/polym16192694">https://doi.org/10.3390/polym16192694</a></p> <p>The data are sorted according to the figures and tables in which they are used.</p> <p>The description in Table 1 is taken from the reference:<br>Villmow, T.; Kretzschmar, B.; Pötschke, P. Influence of screw configuration, residence time, <br>and specific mechanical energy in twin-screw extrusion of polycaprolactone/multi-walled carbon nanotube composites. Compos. Sci. Technol. 2010, 70, 2045-2055. doi: https://doi.org/10.1016/j.compscitech.2010.07.021.</p> <p>Figure 15 is adapted from the references:<br>Krause, B.; Boldt, R.; Pötschke, P. A method for determination of length distributions of multiwalled carbon nanotubes before and after melt processing. Carbon 2011, 49, 1243-1247, https://doi:10.1016/j.carbon.2010.11.042.<br>and<br>Liebscher, M.; Domurath, J.; Krause, B.; Saphiannikova, M.; Heinrich, G.; Pötschke, P. Electrical and melt rheological characterization of PC and co-continuous PC/SAN blends filled with CNTs: Relationship between melt-mixing parameters, filler dispersion, and filler aspect ratio. Journal of Polymer Science Part B: Polymer Physics 2018, 56, 79-88, https://doi:10.1002/polb.24515.<br>The data are reused with permissions. </p> <p>The raw data (TEM images) used for the calculation of the carbon nanotube length distributions and mean carbon nanotube length values shown in Figs. 4, 11, 13, 16, and 17 are publically available at: Krause, B. (2024). Transmission electron microscopy (TEM) images of multiwalled carbon nanotubes (MWCNT) detached from polycarbonate (PC) composites [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11400466</p> <p>Version v2:</p> <p>Compared to the submitted figure, in the final version in<strong> Fig. 9 </strong>the sample using the side feeder (PC-H-05) was removed and two samples extruded at 15 kg/h and 750 rpm (PC-H-25) and 1000 rpm (PC-H-26) were added. In the text-file the unit of GPa for the elastic modulus was corrected to MPa. </p> <p>Compared to the submitted Table, in the final version of <strong>Table 2 </strong>the electrical resistivity values were given in more detail including the standard deviation. The column title of sigma break was changed to sigma max, which is more correct for these stress-strain diagrams.</p> <p>Compared to the submitted Table, in the final version of <strong>Table 3</strong> the column title of sigma break was changed to sigma max, which is more correct for these stress-strain diagrams.The title of the first column was set to "screw" instead of "feeding". </p>
Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.
<p><strong>If you use the data or the analysis pipeline, please refer to:</strong></p> <p>Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00006">https://doi.org/10.3389/fnins.2018.00006</a></p> <p><strong>and/or</strong></p> <p>Andersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00261">https://doi.org/10.3389/fnins.2018.00261</a></p> <p><strong>IMPORTANT</strong><br> Version 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p> <p><br> </p> <p>Dataset with tactile expectations to be analysed with pipelines for either <a href="https://mne.tools/stable/index.html">MNE-Python</a> or <a href="http://www.fieldtriptoolbox.org/">FieldTrip</a>, aiming to follow the MEG-BIDS structure</p> <p><br> <strong>Unzipping the data</strong></p> <p>Data is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called "analyses", which has a sub-directory called "omission_frontiers_BIDS-FieldTrip", which has a sub-directory called "data".<br> Thus, as an example, in my case, I should have the path: /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/data</p> <p><strong>Path:</strong><br> on a Linux system the path would be /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a macOS system the path would be /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a Windows system the path would be C:\Users\your_name\analyses\omission_frontiers_BIDS-FieldTrip\data</p> <p><strong>Steps for unzipping:</strong></p> <p>1. Set up the folder above according to your operating system, following the examples above and substitute "your_name" for your user name.<br> 2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/</p> <p>Now you are ready to run the analyses.</p> <p><br> <strong>The MEG data</strong></p> <p>Raw fif files are contained in the data folder, ordered by subject (n=20)<br> There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recording</p> <p><strong>Processed MRI data </strong></p> <p>For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects' identity</p> <p>For Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG data<br> For MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.</p> <p><br> <strong>Metadata</strong></p> <p>Each subject has a number of tsv-files:<br> *channel.tsv contain information about the channels in that recording<br> *events.tsv contain information about the events in that recording<br> removed_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)<br> ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)<br> *scans_tsv contain information about the scans conducted</p> <p><br> <strong>Scripts </strong></p> <p>Please see Github for the updated scripts at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p>
Raw data for publication: Weathering of Wood Modified with Acetic Anhydride – Physical, Chemical, and Aesthetical Evaluation
<p>Color.xlsx</p> <p>This file contains CIE Lab* color coordinates measured on the surface of wood samples.</p> <p>Weather data.xlsx</p> <p>This file contains daily local weather conditions in San Michelle, Italy during the natural weathering test.</p> <p>Gloss.xlsx</p> <p>This file contains the gloss value measured on the surface of wood samples.</p> <p>contact angle and surface energy.xlsx</p> <p> This file contains dynamic contact angle and surface energy measured on the surface of wood samples.</p> <p>TGA.xlsx</p> <p>This file contains the Thermogravimetric (TG) and derivative thermogravimetric (DTG) data of the wood samples.</p> <p>Surface roughness.xlsx</p> <p>This file contains the roughness value of wood samples.</p>
The metabolomics raw data and a supporting statistical analyses data set for publication: Metabolomic analysis revealed the absence of the principal antimicrobial compound of Pseudomonas donghuensis P482, 7-hydroxytropolone, under restricted nutrient conditions.
<p><a href="../api/records/11220997/draft/files/Metabolomic%20analyses%20raw%20files.zip/content" target="_blank" rel="noopener noreferrer">Metabolomic analyses raw files</a>, Compounds analyses, Hierarchical Condition tress and PCA Scores are uploaded.</p>
Raw data and data accompanying publication: https://doi.org/10.1016/j.antiviral.2024.105946
<p>Raw data and data accompanying publication:</p> <p>Baliga-Gil, A., Soszynska-Jozwiak, M., Ruszkowska, A., Szczesniak, I., Kierzek, R., Ciechanowska, M., Trybus, M., Jackowiak, P., Peterson, J.M., Moss, W.N., Kierzek, E., Targeting sgRNA N secondary structure as a way of inhibiting SARS-CoV-2 replication, Antiviral Research. (2024) 228, 105946. https://doi.org/10.1016/j.antiviral.2024.105946</p>
Associated raw data to the publication: An accurate and efficient camera-based indoor positioning approach for intralogistic environments (MHCL 2015)
<p>This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in the publication "An accurate and efficient camera-based indoor positioning approach for intralogistic environments" at MHCL 2015 conference (see https://mediatum.ub.tum.de/1286589 and http://www.fml.mw.tum.de/fml/images/Publikationen/MHCL_2015_jung_submitted.pdf). Originally these files were recorded and used as uncompressed 8-bit grayscale bitmaps. The images were losslessly compressed to png files in order to reduce the test set file size (by approx. factor 3.5)</p>
Dataset (raw questionnaire data and variable importance results) supplementing the publication "Does Gender Really Matter? How Demographics and Site Characteristics Influence Behavior and Attitudes of German Small-Scale Private Forest Owners"
<p>The dataset contains</p> <ul> <li>a translation of the questionnaire,</li> <li>the questionnaire raw data, and</li> <li>the results of the variable importance analysis</li> </ul> <p>used in the publication "Does Gender Really Matter? How Demographics and Site Characteristics Influence Behavior and Attitudes of Small-Scale Private Forest Owners".</p>
Raw data for the publication entitled -"Local disorder in calcined kaolinitic clays for pozzolanic early-age reactivity understanding: A Synchrotron Pair Distribution Function study-"
<p>Raw data for the publication entitled -"Local disorder in calcined kaolinitic clays for pozzolanic early-age reactivity understanding: A Synchrotron Pair Distribution Function study-". This includes: raw data for calorimetry, SXRPD,LXRPD,NMR and PSD.</p>
Raw Data for the publication of 'Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals'
<p>The dataset used for the ML model described in <strong>Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals. </strong></p> <p> </p>
Raw data of the publication titled "First-row d6 metal complex enables photon upconversion and initiates blue light-dependent polymerization with red light"
<p>Raw data of the publication in Angew. Chem. Int. Ed. titled "First-row d<sup>6</sup> metal complex enables photon upconversion and initiates blue light-dependent polymerization with red light "</p>
Raw data for publication: Cao et al. 2023. GCB-Bioenergy (accepted for publication).
<p>Raw data for publication: Viet Dang Cao, Baskaran Kannan, Guangbin Luo, Hui Liu, John Shanklin, and Fredy Altpeter<span>. </span>2023. Triacylglycerol, total fatty acid and biomass accumulation of metabolically engineered energycane grown under field conditions. GCB-Bioenergy (accepted for publication).</p>
Raw data of the publication ''Radical' differences between two FLIM microscopes affect interpretation of cell signaling dynamics'
<p>Raw data and data used to create the figures.</p>
Raw data to a publication in Functional Ecology
<p>Raw data to a publication in Functional Ecology called <span>Nutrient exchange within common mycorrhizal networks is altered in a multi-species environment </span>created by Veronika Řezáčová, Joanna Weremijewicz and Tereza Michalová.</p>
Raw Data for Publication "Earth observations reveal impacts of climate variability on maize cropping systems in Sub-Saharan Africa"
<p>Phenological metrics extracted for all agricultural fields used in the study. Data also includes the coordinates of the fields.</p>
Raw EEG Data Publication "Embodying the camera"
<p>Raw data files of 16 subjects recorded during experiment as described in article: Heimann, K., Uithol, S., Calbi, M., Umiltà, M.A., Guerra, M., Fingerhut, J., Gallese, V. (submitted to PLOSONE) <strong>Embodying the camera: an EEG study on the effect of camera movements on film spectators´ sensory-motor cortex activation". </strong></p> <p>event trigger value for start of videos = stim, indices for still, zoom and steady condition in 9.xls file (1=still, 2=zoom, 3=steady)</p> <p>event trigger value for slide announcing action execution =ceck (response = resp)</p> <p>Data of further analysis steps available at request to katrinheimann@cas.au.dk</p> <p> </p> <p> </p>
Raw and processed data for publication "Quantitative microscopy reveals dynamics and fate of clustered IRE1alpha"
<p>This dataset contains all raw and processed data for our PNAS paper titled "Quantitative microscopy reveals dynamics and fate of clustered IRE1alpha". Using the associated analysis code (10.5281/zenodo.3544482), all figures in the paper can be reproduced from the data in this upload.</p> <p>Abstract of the paper is included below:</p> <p>"The endoplasmic reticulum (ER) membrane-resident stress sensor IRE1 governs the most evolutionarily conserved branch of the unfolded protein response. Upon sensing an accumulation of unfolded proteins in the ER lumen, IRE1 activates its cytoplasmic kinase and ribonuclease (RNase) domains to transduce the signal. IRE1 activity correlates with its assembly into large clusters, yet the biophysical characteristics of IRE1 clusters remain poorly characterized. We combined super-resolution microscopy, single-particle tracking, fluorescence recovery and photoconversion to examine IRE1 clustering quantitatively in living human and mouse cells. Our results revealed that (1) by contrast to qualitative impressions gleaned from microscopic images, IRE1 clusters comprise only a small fraction (~5%) of the total IRE1 in the cell. (2) IRE1 clusters have complex topologies that display features of higher-order organization. (3) IRE1 clusters contain a diffusionally constrained core, indicating that they are not phase-separated liquid condensates. (4) IRE1 molecules in clusters remain diffusionally accessible to the free pool of IRE1 molecules in the general ER network. (5) When IRE1 clusters disappear at later timepoints of ER stress as IRE1 signaling attenuates, their constituent molecules are released back into the ER network and not degraded. (6) IRE1 cluster assembly and disassembly are mechanistically distinct. (7) IRE1 clusters’ mobility is nearly independent of cluster size. Taken together, these insights define the clusters as dynamic assemblies with unique properties. The analysis tools developed for this study will be widely applicable to investigations of clustering behaviors in other signaling proteins."</p>
ingewortel/2022-listeria-goblets: code & raw data files for publication
<p>Repository with code and raw data at the stage of publication. The repository contains mostly code, but also some raw data files used to generate the data figures of the manuscript. The repository is self-sufficient to reproduce all the simulations and data analyses performed. See the README files for further details.</p>
Raw data belonged to the study of CV-SDG publications
<p>Raw data belonging to the study of CV-SDG publications and countries</p>
Raw data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors"
<p>Raw survey data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors".</p>
Raw data for publication titled " GaN buffer growth temperature and efficiency of InGaN/GaN quantum wells: The critical role of nitrogen vacancies at the GaN surface"
<p>Raw data (Time-resolved photoluminescence and Secondary Ion Mass Spectrometry) used for the publication: <a href="https://doi.org/10.1063/5.0040326">https://doi.org/10.1063/5.0040326</a></p> <p>Layer sequence of each sample could be found in the excel sheet named SampleLibrary</p> <p> </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.