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97 results for “affective dataset”
A dataset recorded during development of an affective brain-computer music interface: calibration session
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A dataset recorded during development of an affective brain-computer music interface: testing session
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A dataset recorded during development of an affective brain-computer music interface: training sessions
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Postnatal Affective MRI Dataset
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Using the Tea Bag Index to unravel how interactions between an antibiotic (Trimethoprim) and endocrine disruptor (17a-estradiol) affect aquatic microbial activity: Supporting Dataset 1
<p>The constant release of complex mixture of pharmaceuticals, including antimicrobials and endocrine disruptors, into the aquatic environment. These have the potential to affect aquatic microbial metabolism and alter biogeochemical cycling of carbon and nutrients. We used the Tea Bag Index (TBI) for decomposition within a series of contaminant exposure experiments to test how interactions between an antibiotic (trimethoprim) and endocrine disruptor (17a-estradiol) affects microbial activity in an aquatic system. The TBI is a citizen science tool used to test microbial activity by measuring the differential degradation of green and rooibos tea as proxies for labile and recalcitrant organic matter decomposition. Here we present the raw data on pharmaceutical exposures and the mass loss of the Rooibos and Green tea bags within the experiment. From Tea Bag mass loss we then calculated the Stabilisation Factor (S) and Initial Decomposition Rate of the labile organic matter fraction.</p>
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Dataset for Sandboxing use case SUC2 related to cyber attacks affecting Wide Area Protection
<p><span>This dataset is related to the operation of the second KIOS CoE sandboxing use case (SUC2) which inclused 3 scenarios (S1-S3) which examins the behavious a WAP scheme of power grids in case of a short circuit fault and in case of two types of cyber attacks. The description of the architecture of the University of Cyprus/ KIOS CoE sandboxing environmnet used for extracting these datasets along with the full list of scenarios and their detailed implementation are described in the supporting documents.</span></p> <p><span>Brief description of each of the 3 scenarios of this SUC2 are provided below.</span></p> <p><span>The datasets for the first scenario (S1) of SUC2</span><span> examines the operation of a wide area protection scheme in a transmission line which receives data sent from PMUs at the two ends of the lines, when a short-circuit fault occurred in the range of the transmission line between buses 7 and 8 of the system. More details about the scenario SUC2/S1 related to this scenario's dataset can be found in Section </span><span>1.3.1</span><span> of the SUC2 supporting document. </span><span><span>The dataset includes electrical measurements of the current flow in line 7-8 (of the IEEE 9-bus system), in both magnitude and sinusoidal form</span><span>.</span><span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files, which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller as they were sent by the two PMUs, while the sine wave measurements were recorder through the OPAL-RT</span></span></p> <p><span>The datasets for second scenario (S2) of SUC2 investigates the operation of a wide area protection scheme which receives data sent from PMUs when a MITM FDI cyber-attack is conducted on the measurements of bus 7</span><span>, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the Typhoon controller via IEEE C37.118 protocol</span><span>. Section 1.3.2 of the SUC2 supporting document provides more details about the scenario related to this dataset. </span><span>This dataset includes electrical measurements of the current flow, in magnitude and sinusoidal format, of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of magnitude values were recorded by the Typhoon controller, while the data from the sinusoidal waveform were recorder by OPAL-RT. </span></p> <p><span>Thie dataset of the SUC2/S3 examines the operation of a wide area protection scheme which receives data sent from PMUs when a combined MITM with DoS cyber-attack is conducted, as actual attack, in the isolated communication network of the sandboxing environment, disrupting the C37.118 UDP communication exchanged between OPAL-RT 5707, where the digital twin of IEEE 9-bus system was implemented, and Typhoon controller. More details about this scenario associated to this dataset can be found in Section </span><span>1.3.3<span></span></span><span> of the supporting document of SUC2.</span></p> <p><span>This dataset includes electrical measurements of current’s flow magnitude of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset was recorded by the Typhoon controller, and it is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. In addition, the dataset includes network traffic packets captured as .pcapng<span> </span>and .csv files. <span> </span></span></p>
Dataset to Manuscript: Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils.
<p>Dataset to Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils.</p> <p>All published data is provided in the files "<strong>dd_</strong>". This includes:</p> <ul> <li>dd_cores: All data of soil cores and with depth</li> <li>dd_fractions: All data obtained from fractionation of the 0-3cm core layers</li> <li>dd_teabag: All data and mass losses of incubated teabags</li> <li>dd_temperature: All data and recorded soil temperatures</li> </ul> <p>All parameters and names are described in the corresponding file starting with "<strong>Var_names_</strong>". Details on methods and calculations are given in the manuscript and supporting information.</p> <p>NanoSIMS data is provided in the folder "<strong>dd_NanoSIMS.zip</strong>". This contains a file with descriptions of the provided tif-files "<strong>dd_NanoSIMS</strong>". Descriptions of the variables and parameters as well as further instructions are given in the file "<strong>Var_names_description_dd_NanoSIMS</strong>". Images and additional data can be requested by the corresponding author (marcusschiedung@gmail.com).</p> <p> </p> <p> </p>
A dataset recording joint EEG-fMRI during affective music listening
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Dataset for the paper "Aircraft wake vortices affecting airport wind measurements"
<p>Dataset in support of the paper "Aircraft wake vortices affecting airport wind measurements". The dataset contains the results of the manual classification as discussed in section 2 and 3 of the paper, details can be found there.</p><p>For each take-off, one row exists in the dataset. The columns are:</p><ul><li><i>takeoff_no</i>: int, Incrementing integer</li><li><i>timestamp</i>: string, UTC time the flight passes by the anemometer</li><li><i>flight_id</i>: string, Unique identifier for the flight</li><li><i>typecode</i>: string, ICAO aircraft typecode of the flight</li><li><i>wtc</i>: string: ICAO wake turbulence category of the flight</li><li><i>groundspeed_kts</i>: float, Groundspeed [kts] at the moment of passing by the anemometer</li><li><i>alt_above_thr_m</i>: float, Altitude above runway threshold [m] at the moment of passing by the anemometer</li><li><i>wind_speed_kts</i>: float, Wind speed [kts]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>wind_dir_deg</i>: float, Wind direction [°]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>is_event_visual_assessor_1</i>: int, Classification of assessor 1 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_2</i>: int, Classification of assessor 2 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_3</i>: int, Classification of assessor 3 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_sum</i>: int, Sum of classifications of 3 assessors (0 to 3)</li><li><i>is_event_wake_model</i>: float, Classification of wheather the flight caused a wake that hit the anemometer based on P2P wake model output (only applied to flights with a sum of classifications of 2 and more)</li><li><i>is_event</i>: int, Final classification of wheather the flight caused a wake that hit the anemometer</li></ul><p> </p>
How do native and non-native speakers recognize emotions in the instructor's voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners [dataset]
<p>Dataset for the journal article <em>How do native and non-native speakers recognize emotions in the instructor’s voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners.</em></p>
Dataset for KIOS CoE Sandboxing use-case SUC4 corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme (IEC 61850 GOOSE)
<p><span>The datasets reflect on two main scenarios (S1-S2) related to SUC4 - corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme. </span><span>The first scenario explores the response of the coordinated overcurrent protection when circuit breakers (CBs) are healthy, under normal operation, i.e., SUC4/S1(without attack), and the under a FDI cyberattack on IEC 61850 - GOOSE communication protocol, i.e., SUC4/S1(with FDI attack). </span>Similarly, the second scenario investigates the response of the coordinated overcurrent protection when there a mechanical failure in the CB of the downstream feeder, under normal operation, i.e., SUC4/S2(without attack), and the under a message suppresion (MS) cyber-attack on GOOSE protocol, i.e., SUC4/S2(with MS attack). Details regarding the datasets captured during the execution of each scenario (with and without attacks), including electrical measurements and network traffic, are briefly rsummarized below, while the full details are provided in the supporting documents.</p> <ul> <li><span><strong>SUC4/S1(without attack) datasets/Normal operation (without cyber-attack on GOOSE) when CBs are healthy </strong>: This dataset is related to the operation of the sandboxing use case SUC4 described in this document, which examines operation of the protection scheme in a substation using overcurrent protective relays (IEDs) in the sandboxing environment, that communicate with each other via IEC6180/GOOSE protocol. Specifically, this dataset corresponds to the first scenario (S1) of SUC4, without any attack. More details about the scenario related to this dataset can be found in Section 1.3.1 of the SUC4 supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.</span></li> <li><span><strong>SUC4/S1(with FDI attack) datasets/FDI cyber-attack on GOOSE signals when CBs are healthy</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(without attack) datasets/ Normal operation (without attack on GOOSE) when CB presents a failure</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of<br>the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(with MS attack) datasets/MS cyber-attack on GOOSE signals when CB presents a failure</strong>: This dataset corresponds to the second scenario (S2) of SUC4, where an MS cyber-attack is conducted in the local network in order prevent critical benign messages, such inter-trip messages requesting backup protection, to reach their destination (back-up IED) when a CB failure occurs during a short-circuit event. As a result, the duration of a short-circuit is prolonged or the protection scheme is not able to clear the short-circuit event, which can cause catastrophic failures to power system. More details about the scenario related to<br>this dataset can be found in Section 1.3.2 of the support document. The dataset includes electrical measurements of the upstream and downstream feeders of<br>the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> </ul>
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
Statistical analysis and dataset for: Invasive ant learning is not affected by seven potential neuroactive chemicals
<p>Linked to the journal article published in Current Zoology (<a href="https://doi.org/10.1093/cz/zoad001">https://doi.org/10.1093/cz/zoad001</a>).</p> <p><em><strong>Abstract</strong></em></p> <p>Argentine ants (<em>Linepithema humile</em>) are one of the most damaging invasive alien species worldwide. Enhancing or disrupting cognitive abilities, such as learning, has the potential to improve management efforts, for example by increasing preference for a bait, or improving ants’ ability to learn its characteristics or location. Nectar-feeding insects are often the victims of psychoactive manipulation, with plants lacing their nectar with secondary metabolites such as alkaloids and non-protein amino acids which often alter learning, foraging, or recruitment. However, the effect of neuroactive chemicals has seldomly been explored in ants. Here, we test the effects of seven potential neuroactive chemicals - two alkaloids: caffeine and nicotine; two biogenic amines: dopamine and octopamine, and three non-protein amino acids: β-alanine, GABA and taurine - on the cognitive abilities of invasive <em>L. humile</em> using bifurcation mazes. Our results confirm that these ants are strong associative learners, requiring as little as one experience to develop an association. However, we show no short-term effect of any of the chemicals tested on spatial learning, and in addition no effect of caffeine on short-term olfactory learning. This lack of effect is surprising, given the extensive reports of the tested chemicals affecting learning and foraging in bees. This mismatch could be due to the heavy bias towards bees in the literature, a positive result publication bias, or differences in methodology.</p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
Dataset: "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?"
<p>This repository contains the dataset presented in "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?" (https://doi.org/10.3390/ijerph20053798) as well as the SPSS syntax used for the statistical evaluation. Additionally, calibrated binaural recordings of the evaluated stimuli are provided as 32 bit .wav files, the values stored in those files correspond to pascals.</p>
Datasets for How is the Pandemic Affecting AGU Journal Article Submissions?
<p>These files provide tabular data on gender, age, and country of corresponding authors (the person submitting the manuscript to the peer review system) of American Geophysical Union (AGU) journals from January 2018 through April 2020. They supplement the article 'How is the Pandemic Affecting AGU Journal Article Submissions?' in Eos (https://eos.org/).</p>
Affective, physiological, and attention restoration at a wooden desk: A pilot study (Datasets and R analysis code)
<p>This entry contains datasets and R processing and analysis code for the article <em>Affective, physiological, and attention restoration at a wooden desk: A pilot study.</em><br> <br> The analysis primarily investigates how people respond to the Mental Arithmetic Task (MAT) in terms of their affective states and physiological arousal, and how their cognitive performance changes between two task administrations. The analysis also checks whether affective, physiological, and cognitive responses differ between settings furnished with or without wood. We base our analysis on self-reported affective states, captured physiological (electrodermal and cardiovascular) activity, and results on the cognitive task (i.e., MAT).</p>
Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification
<p>This repository contains data generated and used for classification in the publication:<br> Duthé, G.; Abdallah, I.; Barber, S.; Chatzi, E. Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 7262. https://doi.org/10.3390/en14217262</p> <p>The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model.</p> <p>One degradation run generates 1200 samples (1 sample every 6 days corresponding to a 20 year degradation period). In total 20 degradation runs are made available (20x1200 = 24'000 multivariate time-series samples). This repo can serve to benchmark long multivariate time-series classification algorithms. There are 10 possible classes of erosion severity.</p> <p>Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade:</p> <ul> <li>Inflow velocity</li> <li>Angle of attack</li> <li>Lift coefficient</li> <li>Drag coefficient</li> </ul> <p>Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.</p> <p> </p>
Dataset: How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?
<p><span>These datasets contain survey data that was used to evaluate the effect of the exposure to heatwave news texts on people’s preference for climate mitigation and adaptation actions, as presented in the manuscript titled “<em>How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?</em>”. Three versions of the dataset are available:</span></p> <ol> <li><strong>Original dataset</strong>: This version contains choice text as data points and includes all finished survey responses that passed the attention check questions (n=1209).</li> <li><strong>Original recoded dataset</strong>: This version was generated by recoding choice text into numerical values. The 'Income' variable, representing household income levels for both Canadian and US residents, was added by converting reported income ranges to a unified scale based on exchange rate equivalencies. The "Income_Canadians" and "Income_US" columns were subsequently removed to avoid repetitions. </li> <li><strong>Final dataset</strong>: This version excludes observations from participants who completed the survey in under four minutes and those who selected the same response for every item within each matrix-style question (also known as straight-lining). Additionally, responses with missing values in questions regarding political views, gender, and household income, as well as responses where participants identified as non-binary or indicated that their gender was not listed, were omitted (see “Methods” for more details). Dependent variables have been added based on the original responses, including personal-level mitigation and adaptation likelihoods, personal-level mitigation preference, and both non-weighted and weighted collective-level mitigation preference. Furthermore, the dataset includes a 'Climate Change Concern' variable, derived through principal component analysis of thirteen variables expressing participants’ climate change attitudes and efficacy beliefs concerning climate actions. Variables not used in the subsequent data analysis were removed. Age, political views, education, and income columns were standardized. The final dataset was used for the data analysis presented in the manuscript.</li> </ol> <p>The following variables/columns can be found across the three versions of the dataset:</p> <ul> <li>Dependent variables: <ul> <li>Starting with “<em>Personal_Mitigation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change mitigation actions</li> <li>Starting with “<em>Personal_Adaptation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change adaptation actions</li> <li>Starting with “<em>Collective_Mitigation</em>”: participant’s ranking of the collective-level climate change mitigation initiatives</li> <li>Starting with “<em>Collective_Adaptation</em>”: participant’s ranking of the collective-level climate change adaptation initiatives</li> <li><em>Personal_Mitigation_Likelihood</em>: personal-level mitigation likelihood (present only in the final dataset)</li> <li><em>Personal_Adaptation_Likelihood</em>: personal-level adaptation likelihood (present only in the final dataset)</li> <li><em>Personal_Preference</em>: personal-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Unweighted</em>: non-weighted collective-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Weighted</em>: weighted collective-level mitigation preference (present only in the final dataset)</li> </ul> </li> <li>Independent variables: <ul> <li><em>Group</em>: group that the participant was assigned to as part of the experimental intervention</li> <li><em>Distance</em>: indicates whether the participant was assigned to read about a heatwave occurring in their community or a city 6,000 km away (for experimental groups only)</li> <li><em>Severity</em>: indicates whether the participant was prompted to read about a heatwave without or with the mention of associated causalities (for experimental groups only)</li> </ul> </li> <li>Covariates and supporting variables: <ul> <li><em>Gender</em>: gender identity</li> <li><em>Identity</em>: ethnic and/or racial identity</li> <li><em>Age</em>: age</li> <li><em>Political_Views</em>: position on the liberal-conservative continuum</li> <li><em>Education</em>: highest level of education</li> <li><em>Country</em>: country of residence</li> <li><em>Canada_Province</em>: province or territory of residence (for Canadian participants only)</li> <li><em>US_State</em>: state of residence (for US participants only)</li> <li><em>Duration_Residence</em>: duration of residence in the current community</li> <li><em>Income_Canadians</em>: annual household income in Canadian dollars (for Canadian participants only)</li> <li><em>Income_US</em>: annual household income in US dollars (for US participants only)</li> <li><em>Income</em>: annual household income for both Canadian and US residents derived by converting reported income ranges to a unified scale based on exchange rate equivalencies</li> <li><em>Efficacy_Mitigation_Personal</em>: belief regarding the response efficacy of personal-level climate change mitigation actions</li> <li><em>Efficacy_Mitigation_Collective</em>: belief regarding the response efficacy of collective-level climate change mitigation actions</li> <li><em>Efficacy_Adaptation_Personal</em>: belief regarding the response efficacy of personal-level climate change adaptation actions</li> <li><em>Efficacy_Adaptation_Collective</em>: belief regarding the response efficacy of collective-level climate change adaptation</li> <li><em>Climate_Change_Importance:</em> perception of climate change as a personally important issue</li> <li>Climate_Change_Worry: level of worry about climate change</li> <li>Starting with “<em>Climate_Risk</em>”: beliefs regarding the degree of harm that climate change will cause to plants and animal species (Climate_Risk_Animals_Plants), future generations of people (Climate_Risk_Future_Generations), people in developing countries (Climate_Risk_Developing_Countries), people in participant’s country (Climate_Risk_Country), people in participant’s community (Climate_Risk_Community), and the participant personally (Climate_Risk_Personal)</li> <li>Climate_Change_Onset_Time: belief regarding when climate change will start harming people in their community</li> <li><em>Six_Americas_Segment</em>: the Global Warming's Six Americas segment participant aligns with derived based on the Six Americas Short SurveY (SASSY) Group Scoring Tool</li> <li><em>Climate_Change_Concern</em>: variable derived through PCA of thirteen variables expressing participants' climate change attitudes and efficacy beliefs pertaining to climate actions (present only in the final dataset)</li> <li><em>Survey_Duration_Seconds</em>: The amount of time it took the respondent to complete the survey</li> </ul> </li> </ul>
ScienceDex guides
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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.