Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

An experimental data set for the analysis of the thermophysical behavior of a single-story naturally ventilated double-skin façade (DSF) under summer boundary conditions

Double-skin facades (DSFs) are adaptive building envelope elements that offer the possibility to dynamically interact with the heat and mass flow between indoor and outdoor environments. Though designed to provide better performance compared to more conventional envelope solutions, these façade systems may, in some cases, underperform and lead to an increase in energy use or in thermal discomfort if not properly designed and operated. One of the known problems is the risk of overheating, in hot periods, in the ventilated cavity. In order to analyze this effect, we have systematically investigated the performance of a single-story, naturally ventilated DSF. The DSF is operated in the so-called outdoor air curtain mode and has venetian blinds installed in the 20 mm deep ventilated cavity. Tests were carried out under a steady-state regime corresponding to relevant summertime conditions. In an effort to enable the scientific community to access experimental data to analyze this problem further or for model validation purposes, we released together with the open-access paper entitled "Characterization of a naturally ventilated double-skin façade through the design of experiments (DOE) methodology in a controlled environment," the entire set of experimental data collected during the tests. The data set contains the results of a series of experimental runs where different configurations of the DSF, as detailed below, have been subjected to various boundary conditions through a climate simulator facility equipped with a solar simulator device. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. Additional information about the original aims of the experiments, the detailed methods, and other data processing procedures can be found in the article mentioned above. The collection of experimental tests in this data set covers: <ul> <li>49 steady-state measurements where the following factors were changed using different experimental designs: solar irradiance (0, 350, and 700 Wm-2), outdoor chamber temperature (15, 25, and 35 ℃), opening size (7, 21, and 42 dm2), and venetian blinds angle (closed blinds θ=0 º, θ=45 º, and open blinds θ=90 º) [file name: "Complete_data.csv"],</li> </ul> Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a> The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.

opencc-by-sa-4.0Dec 2017View details →

Humans display a reduced set of consistent behavioral phenotypes in dyadic games

Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental research has adopted a game theoretical perspective, generating valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals’ behavior when facing different situations and to define a comprehensive classification of the strategies underlying the observed behaviors. We present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals’ actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (envious, optimist, pessimist, and trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts toward the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, which could be applied to simulating societies, policy-making scenario building, and even a variety of business applications.   The data from the "dr Brain" experiment is organized in two separated files: drbrain_users.csv  and drbrain_decisions.csv. 1.)   drbrain_users.csv contains information about the participants of the experiment (or users). There is one row per user, with the following information about each one of them: User_ID: unique ID number to identify the user. Age: user's age Gender: user's gender Experiment_number: Number of the experiment the user participated in. For organizational reasons, our research actually was made 45 experiments (or replicas) run over a period of 2 days, each one run with differnt users. A user was only allowed to participate in one experiment. Each experiment included between 10-25 users typically, and they played around 13-18 game rounds, typically. Each round and each couple of users played in different games (that is, different values of S, Sucker's payoff, and T, Temptation to defect, while the values of P=5 , Punishment, and R=10, Reward, were always fixed). Earnings: number of points the user obtained in total, over all rounds. 2.)   drbrain_decisions.csv  contains the information of the all game rounds for all experiments and all users. User_ID: unique ID number to identify the user.     Experiment_number: Number of the experiment the user participated in. Round_number: Number of the round within a given experiment. S: Value for the "Sucker's payoff" in the game of that round. T: Value for the "Temptation to defect" in the game of that round.     Game: Name of the game corresponding to those values of S and T for that round Action: Action chosen by the user (C: cooperate, D: defect) Opponent_ID: ID number of the user's opponent in that round.  Opponent_Action: Action (C or D) chosen by the user's opponent in that round. -------- For more details, see our research article: Humans display a reduced set of consistent behavioral phenotypes in dyadic games. Julia Poncela-Casasnovas, Mario Gutiérrez-Roig, Carlos Gracia-Lázaro, Julian Vicens, Jesús Gómez-Gardeñes, Josep Perelló, Yamir Moreno, Jordi Duch and Angel Sánchez. Science Advances Vol. 2, no. 8, 2016. DOI: 10.1126/sciadv.1600451 http://advances.sciencemag.org/content/2/8/e1600451

opencc-by-sa-4.0Oct 2024View details →

Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots

<h1>Dataset and code description</h1> This repository contains the codes and data for theScience Robotics paper Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots. The codes are in rr_scirob_analyses and the datasets are in rr_scirob_data.  If you want to rerun the data processing as presented in the paper, you need both rr_scirob_analyses and rr_scirob_data. You can copy the contents of rr_scirob_data into rr_scirob_analyses, as they have the same folder structure. Alternatively, you can run the download scripts to obtain the partial datasets relevant for certain subfigures. The file rr_scirob_data_readmes contains more detailed README files (rosbag info). You can copy its contents to rr_scirob_analyses after copying the contents of the rr_scirob_data. The individual datasets are organised into seven folders. <h2>Three Figures with Key Behavioural Metrics </h2> Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are: <ul> <li>Figure-2-KBM-1-Queen                     Queen - related Key Behavioural Metrics</li> <li>Figure-3-KBM-2-Workers                  Worker Bee - related Key Behavioural Metrics</li> <li>Figure-4-KBM-3-Comb                      Comb and Brood -related Key Behavioural Metrics </li> </ul> Each of these Figure-X folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure.  These are macro, micro, mezo, social, related to the four panels of that figure. Each of these subfolders contains a README file, describing how to process the data and providing further details.  Furthermore, there are three additional folders located in each of the 'panel' folder: <ul> <li>data: this is used to store the data necessary to generate the graphs. You can either populate it with the data from Zenodo, i.e.,  https://zenodo.org/records/13801588 Alternatively, you can use the `download.sh` script wich will download and extract the necessary data from the RoboRoyale project cloud.</li> <li>tmp: This folder is used to store intermediate results of the processing scripts</li> <li> output: This folder is used to store all the generated outputs of the individual scripts. These should be identical with the panels of the figure in the paper. These figures are also provided in the relevant folders.</li> </ul> Running the scripts contained in the micro, mezo, macro and social folders generates images and graphs in the output subfolders. These should be identical to the ones in the panels of Figures 2-4 in the paper. <h2>One Resting Analysis Figure</h2> One folder corresponds to the queen resting analysis figure <ul> <li>Figure-5-Resting       : Queen resting time analysis</li> </ul> This folder has three subfolders named data, tmp and output similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case. <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> Three other folders are related to performance analysis of the core methods required to calculate the KBMs. <ul> <li>KBM-1-performance evaluation:       Provides datasets and scripts to assess the performance of the queen marker detector</li> <li>KBM-2-performance evaluation:       Provides datasets and scripts to assess the performance of the worker bee detector</li> <li>KBM-3-performance evaluation:       Provides datasets and scripts to assess the performance of the oviposition detector </li> </ul> Each of these folders contains a README file explaining what to run in order to evaluate the performance of the method and to replicate the paper's results. <h2>Additional materials and data</h2> The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file 2023-month-queenpos-short.txt.  A description of the file structure is provided in the README of the relevant KBM folder. Additional data are available in the dataset section of https://roboroyale.eu. <h2>Rosbags</h2> The work is based on the Robot Operating System (ROS) and thus, the raw data come in the form of rosbags. We provide a few of the rosbags to allow checking examples of video and other raw data as reported by the system: <ul> <li>2023-10-25-08-42-20-Queen-Feeding.bag       -   queen feeding (KBM-1 Social)</li> <li>KPI1_2_mezo-queen_walk_sample.bag           -   queen walk as drawn in (KBM-1 Mezo)</li> <li>2023-10-10-00-04-10-trophylaxis.bag              -    worker bee trophylaxis  (KBM-2 Social)</li> <li>2023-09-19-09-00-20-egg-removal.bag           -   worker bee removing egg (KBM-2 Social)</li> </ul> <h2>Licence </h2> This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please cite the relevant paper, i.e.,  Ulrich, Stefanec, Rekabi-bana et al.: Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots. Science Robotics, 2024.

opencc-by-4.0Nov 2024View details →

Subcellular behavior model enables highly precise temporal super-resolved live-cell imaging

<div> <div>This repository contains the preprocessed dataset for [SuB-VFI](https://github.com/sduzzx857/SuB-VFI), including the real datasets we collected and the simulated testing and training datasets. You can refer to the Github repository for details.</div> <div> </div> <div>The simulated testing datasets can be downloaded from [the 2014 ISBI Particle Tracking Challenge](http://bioimageanalysis.org/track/).</div> <div>The EB1 datasets can be downloaded from the paper [The dynamic behavior of the APC-binding protein EB1 on the distal ends of microtubules](https://www.cell.com/current-biology/fulltext/S0960-9822(00)00600-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS096098220000600X%3Fshowall%3Dtrue).  We used *Movie2* from the Supplementary data.</div> <div>The CCR5 datasets can be downloaded from the paper [Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status](https://elifesciences.org/articles/76281). We used *Video4* in the Results section. </div> <div> </div> <div>The Lysosome datasets can be downloaded from [Content-Aware Frame Interpolation Microscopy Datasets](https://zenodo.org/records/10076346). We used data from the `Zproject` folder within the compressed file `Source_Data_Lysosomes_z-proj_Fig_5.zip`</div> </div>

opencc-by-4.0Apr 2021View details →

SIMUSAFE cyclist behavior in simulator and in real-world

This dataset includes data collected by the SIMUSAFE H2020 EU project (2017-2021) during its first data acquisition cycle. Voluntary bicycle riders are the subjects in this dataset, and the dataset includes a combination of sensory and psychological characteristics data. Sensory data was recorded in one of two settings: driving around the city in reality (NDT) and driving in a simulator (NST) along routes that were designed to imitate similar in-city driving. Overall, the dataset consists of recordings from 7 subjects for the following durations: Total time per user (hours): User                        Overall recording duration USER0                      0 days 17:28:06.284997888 USER1                       0 days 04:25:50.084999680 USER2                       0 days 00:11:50.677999616 USER3                       0 days 01:30:38.754000640 USER4                       0 days 02:44:50.625000192 USER5                        0 days 00:24:49.070000128 USER6                        0 days 01:15:46.702999040 Data measurements and computed attributes: GPS coordinates, acquired by a real GPS receiver in NDT and via a simulated receiver in NST. We extracted velocity from the GPS measurements, computed as the distance between every two subsequent coordinates divided by their corresponding timestamps. As a second derivative, Acceleration was then also derived from the difference of the above-mentioned velocity change between the two subsequent points divided by the time-delta. Accelerometer data were used to compute the Euclidean norm of the acceleration (a.k.a l2-norm) over the acceleration coordinates vector (i.e., {ax; ay; az}) at each point in time. This feature is sometimes also referred to as the energy-expenditure of the motion. Additional features: De/Acceleration {high / low / none}, computed per user per scenario. For each user, acceleration measurements were partitioned by quartiles and were computed per scenario. High-Acceleration was defined as values above the 3rd quartile and low-acceleration as values below. No-acceleration was denoted for the case of acceleration is equal to zero. Respectively, decelerations were computed in an equivalent manner, computed from the partitioning of negative acceleration values. Data preparation & preprocessing <ul> <li>GPS coordinates were de-duplicated w.r.t subsequent entries.</li> <li>To avoid issues originating from weak/loss of GPS signal, entries were partitioned into sessions. A session is defined as a sequence of entries with time-deltas no larger than 10 seconds. Velocity & acceleration were derived based on time-deltas within sessions.</li> <li>Rows with a velocity above or equal to 50km/h were filtered out based on the assumption that a regular bike rider won't reach such speeds.</li> </ul> In addition to the data sources and processing procedures mentioned above, the data has been processed according to the following. Per each subject & scenario, the data was partitioned into windows of 30 seconds using a sliding window with overlap. On each window 5 statistics were computed I.e., entropy, mean, variance, skew, kurtosis on 3 different sources: GPS-based velocity, GPS-based acceleration, and accelerometer-based magnitude. Achieving a total of 15 features. Data Schema The data comprises measurement data, data computed after windowing as well as subject psychometric evaluation data. Window data is computed with a sliding window of size 40 (samples) with an overlap of 20 samples. Before computing windows, the measurement data is filtered from entries with missing ‘v_gps’ (velocity computed from GPS coordinates) values. The measurement dataset is in the attached bicycle_cycle_1_measurement_data.csv file. Dataset computed with windowing is in the attached bicycle_cycle_1_windowed_data_w_computed_features.csv file. The psychometric evaluation dataset is in the attached bicycle_cycle_1_subject_psychometric_evaluation.csv file. Description of all dataset attributes in all three datasets is detailed in the Data description.docx file. Preliminary correlations identified in the dataset is detailed in BICYCLE-DATA-CORRELATIONS.pptx file

opencc-by-4.0Jul 2021View details →

Behavioral and fMRI Data: Nurturing the reading brain: Home literacy practices are associated with children's neural response to printed words through vocabulary skills

This is the behavioral and fMRI dataset described in "Nurturing the reading brain:  Home literacy practices are associated with children’s neural response to printed words through vocabulary skills".  Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual pre-processed volumes, normalized into the MNI template (see paper for details about the preprocessing pipeline). Anonymized behavioral data and first level analyses are also provided for each participant (SPM.mat file as well as beta, con, spmT, RPV and ResMS files). Note that the dataset also include runs and GLM results for a third task (Dots) that was not analyzed in the paper. Finally, the <a href="https://www.psychopy.org">PsychoPy</a> implementation of the tasks is also provided. If you have any questions, please send an email to jerome.prado [at] univ-lyon1.fr.  IMPORTANT: In accordance with EU privacy regulations, we ask that you sign and return a Data Use Agreement (DUA) before downloading the data. You can download the DUA <a href="https://zenodo.org/record/4965716/files/DUA.pdf?download=1">here</a>. Please, sign it and send it to jerome.prado [at] univ-lyon1.fr.

opencc-by-4.0Feb 2023View details →

Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"

This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior

openCC (other)Jul 2024View details →

Quantifying the relation between predator-induced behavior and growth performance in larval anurans, 1999.

Because the nature and magnitude of species interactions are functions of the traits that species possess, understanding how individual traits affect performance is important to our understanding of community structure. To examine the relation between species traits and performance, we first assessed behavioral responses of two larval anurans to three predator species in the laboratory. We then correlated these responses with growth performance of the two anurans when they competed in the field. In the laboratory experiment, larval bullfrogs (Rana catesbeiana) and green frogs (R. clamitans) exhibited no reduction in activity or spatial avoidance to bluegill sunfish (Lepomis macrochirus), moderate reductions in activity and spatial avoidance of mudminnows (Umbra limi ), and large reductions in activity and spatial avoidance of larval dragonflies (Anax spp.). In the field experiment, these behavioral responses were directly related to corresponding reductions in growth of the anuran larvae. Thus, for both species, changes in growth in the field could be correlated to the behavioral responses observed in the laboratory. Further, proportional changes in behavior in the presence of the different predators appeared to be related to changes in competitive relations in the field.

openCC (other)Dec 2025View details →

Comparing effects of auditory and visual disturbances on smallmouth bass parental care behaviors during the summer of 2025 at Douglas Lake, Michigan, USA

A prevalent source of sensory pollution within aquatic systems is recreational motorboats that can impact aquatic organisms through several exposure mechanisms. Auditory and visual sensory disturbances are particularly important as fish may utilize these cues during critical reproductive behaviors such as parental care. Here, we conducted a field study in Douglas Lake, Michigan, and located wild smallouth bass nests actively guarded by males. We exposed smallmouth bass to two sequential treatments of playback auditory noise and visual disturbances. Using an underwater drone, parental care behaviors of smallmouth bass were monitored before, during, and after both auditory and visual disturbances. The results show that auditory and visual disturbances may alter smallmouth bass parental care behaviors differently.

openCustomJan 2020View details →

Effects of experience and context on phototaxic behaviors of larval stream salamanders in the Upper Little Tennessee River basin

Desmognathus quadramaculatus larvae were captured from 2 locations in the Upper Little Tennessee River basin. Naïve individuals with respect to high-light environments were collected within the fully forested Ball Creek watershed at the Coweeta Hydrological Laboratory in Macon County, North Carolina. This watershed is a control basin that has been undisturbed since 1927. Individuals with experience in high-light environments, defined as habituated individuals, were collected from a first-order stream with less than 10% canopy cover located in Rabun County, Georgia. Salamanders were captured opportunistically using dipnets and cover object searches. Upon capture, salamanders were held individually in a cooler during transport where they were placed in containers with a paper towel cover object, native stream water, and kept in a temperature controlled room (15.5 C) with an indirect, natural photoperiod. Individual behaviors were tested within 48 hours of capture, and individuals were released at their capture location within one week.

openCC (other)Nov 2023View details →

Data and code from "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" Kaiser et al. 2023 Behavioral Ecology and Sociobiology

This dataset is published in support of "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" by Kaiser et al. 2023 in Behavioral Ecology and Sociobiology. Data and code to test the assumptions and key predictions of the Trivers-Willard hypothesis, which proposes that females produce more sons or daughters depending on food availability, in the black-throated blue warbler at the Hubbard Brook Experimental Forest, NH, 2007-2012. Datasets support analyses of sex ratio bias at both the nest and nestling levels. Data tables support the comparison of the ratio of variances in the scaled pre-fledging mass of male and female nestlings using an F test and reproduction of Figures 2a and 2b. Figures are those used in the published manuscript. Code supports the calculation of offspring sex ratio bias at the population level, and considering separately both low- and high-quality habitats, using the Neuhäuser test, statistical models testing the assumptions of the Trivers-Willard hypothesis, effects of food availability and parental provisioning on offspring sex ratio, and effects of food availability on pre-fledging nestling mass of sons and daughters, and a power analysis to determine the power to detect an effect of food supplementation on sex ratio. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.

openCC (other)May 2019View details →

MCR LTER: Coral Reefs: Stegastes behavior data in support of Kamath, et al., Oikos 2019

Stable between-group differences in collective behavior have been documented in a variety of social taxa. Here we evaluate the effects of such variation, often termed collective or colony-level personality, on coral recovery in a tropical marine farmerfish system. Groups of the farmerfish Stegastes nigricans cultivate and defend gardens of palatable algae on coral reefs in the Indo-Pacific. These gardens can promote the recruitment, growth, and survival of corals by providing a refuge from coral predation. Here we experimentally evaluate whether the collective defensive behavior of farmerfish colonies is correlated across intruder feeding guilds—herbivores, corallivores and egg-eating predators. Further, we evaluate if overall colony responsiveness or situation-specific responsiveness (i.e., towards herbivores, corallivores, or egg-eaters in particular) best predicts the growth of outplanted corals. Finally, we experimentally manipulated communities within S. nigricans gardens, adding either macroalgae or large colonies of coral, to assess if farmerfish behavior changes in response to the communities they occupy. Between-group differences in collective responsiveness were repeatable across intruder guilds. Despite this consistency, responsiveness towards corallivores (porcupinefish and ornate butterflyfish) was a better predictor of outplanted coral growth than responsiveness towards herbivores or egg-eaters. Adding large corals to farmerfish gardens increased farmerfish attacks towards intruders, pointing to possible positive feedback loops between their aggression towards intruders and the presence of corals whose growth they facilitate. These data provide evidence that among-group behavioral variation could strongly influence the ecological properties of whole communities. These data support the publication Kamath, A., J. N. Pruitt, A.J. Brooks, M.C Ladd, D.T. Cook, J.P. Gallagher, M.E. Vickers, S.J. Holbrook and R.J. Schmitt. 2019. Potential feedback between cor

OpenNeuro44/100

The Contributionsof Eye Gaze Fixations and Target-Lure Similarity to Behavioral and fMRI Indices of Pattern Separation and Pattern Completion

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →

opencc-by-4.0Feb 2020View details →

Public Dataset for "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior"

Dataset for the "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior" paper, published in ICWSM 2018. The full text of the paper can be found <a href="https://arxiv.org/pdf/1802.00393.pdf">here</a>.   The dataset provided here includes an updated version of the original dataset, with ~100k tweets annotated using the CrowdFlower platform:  <ul> <li> hatespeech_id_label_PUBLIC_100K.csv: contains ~100K rows, where every row consists of a unique Tweet ID.  </li> <li> hatespeech_text_label_vote_RESTRICTED_100K.csv: contains ~100K rows, where every row consists of the tweet text, its label according to majority annotation and the number of majority annotators. Available only <a href="https://zenodo.org/record/3706866#.Xmkh6i97FQI">here</a>. </li> <li> retweets.csv: contains ~2K rows, where every row consists of the row number in the  hatespeech_text_label_vote_RESTRICTED_100K.csv file which is the first occurrence of a Tweet text followed by comma-separated row numbers of all other occurrences of the same Tweet text in the same file. There are ~8K other occurrences due to retweets. Available only <a href="https://zenodo.org/record/3706866#.Xmkh6i97FQI">here</a>. </li> </ul>   UPDATE: It has come to our understanding that a number of the tweets are not available anymore for download on Twitter. Therefore, we provide <a href="https://zenodo.org/record/3706866#.YYLG6S8RqjQ">here </a>the hatespeech_text_label_vote_RESTRICTED_100K file with the full ~100K tweet texts, their associated majority label, and the number of votes for the majority label. The tweets are shuffled so that there is no connection between tweet IDs and texts (in order to be in line with the T&C of Twitter).   Please cite the paper in any published work that uses any of these resources.  @inproceedings{founta2018large,      title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior},      author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas},      booktitle={11th International Conference on Web and Social Media, ICWSM 2018},      year={2018},      organization={AAAI Press}  }  For any further questions contact a.m.founta at gmail dot com AND markos.charalambous at eecei dot cut dot ac dot cy

opencc-by-4.0Feb 2020View details →

Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2 nanoelectromechanical resonators"

Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2 nanoelectromechanical resonators", Nat. Commun. 10, 4831 (2019); DOI:<a href="https://doi.org/10.1038/s41467-019-12795-1">10.1038/s41467-019-12795-1</a>.

opencc-by-4.0Jan 2020View details →

Acclimation to water restriction implies different paces for behavioral and physiological responses in a lizard species

Raw data of the article "Acclimation to Water Restriction Implies Different Paces for Behavioral and Physiological Responses in a Lizard Species" by Rozen-Rechels D. et al., published in Physiological and Biochemical Zoology 93(2):160-174 in 2020 (https://doi.org/10.1086/707409). These data are freely available in csv format. See the readme file for metadata explanation. Data were formatted by the first author David Rozen-Rechels and collected according to standards and procedures described in the companion journal article.

opencc-by-4.0Mar 2019View details →

Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"

This dataset contains results and analysis described in the study "Robots mediating interactions between animals for interspecies collective behaviors", Bonnet, F., Mills, R., Szopek, M., Schönwetter-Fuchs, S., Halloy, J., Bogdan, S., Correia, L., Mondada, F. and Schmickl, T. (2019), Science Robotics, 4(28), doi: 10.1126/scirobotics.aau7897 Contents:  <ul> <li>experimental data (logs from robotic systems, example videos)</li> <li>animal tracking analysis output</li> </ul> See the readme and summary files contained within the archives for further details.

opencc-by-4.0Apr 2017View details →

Laboratory dataset on Self-Heating Behavior and Ignition of Shale Rock

The file attached contains a complete set of experimental data from shale rock self-heating ignition cubic basket experiments. The experiments were carried out in a thermostatically controlled oven with thermocouples for measuring the ambient and shale sample temperatures. The data is divided in two parts, one for coarse particles and one for fine particle experiments. The data reported includes the dates of experiments, volume of shale basket being tested, oven ambient temperature, fuel mass of shale, bulk density of the shale, residue mass after the experiment, percentage of residue in respect to initial mass, and if the sample ignited or not. This data is in support of the journal paper: F. Restuccia, N. Ptak, G. Rein, Self-Heating Behavior and Ignition of Shale Rock, Combustion and Flame, Vol 176, 2017, pp 213-219. doi: 10.1016/j.combustflame.2016.09.025.

opencc-by-4.0Jun 2024View details →

International datasets behavior effects COVID-19

This dataset stems from the project ‘Beprepared’: (<a href="https://be-prepared-consortium.nl/">https://be-prepared-consortium.nl/</a>) which aims to provide in-depth analyses of mixed-method behavioural science data collected throughout the unprecedented COVID-19 pandemic and inform preparedness strategies for future outbreaks. In approaching the research from a behavioural and social science perspective, researchers focus on four main themes: ·       Prevention behaviour, psychosocial and contextual determinants, and (communication) interventions ·       Resilience and engagement of citizens, communities and organisations ·       Research methodology and preparedness ·       Effective and integrated policy advice   This resource links to the theme ‘research methodology’ and provides an overview of datasets that have been used internationally to study the behavioral effects of the Covid-19 pandemic. These datasources can be used to study how people behave in a variety of settings during the Covid pandemic and so to inform policy-makers, but also to study the effects of behavioral interventions. It includes datasources that for example study mobility behavior at a regional or national level, physical distancing in public, health adherence behaviors (like handwashing, mask wearing), social contacts on- and offline, purchasing behaviors (shopping) etc.   The resource consists of two datasets: 1.     A dataset (in .xlsx and .csv format) of the search strategy used to come to the list of datasources called “search strategy” 2.     A dataset (in .xslx and .csv format) of the results of the search, called “search results” 3.     A dataset (in .xslx and .csv format) of a step where duplicate studies are identified 4.     A dataset (in .xslx and .csv format) where for 131 studies the data quality was assessed