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.
3,983
datasets available to search
ShareScore release 0.7.1
Dataset results
3,983 results for “personality”
Role of Personal Connections in Shaping Decisions About Private Forest Use in Central Massachusetts 2008
We begin with a simple premise: Social and ecological systems are interconnected in complex ways. Forests are, perhaps, one of the most intriguing examples of this interconnectedness--particularly those in private ownerships. Forested landscapes are essential in maintaining human systems through the provision of multiple ecosystem services that span public (e.g., clean water, nutrient cycling) and private (e.g., fiber, maple syrup, home sites) goods. However, the majority of forestland in the Eastern United States is a mosaic of small landholdings (less than 20 ha) where property management is largely uncoordinated. On such landscapes, decentralized, ownership-centric decision-making defines the mix of ecosystem services provided and the landscape patterns present now and in the future. While somewhat effective for less spatially sensitive ecosystem services (e.g., fiber production), this ownership-centric approach is ill suited to spatially sensitive ones (e.g., water quality) and may, in some cases, be detrimental to them (e.g., habitat fragmentation). Improving the ecological and landscape sensitivity of private forest conservation and management is a major challenge facing researchers, practitioners, and policymakers in sustaining forest ecosystems. Central to unraveling this challenge is a fundamental understanding of how landowners simultaneously fit within their social and bio-physical landscapes. Despite their importance to broader forest sustainability, our collectively understanding of forest landowners has been primarily concerned with individual landowners and/or individual properties. For example, most research surrounding private forest landowners centers on primarily agent-based theories of behavior or decision-making (e.g., rational actor, theory of planned behavior). This perspective is useful in predicting and effecting behavior at broad scales, but lacks the specificity needed to address local landscape concerns and/or opportunities. Other studies
When my wrongs are worse than yours: behavioral and neural asymmetries in first-person and third-person perspectives of accidents
Open the record for dataset details and reuse information.
Privacy Policies Paragraph containing Personal Data
<p>The data consists in crawled privacy policies from European privacy policies. They were split into paragraphs and annotated as containing or not personal data.</p> <p>The question that was asked to annotators was "Does this paragraph contain the explicit mention of specific personal data (e.g. name, phone number, social security, …) being collected?".</p> <p>A full description of the dataset can be found in D3.4 of the SMOOTH project</p>
LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots
<p>In forest inventory, trees are usually measured by handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory is nowadays redesigned, since modern laser scanner technology became available. Laser scanner generate massive data in the form of 3D point clouds. Novel methodology is currently developed to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data was collected by two different laser scanning systems: (i) a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK), and (ii) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contains digital terrain models (DTM), field measurements as reference data (“ground-truth”), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.</p>
Dataset of the manuscript "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment"
<p>The present database belongs to the manuscript titled "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment". The study has been peformed in English, but the research is conducted in Spanish.</p>
Atrial Models with Personalized Effective Refractory Period
<h1>Impact of Effective Refractory Period Personalization on Prediction of Atrial Fibrillation Vulnerability</h1> <div> </div> <div> <div><strong>Authors:</strong> Patricia Martínez Díaz, Christian Goetz, Albert Dasi, Laura Anna Unger, Annika Haas, Olaf Dössel, Armin Luik, Axel Loewe</div> <div>patricia.martinez@kit.edu / publications@ibt.kit.edu</div> <div><a href="https://doi.org/10.1093/europace/euad122.542">doi:10.1093/europace/euad122.542</a></div> <div> </div> <div>This dataset contains 7 atrial meshes, 6 left atria and 1 right atrium, derived from electroanatomical mapping and measurements of the effective refractory period (ERP), bipolar voltage (bi) and local activation times (lat). The meshes include annotations and fibers and are ready for simulations in the cardiac electrophysiology simulator <a href="https://doi.org/10.1016/j.cmpb.2021.106223">openCARP</a>. We also provide the code to reproduce 272 reentries by reading the selected parameters.par and state.roe files. The meshes were generated using <a href="https://github.com/KIT-IBT/AugmentA">AugmentA code</a> and the simulated reentries were induced following the <a href="https://doi.org/10.3389/fphys.2021.656411">PEERP protocol</a> by Azzolin et al. </div> <div> </div> <h2>Folder structure</h2> <div>The code is located in the `src` folder, the meshes in the `data` folder and the reentries in the `results` folder. </div> <div>```</div> <div>src/</div> <div> |-- run.py</div> <div> |-- induceReentry.py</div> <div> |-- getStimPoints.py</div> <div> |-- element_tag.csv</div> <div> |-- al_mk_H.par</div> <div> |-- requirements.txt</div> <div> |-- reproduceReentry.py</div> <div>data/</div> <div> |-- meshes/</div> <div> |-- P1/ </div> <div> |-- P1_with_erp_lat_bi.vtk </div> <div> |-- ERP.pts</div> <div> |-- ERP_values.txt</div> <div> |-- ablation.pts</div> <div> |-- LA_stim_points_2cm.pts</div> <div> |-- bilayer/</div> <div> <div> |-- nodal_adjustment/</div> <div> |--PARAMETER_SCENARIO.adj (e.g. Gto_continuous.adj)</div> </div> <div>.</div> <div>.</div> <div>.</div> <div> |-- P7 </div> <div>results/</div> <div> |-- MESH_SCENARIO_CV/ (e.g P1_continuous_0.3) </div> <div> |-- point_X_beat_Y</div> <div> |-- MESH_SCENARIO_CV_PERTURBATION_SET/ (e.g P1_continuous_0.7_2_1) </div> <div> |-- point_X_beat_Y </div> <div>README.md</div> <div>```</div> <div> <ul> <li>`src`: contains the source files needed to run PEERP protocol <ul> <li>`run.py` This is the main function to run the pacing protocol (not needed to run if reentries are only reproduced, check reproduceReentry.py)</li> <li>`induceReentry.py` Contains a list of pacing protocols. The PEERP protocol is included here</li> <li>`getStimPoints.py` Extract the stimulation points</li> <li>`element_tag.csv` Region tag numbering</li> <li>`al_mk_H.par` Par file with ionic scaling factors for three states; H:Healthy, M:Mild, S:Severe</li> <li>`requirements.txt` Packages to create the virtual enviroment. (This was my output of ```pip3 list> requirements.txt```)</li> <li>`reproduceReentry.py` Reentries can be reproduced given a selected folder where the .par and .roe files are stored.</li> </ul> </li> <li>`data`: contains the `meshes` folder with the bilayer meshes in openCARP (.elem, .lon and .pts) and .vtk format. Synthetic fibrotic distributions are included in the the .regele files. <ul> <li>`meshes/P1/P1_with_erp_lat_bi.vtk` Mesh with ERP, LAT and bipolar voltage data</li> <li>`meshes/P1/ERP_values.txt/` measured ERP data</li> <li>`meshes/P1/ERP.pts/` electrode coordinates where ERP data was measured</li> <li>`meshes/P1/ablation.pts/` electrode coordinates where tissue was ablated</li> <li>`meshes/P1/LA_stim_points_2cm.pts` Stimulation points for the PEERP protocol</li> <li> `meshes/P1/bilayer/LA_bilayer_with_fiber_slow_conductive.regele` Element ids corresponding to regions of low voltage (< 0.5mV)</li> <li>`meshes/P1/bilayer/LA_bilayer_with_fiber_scar.regele` Element ids corresponding to regions of low voltage (< 0.1mV)</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_regions_um.vtk` Bilayer mesh with a discrete split where each region has a single ERP value</li> <li>`meshes/P1/bilayer/LA_bilayer_with_fiber_with_fibrosis.vtk` Bilayer mesh with fibrosis informed by low voltage areas</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_continuous_um.vtk` Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_continuous_2ms_um.vtk` Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data with +- 2ms perturbation</li> </ul> </li> </ul> <p>We studied 7 different scenarios: </p> </div> <ol> <li>Monoregion scenario with no ERP personalization, where all nodes had the same ERP</li> <li>Control scenario with no ERP personalization, where ERP nodes of certain defined anatomical regions where modified as reported in Loewe et al. 2015 </li> <li>Regional scenario with ERP personalization, where each region had a single ERP value derived from clinical measurement</li> <li>Continuous scenario with ERP personalization, where the ERP distribution was generated by interpolation of measured ERP data</li> <li>Control scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements</li> <li>Continuous scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements</li> <li>Continuous scenario where ERP measurements with additional perturbation draw from a uniform distribution. The perturbations were 2,5,10 and 20 ms, and we repeated this set 5 times for P6</li> </ol> <p>In summary, we provide the following data: </p> <div> <ul> <li>7 meshes for openCARP simulations</li> <li>7 meshes in vtk format with continuous ERP distribution</li> <li>27 meshes in vtk format with continuous ERP distribution with perturbed ERP with 2,5,10 and 20ms from a random uniform distribution</li> <li>7 meshes in vtk format with regional ERP</li> <li>7 meshes in vtk format with ERP, LAT and bipolar voltage</li> <li>7 ablation set points</li> <li>7 ERP set points with their corresponding values</li> <li>209 reentries generated under 4 ERP scenarios (monoregion, control,regional,continuous) run with a conduction velocity of 0.7 0.5 and 0.3 m/s</li> <li>26 reentries generated under 2 scenarios ERP+Fibrosis (control + continuous) run with a conduction velocity 0.3 m/s</li> <li>37 reentries induced with continuous ERP for patient P3 @CV 0.3 for the sensitivity analysis </li> </ul> </div> <h2>Create a dynamic Courtemanche model</h2> <p>As we will modify the ionic parameters on a nodel basis you will need to create a dynamic Courtemanche model and then declare the variables (ionic conductances) you need to modify. In your openCARP installation folder, go to the `limpet` copy the Courtemanche.model file</p> <p>```<br>cd openCARP/physics/model/limpet<br>cp Courtemanche.model Courtemanche_nodal.model<br>vim Courtemanche_nodal.model<br>```</p> <p>Then add on top the parameters that need to be modified on a nodal-basis:</p> <p>```<br>group {<br> GK1 ;<br> Gto ;<br> GKr ;<br> GKs ;<br> GCaL ;<br> factorGKur ;<br> maxINaCa ;<br> maxIpCa ;<br>} .nodal();</p> <p>```</p> <p>Then you would need to recompile openCARP. In the terminal, go to your openCARP's top level folder:<br>```<br>cd openCARP/ <br>```</p> <p>Configure CMake with updated imp_list.txt via:<br>```<br>cmake -S. -B_build -DUPDATE_IMPLIST=ON</p> <p>```<br>Run the CMake building process:<br>```<br> cmake --build _build<br>```<br> This will generate the `.h` and `.cc` files for your dynamic model inside `physics/limpet/src/imps_src`</p> <p>**Note:** If you want to add or modify a model file after openCARP was compiled, it is possible to first clean the previous generated files during compilation by running `make clean` before recompiling openCARP.</p> <p>If you compile your own version of openCARP, then you can modify the settings.yaml file, to point to your openCARP version with the dynamic model.<br>```<br>cd .config/carputils<br>subl settings.yaml <br>```<br>Add the build name:</p> <p>```<br>CARP_EXE_DIR:<br> CPU: /Users/lm104/Documents/OpenCARP/opencarp/_build/bin<br> NODAL: /Users/lm104/Documents/OpenCARP/openCARP_nodal_adj/_build/bin<br>```<br>You can check that the new dynamic model is there by calling bench<br>```<br>bench -—list-imps<br>bench —-imp Courtemanche_nodal --imp-info<br>```</p> <p>You can find additional information about dynamic models <a href="https://opencarp.org/documentation/examples/01_ep_single_cell/04_limpet_fe">here</a>.</p> <h2>Reproduce the reentries </h2> <div>You can generate the .igb file of a specific reentry by selecting the corresponding folder in the results directory. An example is given to reproduce the reentry in P1_bi_M_LA/point_0_beat_2/reproduce_reentry.igb. Select the folder `--par_file_directory`and set `--tend` to define the duration of the simulation in miliseconds.</div> <div>_HINT: We recommend keeping the folder structure so that the other parameters, such as: mesh, scenario, state and chamber, can be read from the --par_file_directory. Otherwise, the meshes and results directories need to be modified._</div> <div>```</div> <div>cd src/</div> <div>reproduceReentry.py --par_file_directory ../results/P1_bi_M_LA/point_0_beat_2 --tend 1500</div> <div> </div> <div>```</div> <div> </div> <h3>Preparation before running the PEERP pacing protocol</h3> <div>Follow the next steps if you want to run the PEERP pacing protocol, either for the provided meshes or for your own meshes. To run the PEERP protocol in a controlled environment, it is recommended, before running the run.py, to create a virtual environment. Go to your terminal and type: </div> <div>```</div> <div>cd src/</div> <div>python3 -m venv ./myEnv</div> <div>source ./myEnv/bin/activate</div> <div>pip3 install -r requirements.txt</div> <div>```</div> <div> </div> <div>You need to add carputils to your `PATH`. You can run the code in the terminal or use and IDE to debug the code. </div> <div>Note: I am using PyCharm 2020.3. and in Settings --> Python interpreter --> show all and then in the (+) symbol, add the path to carputils there:</div> <div> </div> <div>Otherwise you can add this extra lines at the beginning of `run.py``:</div> <div>```</div> <div># Replace '/path/to/carputils' with the actual path to your carputils package</div> <div>carputils_path = '/path/to/carputils'</div> <div> </div> <div># Add the carputils path to sys.path</div> <div>sys.path.append(carputils_path)</div> <div>```</div> <h3>Run the PEERP protocol</h3> <div> </div> <div>The following example runs the PEERP from a single stimulation point. If you want to run PEERP over all the points, simply add the flag --run_all_points 1 </div> <div>```</div> <div>cd src/</div> <div>python3 run.py --giL 0.4166 --geL 1.458 --cv 0.8 --mesh monoatrial --protocol PEERP --pacing 122718 --stim_file LA_stim_points.txt --geometry LA_bilayer_with_fiber_um --cell_bcl 500 --model Courtemanche --ionic_prop_file al_mk_S.par --max_n_beats_PEERP 1 --overwrite-behaviour overwrite</div> <div>```</div> <div> </div> <h3>Running your own experiment and making your own changes</h3> <div>Extract the stimulation points on your mesh, where the PEERP protocol will be run: </div> <div>```</div> <div>python3 getStimPoints.py --mesh monoatrial --tolerance 20000 --stim_file LA_stim_points.txt --chamber LA</div> <div>```</div> <div> </div> <div>Tune conduction velocity (CV) and conductivites. The code expects the intracellular end extracellular longitudinal conductivity values as an input. We used `tuneCV` to fit CV=0.7m/s with dx=0.4mm and dt=20us</div> <div>If you want to adjust the values, run in the terminal:</div> <div>```</div> <div>tuneCV --resolution 400 --model Courtemanche --velocity 0.7 --converge True --sourceModel monodomain --surf True --dt 20</div> <div>```</div> <div>You can provide the location of the start of the activation by selecting the desired point ID:</div> <div>- Load the mesh in Paraview (or Meshalyzer)</div> <div>- click on the ? symbol</div> <div>- save the ID and change the `--pacing` argument </div> <div> </div> <div>Call `run.py` with a new mesh. The protocol starts by prepacing the mesh and then using the last beat as initial condition tu run the PEERP.</div> <div>Be aware that for a monoatrial mesh you might need to give the new id for the location of the earliest activation. Change `12345` to your desired point ID.</div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol prepace --stim_file LA_stim_points.txt</div> <div>```</div> <div> </div> <div>Run the protocol with different electrical remodelling stage. You can change the .par file or select one file from the three provided: </div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol PEERP --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <div>```</div> <div> </div> <div>You can also try to run a biatrial example. The biatrial mesh is also provided. You need to extract the points on the RA surface using `getStimPoints.py`, to run the RA experiment: </div> <div>```</div> <div>cd src</div> <div>python3 getStimPoints.py --mesh biatrial --tolerance 20000 --stim_file RA_stim_points.txt --chamber RA</div> <div>```</div> <div>Then run PEERP twice, one per each chamber:</div> <div> </div> <div>```</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --pacing 12345 --stim_file LA_endo_2cm.txt --args.ionic_prop 'l_mk_M.par'</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <p> </p> </div> <p> </p>
BIRAFFE2: The 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems
<p>This is our 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (<strong>BIRAFFE2</strong>). It is a dataset consisting of <em><strong>electrocardiogram (ECG)</strong></em>, <em><strong>galvanic skin response (GSR)</strong></em>, changes in <em><strong>facial expression</strong></em> signals and <em><strong>hand movements</strong></em> (represented by gamepad's accelerometer and gyroscope) recorded during affect elicitation by means of <em><strong>audio-visual stimuli</strong></em> (from IADS and IAPS databases) and our proof-of-concept three-level <em><strong>emotion evoking game</strong></em>. All the signals were captured using portable and low-cost equipment: BITalino (r)evolution kit for ECG and GSR and Creative Live! web camera for face photos (further analyzed by MS Face API).</p> <p>Besides the signals, the dataset consists also of <em><strong>participants' self-assessment</strong></em> of their affective state after each stimuli (in the <em><strong>valence and arousal dimensions</strong></em>), <em><strong>"Big Five" personality traits</strong></em> assessment (using NEO-FFI inventory), and <em><strong>game involvement</strong></em>-related metrics (using GEQ questionnaire).</p> <p>In 1.1.0 version, RAW questionnaire data was included. The licence was changed from CC BY-NC-ND 4.0 to CC BY 4.0.</p> <p>For detailed description see <a href="https://doi.org/10.1038/s41597-022-01402-6">BIRAFFE2 Data Descriptor in Nature Scientific Data</a>.<br> For preview of the files before downloading the whole dataset see <em>sample-SUB211-[...]</em> files.</p> <p>All documents and papers that report on research that uses the BIRAFFE dataset should acknowledge this by <strong>citing the paper</strong>:<br> Kutt, K., Drążyk, D., Żuchowska, L., Szelążek, M., Bobek, S., & Nalepa, G. J. (2022). <strong>BIRAFFE2, a multimodal dataset for emotion-based personalization in rich affective game environments</strong>. <em>Scientific Data</em>, <em>9</em>, 274. <a href="https://doi.org/10.1038/s41597-022-01402-6">https://doi.org/10.1038/s41597-022-01402-6</a></p>
Data and analysis script for "The (non)effect of personalization in climate texts on credibility of climate scientists: A case study on sustainable travel"
<p>Dataset and analysis script for the article "<strong>The (non)effect of personalization in climate texts on credibility of climate scientists</strong><strong>: A case study on sustainable travel</strong>", under review at Geoscience Communication (https://doi.org/10.5194/egusphere-2024-543)</p>
PEDRo: an Event-based Dataset for Person Detection in Robotics
<p>This event-based dataset is specifically designed for person detection in robotics applications. </p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Dataset for Website Personality Detection
<p>This dataset supports research on identifying the personality of websites. It contains data from 3,000 websites, covering five distinct website categories, and provides quantitative elements extracted from these sites. Additionally, the dataset includes information about the selected website categories, as well as details on website personality "Facets" and "Items." The dataset is accompanied by survey results related to the research.</p>
Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
DATA: Linking Personality and Trust in Intelligent Virtual Assistants
<p>This dataset (n=367) investigates links between people's personality, their trust in intelligent virtual agents (e.g., Amazon's Alexa, Apple's Siri, etc.) and their affinity for technology interaction.</p>
Small Object Aerial Person Detection Dataset
<p><strong>Small Object Aerial Person Detection Dataset:</strong></p> <p>The aerial dataset publication comprises a collection of frames captured from unmanned aerial vehicles (UAVs) during flights over the University of Cyprus campus and Civil Defense exercises. The dataset is primarily intended for people detection, with a focus on detecting small objects due to the top-view perspective of the images. The dataset includes annotations generated in popular formats such as YOLO, COCO, and VOC, making it highly versatile and accessible for a wide range of applications. Overall, this aerial dataset publication represents a valuable resource for researchers and practitioners working in the field of computer vision and machine learning, particularly those focused on people detection and related applications.</p> <p> </p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>People</td> </tr> <tr> <td>Training</td> <td>2092</td> <td>40687</td> </tr> <tr> <td>Validation</td> <td>523</td> <td>10589</td> </tr> <tr> <td>Testing</td> <td>521</td> <td>10432</td> </tr> </tbody> </table> <p> </p> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p>
Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school
<p><strong>ABSTRACT</strong></p> <p>The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.</p> <p><strong>INTRODUCTION</strong></p> <p>Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.</p> <p>The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).</p> <p>Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).</p> <p>These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.</p> <p><strong>PARTICIPANTS</strong></p> <p>The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).</p> <p><strong>DATE STRUCTURE AND ARCHIVES FORMAT</strong></p> <p>The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.</p> <p><strong>Social network</strong></p> <p>The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.</p> <p>The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.</p> <p>In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.</p> <p><strong>Personal networks</strong></p> <p>Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).</p> <p>Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.</p> <p><strong>Sense of community and metropolitan displacements</strong></p> <p>The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:</p> <p> • Socio-economic data.</p> <p> • Data on habitual residence.</p> <p> • Information on intercity journeys.</p> <p> • Identity and sense of community.</p> <p> • Personal network indicators.</p> <p> • Social network indicators.</p> <p><strong>DATA ACCESS</strong></p> <p>Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.</p> <p>The visual representation of the personal networks of the respondents in both waves is available in the following album of the <em>Graphic Gallery of Personal Networks</em> on Flickr: <<a href="https://www.flickr.com/photos/25906481@N07/albums/72157667029974755">https://www.flickr.com/photos/25906481@N07/albums/72157667029974755</a>>.</p> <p>In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:</p> <ul> <li>Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. <em>Universitas Psychologica, 17</em>(1), 86-98. <a href="https://doi.org/10.11144/Javeriana.upsy17-1.eerp">https://doi.org/10.11144/Javeriana.upsy17-1.eerp</a> </li> </ul> <p>The English version of this article can be downloaded from: <a href="https://tinyurl.com/yy9s2byl">https://tinyurl.com/yy9s2byl</a></p> <p><strong>CONCLUSION</strong></p> <p>The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.</p> <p>The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.</p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.</p> <p><strong>REFERENCES</strong></p> <p><strong>Brandes, U., & Wagner, D. (2004). </strong>Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), <em>Graph Drawing Software</em> (pp. 321-340). New York: Springer-Verlag. </p> <p><strong>Maya-Jariego, I. (2018).</strong> Why name generators with a fixed number of alters may be a pragmatic option for personal network analysis. <em>American Journal of Community Psychology, 62</em>(1-2), 233-238. DOI 10.1002/ajcp.12271</p> <p><strong>Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018).</strong> Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. <em>Universitas Psychologica, 17</em>(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp</p> <p><strong>Maya-Jariego, I., Lubbers, M. J. & Molina, J. L. (2019).</strong> A friendship network in decay: The dynamics of social relationships of a secondary school cohort over the transition to university. <em>Remitido</em>.</p> <p><strong>Seidman, E., & French, S. E. (2004).</strong> Developmental trajectories and ecological transitions: A two-step procedure to aid in the choice of prevention and promotion interventions. <em>Development and Psychopathology, 16</em>(4), 1141-1159. https://doi.org/10.1017/s0954579404040179</p>
Duhumbi Personal Narratives - Transcribed, parsed, glossed, translated text files
<p>This data set contains the .wav sound files, .trs Transcriber files, .txt Toolbox-compatible Notepad files and .pdf files with the completely transcribed, glossed, parsed and translated examples of the following recordings that belong to the following publication:</p> <p>Bodt, Timotheus Adrianus. 2020. Grammar of Duhumbi. Leiden: Brill. ISBN 978-90-04-40947-7. <a href="https://brill.com/view/title/55767">https://brill.com/view/title/55767</a></p> <ul> <li>[CHUK230512D1A] / CMT / The story of the former CM’s death</li> <li>[CHUK230512C1A] / LHT / The history of Laphek village</li> <li>[CHUK230512B1] / THT / Hunting takin</li> <li>[CHUK260413A3A]/ ACK / Alcohol consumption</li> <li>[CHUK131014] / DTPK / Chasing the demons</li> </ul> <p>The explanation of all the grammatical features that occur in these sound files can be found in the Grammar of Duhumbi.</p> <p>The main Toolbox files can be found in the zip file “Settings”, this includes the IPA keys for Duhumbi, the entire setup of the Toolbox database, and the Duhumbi dictionary and Parsing dictionary.</p> <p>The .wav, .txt and .trs files combined in the same folder will enable to open Toolbox and work with the recordings, e.g. play them sentence for sentence and see the transcriptions and translations.</p> <p>Transcriber version 1.5.1: <a href="http://trans.sourceforge.net/en/presentation.php">http://trans.sourceforge.net/en/presentation.php</a> or <a href="https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/">https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/</a></p> <p>Toolbox version 1.6.1: <a href="https://software.sil.org/toolbox/download/">https://software.sil.org/toolbox/download/</a></p> <p>For the metadata of the sound files in this data set, I refer to Chapter 13 Texts in the Grammar of Duhumbi. This Chapter has a complete listing of the texts, their topics, the speakers and their background etc.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <strong><em>of any kind</em></strong><em>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: monpasang (at) gmail (dot) com</p>
Data from the "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation"
<p>Data associated with:</p> <p>Dumas, D., Doherty, M., Organisciak, P. (2020) "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation". PLOS ONE.</p> <p>Description of work associated with this data:</p> <blockquote> <p>As a profession, acting is marked by a high-level of economic and social riskiness concomitantly with the possibility for artistic satisfaction and/or public admiration. Current understanding of the psychological attributes that distinguish professional actors is incomplete. Here, we compare samples of professional actors (n = 104), undergraduate student actors (n = 100), and non-acting adults (n = 92) on 26 psychological dimensions and use machine-learning methods to classify participants based on these attributes. Nearly all of the attributes measured here displayed significant univariate mean differences across the three groups, with the strongest effect sizes being on Creative Activities, Openness, and Extraversion. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) classification model was capable of identifying actors (either professional or student) from non-actors with a 92% accuracy and was able to sort professional from student actors with a 96% accuracy when age was included in the model, and a 68% accuracy with only psychological attributes included. In these LASSO models, actors in general were distinguished by high levels of Openness, Assertiveness, and Elaboration, but professional actors were specifically marked by high levels of Originality, Volatility, and Literary Activities.</p> </blockquote>
Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school
<p>The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.</p> <p><strong>INTRODUCTION</strong></p> <p>Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context.</p>
Analysis of In-person Conferences
<p>Data set and code associated with the manuscript titled: “Evaluating features of scientific conferences: A call for improvements” preprinted at bioRxiv: doi: https://doi.org/10.1101/2020.04.02.022079</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.