Skip to main content
Powered by ShareScore

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.

42

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

42 results for “behaviour systems”

Learn how ShareScore rates datasets ↗
zenodo48/100

European consumers ́ preference and willingness to pay for food products labelled as obtained by a circular farming system -in relation to environmental attitudes and consumption behaviours

<p>Data was collected with questionnaire-based research carried out in Belgium, Croatia, Hungary, Italy, Poland, and Spain as part of a European project. The survey questions were designed to obtain the Willingness to pay using 2 different methodologies the discrete choice experiment and the open-end choice experiment. The survey also included questions about consumers environmental attitudes, and consumption behavior (purchase, use and recycling), to identify if them have influence on preferences towards more sustainable food products. The 3 analyzed food products were pork meat, milk and bread, all of them obtained through different agricultural production systems (circular, conventional, and organic agriculture). The sample was stratified in terms of gender and age to be representative to the average population in each country. Furthermore, respondents included in this study were those that are mainly, or in part responsible for the household food shopping. The questionnaire was translated to the languages of the countries involved in the data collection and pre-launched using a pilot sample of 50 consumers in each case study country. &nbsp;Finally, a total of 5,362 validated questionnaires were obtained. Data was collected online using the Qualtrics market research company, and Net panel market company for Hungary from June 2021 to January 2022.</p>

opencc-by-4.0Sep 2023View details →
zenodo44/100

Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis

<p>Underlying dataset and analysis tests of the results described in the article &quot;Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis&quot;.</p>

opencc-by-4.0Feb 2023View details →
zenodo40/100

Data and models for "Modelling human behaviour in cognitive tasks with latent dynamical systems"

<p>Ebb and Flow gameplay data and trained model parameters for:</p> <p>Jaffe, P.I., Poldrack, R.A., Schafer, R.J. &amp; Bissett, P.G.<em> </em>Modelling human behaviour in cognitive tasks with latent dynamical systems.&nbsp;<em>Nat Hum Behav</em>&nbsp;(2023). https://doi.org/10.1038/s41562-022-01510-8&nbsp;</p> <p>Ebb and Flow is a task-switching game offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The data and model parameters are organized by participant/model in individual archived directories&nbsp;(140 participants; 245&nbsp;models). Within each model directory, &ldquo;data_pre_split.pickle&rdquo; contains the raw Ebb and Flow data. The processed model inputs for the training, validation, and holdout/test splits are contained in the files "train_model_inputs.pt", "val_model_inputs.pt", and "test_model_inputs.pt", respectively. Other metadata associated with each split is contained in "train_other_data.pkl", "val_other_data.pkl", and "test_other_data.pkl". The parameters from the trained model are stored in &ldquo;model_params.pth&rdquo;. Some intermediate analysis products are contained in the subfolder &ldquo;model_analysis&rdquo;.</p> <p>Metadata for all models can be found in &ldquo;model_metadata.csv&rdquo;. The metadata field &ldquo;switch_cost_type&rdquo; identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked &ldquo;NA&rdquo;, except for the optimal models, were also trained on data with a switch cost but were not included in the paired comparison of the sc+ and sc- models; see manuscript for details). The "exgauss" field identifies models that were trained with an exGaussian response template (coded as "exgauss+"); models identified as "exgauss-" were trained with a Gaussian kernel and were used in paired comparisons with the exgauss+ models. The "early" field identifies models that were trained with early-stage practice data if set to TRUE. The "optimal" field identifies models that were trained to perform the task optimally if set to TRUE. The other metadata fields are self-explanatory.</p> <h2><strong>Fast command line download instructions (macOS/linux)&nbsp;</strong></h2> <p>For help downloading on Windows, see <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a>.<strong><br></strong></p> <p>1) Copy and save the complete list of files below to a text file, e.g. "files.txt". Save it to the same directory you would like to save the data to.&nbsp;</p> <p>2) Install parallel if it's not already installed:</p> <pre><code>sudo apt-get install parallel</code></pre> <p>3) Run the following from the directory with files.txt (all data will be saved here). The flag -jN will create N parallel wget instances to download the files, e.g.:</p> <pre><code>cat files_test.txt | parallel -j8 wget {}</code></pre> <p>4) Unzip the files and cleanup:</p> <pre><code>unzip "*.zip" rm *.zip files.txt</code></pre> <h2><strong>List of files</strong></h2> <p>https://zenodo.org/records/7102065/files/ages80to89_u4120_exgauss.zip<br>https://zenodo.org/records/7102065/files/optimal_square9.zip<br>https://zenodo.org/records/7102065/files/optimal_square8.zip<br>https://zenodo.org/records/7102065/files/optimal_square7.zip<br>https://zenodo.org/records/7102065/files/optimal_square6.zip<br>https://zenodo.org/records/7102065/files/optimal_square5.zip<br>https://zenodo.org/records/7102065/files/optimal_square4.zip<br>https://zenodo.org/records/7102065/files/optimal_square3.zip<br>https://zenodo.org/records/7102065/files/optimal_square2.zip<br>https://zenodo.org/records/7102065/files/optimal_square1.zip<br>https://zenodo.org/records/7102065/files/optimal_square10.zip<br>https://zenodo.org/records/7102065/files/model_metadata.csv<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5150_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4239_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4120_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3701_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1887_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1447_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u975_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4864_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3898_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3538_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3509_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3509_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3457_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3457_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3218_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3218_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3143_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3143_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2266_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2266_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u21_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2141_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2141_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2022_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u1276_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u1276_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u627_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5459_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5459_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5069_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5069_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4993_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4993_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4964_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4852_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4852_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u478_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u438_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u438_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u438_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u388_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u388_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3724_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3724_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3469_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3461_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3461_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3461_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3328_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3328_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3066_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3066_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u268_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u2490_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u2186_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u2186_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u1597_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u1597_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3969_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u1194_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u697_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u697_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u697_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u5128_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u5128_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4609_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u452_expt2.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u452_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4220_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4150_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4107_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3536_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3316_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3316_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3199_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3195_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2584_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2584_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2347_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2347_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2247_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u195_expt2.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u195_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u195_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1917_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1917_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1590_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1434_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1434_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1325_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1325_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1027_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u970_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u734_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u734_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u734_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u660_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u4878_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u4487_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3887_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3574_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3233_expt2.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3233_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3055_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2772_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2771_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2771_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2492_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2492_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2482_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2482_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2166_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1892_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1739_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1739_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1614_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1614_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1468_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1468_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1468_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1462_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1462_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1397_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u98_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u878_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u878_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u5155_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u5155_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4851_expt2.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4851_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4496_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4367_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4367_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u418_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u418_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4039_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4024_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3873_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3859_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3859_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3859_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3850_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3850_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3531_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3365_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2856_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2280_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2106_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2106_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1495_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1387_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1248_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1248_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u953_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u953_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u890_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u5396_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u5326_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4587_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4587_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4504_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4504_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4394_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4394_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3975_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3975_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3750_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u367_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3365_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3257_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3257_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3139_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u2809_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u2360_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u224_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1559_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1474_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1444_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_early.zip</p> <p>&nbsp;</p>

opencc-zeroSep 2022View details →
zenodo40/100

Review of Recent Trends in Measuring the Computing Systems Intelligence-igure 2. Intelligence of different living creature (accessed 01.11.2017). 2.1. A crow solving a complex task (https://www.disclose.tv/spooky-genius-crow-had-to-be-removed-from-scientific-experiment- 314886). 2.2. A group of dolphins with a social behaviour (http://www.sciencemag.org/news/2012/04/teamwork-builds-big-brains); 2.3. An orangutan that use a spear to fish (https://primatology.net/2008/04/29/orangutan-photographed-using-tool-as-spear-to-fish)

<p>Some species of birds have been shown capable of using different tools. Many studies consider the crows as very intelligent. Smirnova, Lazareva, and Zorina (2000) suggested that crows have some kind of numerical ability. Figure 2.1 presents a crow that uses a tool, a small stone in order to catch a worm from a glass of water.The dolphins in many studies are considered intelligent at the individual level. An advanced ability of dolphins is the self-awareness. Marten and Psarakos (1995) presented an interesting study based on self-view television to distinguish between self-examination and social behavior in the Bottlenose dolphin. The most well-known abilities of dolphins are to teach, learn and cooperate. Dolphins have a complex communication and social behaviour. Figure 2.2 presents the image of a common group of dolphins. Some studies prove that primates are one of the most intelligent in the class of animals (Reader, Hager, &amp; Laland, 2011). Orangutans are one of the most intelligent primates. The ability of orangutans to use different types of tools in order to perform tasks is well-known. Figure 2.3 presents an orangutan that uses a spear to catch fish. The orangutans can be considered intelligent at individual level.</p>

opencc-by-4.0Apr 2018View details →
zenodo40/100

Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 1. Intelligence of different simple living creature (accessed 01.11.2017). 1.1. A carnivorous plants catching an insect (https://phys.org/news/2016-05-colombia-peace-reveal-jungle-species.html); 1.2. A colony of ants solving a very complex task (https://mappingignorance.org/2016/05/27/rafting-ants); 1.3. The collective behaviour of a school of fish (https://simple.wikipedia.org/wiki/Shoaling_and_schooling)

<p>The biological intelligence of different life forms, ranging from very simple (such as plants) to very complex (such as humans) is the subject of many studies and a large amount of research. Frequent studies related to different kind of biological intelligence include: the intelligence of horses (Krueger, &amp; Heinze, 2008; Krueger, Farmer, &amp; Heinze, 2014; Schuetz, Farmer, &amp; Krueger, 2016), intelligence of pigs (Broom, Sena, &amp; Moynihan, 2009), intelligence of dogs (Coren, 1995), intelligence of primates (Reader, Hager, &amp; Laland, 2011) and so one. Figures 1, 2, and 3 present some biological life forms that are frequently considered intelligent. Trewavas (2002; 2005) considered that plants intelligence should be based on principles such as their ability to adjust their morphology, and phenotype accordingly to ensure self- preservation and reproduction. Figure 1.1 presents an intelligent plant (carnivorous) that uses a strategy for catching very fast flying insects. In order to eat the insect, it makes a movement. Figure 1.1 presents the catching of an insect by a carnivorous plant. The intelligence of colonies of ants, termites and other insects that live in large colonies is considered at the colony level (Brady, Fisher, Schultz, &amp; Ward, 2014; Johnson, Borowiec, Chiu, Lee, Atallah, &amp; Ward, 2013). Figure 1.2 presents the coherent intelligent surviving behaviour of a colony of a species of ants. The ants make a structural reorganization in order to move on the surface of the water. Figure 1.3 presents a very large school of fish with an intelligent coherent collective feeding and self-protecting behaviour. Each individual fish has a very simple behavior. Based on this it cannot be considered intelligent. The intelligence in large schools of fish emerges at the collective level (Shaw, 1978; Parrish, Viscedo, &amp; Grunbaum, 2002).</p>

opencc-by-4.0Apr 2018View details →
zenodo36/100

Behaviour-based large-scale planning Fleet Management System Evaluation

<p>This dataset was generated within the SafeLog project while evaluating the performance of a decentralized Fleet Management System (MARS). MARS attempts to find a solution to the multi-agent path finding problem in a decentralized manner, by representing each component as an autonomous agent. We compared the results of a large set of routing assignments within a benchmarking environment to a centralized planner, with respect to the goodness of the resulting routes as well as the computation time. The results can be found in the &quot;MARS&quot; directory within the dataset.</p> <p>We further investigated ROS 2 as a potential framework for an upcoming iteration of the software. To that end, an abstraction of the architecture was built in ROS and ROS 2, in order to compare the communication overhead in the context of a very large number of nodes. The results of this can be found in the &quot;ROS&quot; directory.</p> <p>&nbsp;</p> <p>Please refer to the README.md for further explanation.</p>

opencc-by-4.0Jan 2021View details →
zenodo36/100

Research data supporting: "Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems"

<p>Contains the relevant simulation data and input files. See "readme.txt" for information.</p>

opencc-by-4.0Oct 2024View details →
zenodo36/100

On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models

<p>In this article, a local Linear Parameter Varying (LPV) model identification approach is exploited to analyze the dynamic behaviour of a structure whose dynamics varies over time. This structure is composed by two aluminum crosses connected by a rubber mount. To observe time-dependent variations on the dynamics of this assembly, it is placed in a climate chamber and submitted to a six minute temperature run-up. During this run-up the structure is continuously excited by a shaker. The load provided by this device is measured by a load cell, while six accelerometers are measuring the responses of the system. The temperatures of the air inside the climate chamber and at the surface of the mount are also continuously measured. It is found that during the performed temperature run-up, the rubber mount temperature increased from, roughly, 14℃ to, approximately, 35.2℃. By using the measured load provided by the shaker and the measured accelerations, Frequency Response Functions (FRFs) at five different rubber mount temperatures are computed. From each of these sets of FRFs, state-space models are estimated. Afterwards, these models are used to define an interpolating LPV model, which enables the computation of interpolated state-space models representative of the dynamics of the system at each time sample. It is found that by feeding the interpolated state-space models with the measured load, an accurate simulation of the measured accelerations is obtained. Moreover, by exploiting a joint input state estimation algorithm with the interpolated state-space models and with the measured accelerations, a very good prediction of the applied load can be obtained. It is also shown that if the time dependency of the dynamics of the system is ignored, the results are less accurate.</p>

opencc-by-4.0Apr 2024View details →
dryad36/100

Interactive effects of arrival date, territory quality and male polyterritorial behaviour on the mating system of the sedge warbler Acrocephalus schoenobaenus – a path analysis

<p>Classical models predict that male fitness is based on resources monopolized and invested in reproduction, and/or on individual quality providing offspring with sexually attractive traits or viable genes. However, these factors are frequently correlated, making their relative influence on male fitness difficult to describe and quantify. We analysed the relative influence of the main features of the sedge warbler's (<em>Acrocephalus schoenobaenus</em>) breeding system, i.e. age, arrival date, territory quality, male sexual activity (song and polyterritorial behaviour), on males' mating success, fledging success and local recruitment. Results show that this species' breeding system involves three main paths: (1) earlier-arriving males have higher mating success, regardless of territory quality, (2) the quality of territories pre-empted by earlier males directly influences recruitment, and (3) mating success is influenced by male sexual activity (polyterritorial behaviour), but an additional territory also affects recruitment directly. As arrival date plays a significant role in all the paths, the whole system seems to depend on male quality; although the benefits accruing to male fitness are obtained not only through sexual selection but also through resource monopolization.</p>

opencc-zeroSep 2022View details →
dryad36/100

Linking animal behaviour and tree recruitment: Caching decisions by a scatter hoarder corvid determine seed fate in a Mediterranean agroforestry system

<p><span>1. Seed dispersal by scatter-hoarder corvids is key for the establishment of important tree species from the Holarctic region such as the walnut (<em>Juglans regia</em>). However, the factors that drive animal decisions to cache seeds in specific locations and the consequences of these decisions on seed fate are poorly understood. </span></p> <p><span>2. We experimentally created four distinct, replicated habitat types in a Mediterranean agricultural landscape where the Eurasian magpie (<em>Pica</em> <em>pica</em>) is a common scatter-hoarder: soft bare soil; compacted bare soil; compacted soil with a dense herbaceous cover; and soft linear bare soil made up of the irrigation furrows that separated the rest of the treatments. We also experimentally placed visual landmarks (stones, sticks, and bunches of dry plants) to test if magpies use them to place seed caches. Walnut dispersal from feeders to the habitats was monitored by radio-tracking and camera traps. </span></p> <p><span>3. A sowing experiment simulating natural caches tested the effect of caching type on seed germination and seedling emergence. Seed mass was controlled for the dispersal and sowing experiments.</span></p> <p><span>4. Magpies selected the two habitats with soft soil, and avoided the one with compacted soil, to cache nuts. Seed mass did not affect dispersal distance, germination, or emergence; however, heavier seeds were cached more often under litter and in the habitat with herbaceous cover, whereas lighter seeds were more often buried in the soft bare soil habitat. Seed burial under soil or litter determined seed fate, as there was virtually no emergence from unburied nuts. There was no evidence of any effect of the visual landmarks.</span></p> <p>5. Synthesis. The consequences of seed caching for seedling early establishment are driven by a fine decision-making process of the disperser. Magpies seemed to ponder the characteristics of the habitat and the seed itself to determine where and how to cache each nut. By doing so, magpies reinforced the quality of seed dispersal effectiveness, as they cached walnuts in locations that enhanced both seed survival and seedling emergence.</p>

opencc-zeroOct 2022View details →
dryad36/100

Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge

<p>The main problem in the operation of nuclear power plants is the scale formation of mineral impurities in an open recirculating system. However, water discharge from an open recirculating system into water bodies can lead to changes in the chemical equilibrium of wastewater components and requires constant monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts in water in an open recirculating system during water treatment using the example of the Rivne Nuclear Power Plant. Moreover, the analysis impact of their discharge with return water in the Styr River. The dissolved inorganic salt concentration has a significant impact on the efficiency of the system and the environment of an open recirculating system power plant. Altogether, each dissolved inorganic salt component was analysed separately using standard measurement methods, using statistical methods of data processing, and correlation analysis. In addition, the annual discharge of the dissolved inorganic salts components was calculated and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the influence of the formation of the dissolved inorganic salts and changes in their concentration value during the discharge of returned water into a natural water body was analysed.</p>

opencc-zeroJun 2024View details →
zenodo36/100

Supporting material for "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test"

<p>The data were uploaded to support the manuscript &quot;Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test&quot; for the submission to Journal of Pharmacological and Toxicological Methods.</p>

opencc-by-sa-4.0Oct 2017View details →
zenodo36/100

The influence of food web structure and foraging behaviour on visual system traits in a predatory freshwater fish

<p>Dataset used in the manuscript titled "The influence of food web structure and foraging behaviour on visual system traits in a predatory freshwater fish" Dataset includes lake trout visual system traits, body size, and food web structural attributes sampled from four different lakes in Algonquin, ON, Canada.</p>

opencc-by-4.0May 2024View details →
zenodo36/100

Dataset for "DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion model"

<p>This dataset contains both numerical simulation data and experimental data describing the swelling behaviour of binary particle systems. Detailed study on the swelling behaviour is presented in a journal paper entitled &quot;DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion mode&quot;. We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1-&nbsp;Evolution of the height of the particle bed obtained from experiments and simulations (used in Fig.6 of the associated paper): Experimental data for the measured hight and associated standard deviation at various time, as well as the numerical data.</p> <p>Dataset 2 -&nbsp;Evolutions of the normalised average radius r/r_0&nbsp;(used in Fig.7a) and the coefficient of variation CV (used in Fig.7b) for SAP particles obtained from DEM-CFD simulations.</p> <p>Dataset 3 - Radial distribution Functions (RDFs) for (Fig.9a) SAP particles and (Fig.9b) POM particles at different time instants.</p> <p>Dataset 4 -&nbsp;Positions of&nbsp;POM particles&nbsp;projected into the x-y plane at different time instants (the red dots are particle centres), (a) t = 0 min; (b) t = 3 min; (c) t = 6 min; (d) t = 10 min.</p> <p>Dataset 5 -&nbsp;Evolution of Lacey mixing index for the binary particles during the swelling process.</p> <p>Dataset 6 -&nbsp;Average normal stress distribution of particles in x-y and x-z planes at t = 2 min.</p> <p>DataSet 7 - &nbsp;Velocity vectors of particles projected in x-z planes at different time instants: (a) t = 1 min; (b) t= 2 min (Data for Fig.13)</p> <p>Dataset 8 - Positions of POM particles projected into x-y plane at time t= 10 min (the red dots are particle centres) with different coefficient of frictions (a) &mu;=0.1; (b) &mu;=0.2; (c) &mu;=0.3; (d) &mu;=0.4; (e) &mu;=0.5.(Data for Fig.15)</p> <p>Dataset 9 - RDFs for POM particles at time t= 10 min with different coefficients of friction.(Data for Fig.16)</p>

opencc-by-4.0Dec 2022View details →
dryad36/100

Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge

Open the record for dataset details and reuse information.

publicJun 2024View details →
dryad36/100

Interactive effects of arrival date, territory quality and male polyterritorial behaviour on the mating system of the sedge warbler Acrocephalus schoenobaenus – a path analysis

Open the record for dataset details and reuse information.

publicSep 2022View details →
dryad36/100

Linking animal behaviour and tree recruitment: Caching decisions by a scatter hoarder corvid determine seed fate in a Mediterranean agroforestry system

Open the record for dataset details and reuse information.

publicOct 2022View details →
dryad32/100

Data from: The mating behaviour of the seed shrimp Parapolycope spiralis (Ostracoda: Cladocopina), with insight into the evolution of mating systems in cryptic interstitial habitats

The mating behaviour of the interstitial ostracod Parapolycope spiralis Tanaka &amp; Tsukagoshi, 2010 is reported here for the first time. This species is adapted to an interstitial environment, and is typified by having tiny body size and being completely blind. In this paper it is presumed that the characteristics of the mating behaviour are typical for species in an interstitial environment. Observations by microscopy on mating behaviour in the laboratory showed various distinct stages: (1) the male captures the female by a sucker on the antennula; (2) the male clasps and maintains the mating position using the endopodite, the endopodite claw, and the exopodite of the antenna; (3) the male makes contact using the distal part of the upper lip; and (4) copulation takes place. These observations indicate that the sequence of stages of the mating behaviour of miniaturized animals is not necessarily always a simplified process. During the process, it seems that the male upper lip establishes physical contact stimuli with the female. Because the interstitial environment is cryptic and consists of a three-dimensional space with reduced connectivity, direct physical contact is likely to be one of the most effective and successful courtship behaviours.

opencc-zeroDec 2012View details →
dryad32/100

Data from: A high-resolution panorama camera system for monitoring colony-wide seabird nesting behaviour

1. Obtaining accurate and representative demographic metrics for animal populations is critical to many aspects of wildlife monitoring and management. However, at remote animal colonies, metrics derived from sequential counts or other continuous monitoring are often subject to logistical, weather and disturbance challenges.The development of remote camera technologies has assisted monitoring, but limitations in spatial and temporal resolution and sample sizes remain. 2. Here we describe the application of a robotic camera system (Gigapan) which takes a tiled sequence of photographs that are automatically stitched together to form high-resolution panoramas. We demonstrate the application of the Gigapan using data collected during field-testing at a shy albatross colony on Albatross Island in northwest Tasmania. 3. We took daily panoramas over five days to estimate mean incubation shift-duration, an indirect measure for foraging trip duration, in an existing study area. Similar numbers of occupied nests could be observed at a distance of ~100m in the Gigapan panoramas compared to ground-based counts (115 and 117 respectively). Of these, birds on 90% of nests visible in the panoramas could be unambiguously identified as marked or unmarked with a small daub of paint throughout the study period and thus a shift change reliably recorded. Gigapan-based shift duration was estimated using a novel instantaneous statistical method and were longer than estimates earlier in the egg brooding period, potentially revealing a new pattern in shift duration. 4. This example field application provides proof-of-concept and demonstration that the relatively low cost Gigapan system provides the spatial advantages of satellite or aerial photos with the detail and temporal replication of land-based camera systems. The Gigapan system can extend or enhance traditional data collection methods, particularly for simultaneous observations, at distance, of the behaviour of many surface nesting colonial seabirds..

opencc-zeroDec 2014View details →
zenodo32/100

Assessing the fate and behaviour of plant nutrients in aquaponic systems by chemical equilibrium modelling: A meta-analytical approach

<p>Cleaned dataset and Visual MINTEQ input files (without and without assumed presence of dissolved organic matter) for each record in the dataset.</p>

opencc-by-4.0Mar 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

ScienceDex for faculty

Your academic CV and NIH biosketch

Make your CV in your school's format and your NIH biosketch from it, then download both as Word files.