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29,897 results for “activism”
Active thermography to estimate leaf heat transfer - leaf level data to manuscript
<p>Active thermography is an informative methodology to measure that we adopted to plant sciences to measure leaf heat transfer and derive spatial maps of thermal responsiveness of leaves. This method and its usability is described in a publication in 'Frontiers of Plant Sciences'.</p> <p>Here we publish the data-set of active thermography measurements that were used in this publication. The Mat-lab code that was developed with this publication is also published at Zenodo under the doi 10.5281/zenodo.1195869</p>
Andersen et al., 2018 - A UAV-based active AirCore system for measurements of greenhouse gases - Raw data
<p>Raw Picarro data sets and flight logs from the Lutjewad drone flights on September 13th 2016, along with the processed flight data, as presented in "A UAV-based active AirCore system for measurements of greenhouse gases" published in <em>Atmospheric Measurement Techniques </em>(<a href="https://doi.org/10.5194/amt-11-1-2018">https://doi.org/10.5194/amt-11-1-2018</a>)<em>. </em></p>
REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults
<p><strong>Methods and design</strong></p> <p><em>Participants</em></p> <p>We recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.</p> <p><em>Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p><em>Procedure</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48–72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15’ resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10’ warm-up on a cycle-ergometer at a power load of 20% of their individual VO<sub>2</sub> VAT, following by 30’ exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO<sub>2</sub> VAT (see Table 1). Upon completion of the exercise, a 10’ cool down period at 20% VO<sub>2</sub> VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm • min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5’ 44’’). The first flanker task was then performed for 6’, followed by a 15’ resting period with closed eyes. Finally, they again completed the 6’ flanker task.</p> <p><em>Flanker task</em></p> <p>We used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., <<<<< or >>>>>), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., <<><< or >><>>). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000–1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.</p> <p><em>EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 kΩ. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme & Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme & Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.</p> <p><em>Spectral power analysis</em>. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.</p> <p><em>Event-Related Spectral Perturbation (ERSP) analysis.</em> Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from –500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a −300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.</p>
Increasing the value of wind - From passive to active actors in the power markets (On-line Appendix)
<p>Mathematical formulations and input data for the evaluation of the different market participation strategies.</p>
Charter School Websites: A Physical Education and Physical Activity Content Analysis
<p>Excel file for data set associated with analysis of 520 California elementary charter school websites' mentioning of physical education and physical activity opportunities.</p>
Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data
<p>This dataset contains the data used in our manuscript titled "Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data". Data structure is explained in the README.txt file located at the top-level of the dataset. Manuscript title in the README.txt file and contained in the title of the zip file are of a previous working title.</p> <p>Please contact corresponding author Richard B. Woodward for any questions.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Wave 1 Questionnaires: Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia – EXTRACT_Political passivity
<p>This dataset contains the data underlying the following publication: Dahl, V., Amnå, E., Banaji, S., Landberg, M., Šerek, J., Ribeiro, N., ... & Zani, B. (2017). Apathy or alienation? Political passivity among youths across eight European Union countries.European Journal of Developmental Psychology, 1-18, https://doi.org/10.1080/17405629.2017.1404985.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Wave 2 Questionnaires
<p>Abstract</p> <p>WP7 was designed to address questions concerning young Europeans’ EU citizenship, in particular to shed light on factors relevant in processes of construction of citizenship and their joint workings by way of the quantitative analysis of questionnaire data provided by adolescents and young adults in various situations of life across eight EU countries representing variations in, e.g., their economic situation/crisis, political conditions, and their history as EU member state.</p> <p>This work package (WP7) sets out to address open questions concerning factors associated with youth’s active EU citizenship. In particular, assumed (directions of) influences of relevant factors and their joint workings will be examined among adolescents and young adults in various situations of life, across different EU countries representing variations in, e.g., economic situation/crisis, political conditions, and history as an EU member state. This dataset presents the second wave of data collection. WP7 wave 2 data collections have been completed at all eight participating sites in winter 2017/18. The national data sets have been checked and processed including analyses of measurement quality. Finally, a longitudinal cross-national data set has been established which is ready for analyses and available to all Consortium members.</p> <p>The dataset contains more than 7400 questionnaires submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>The data can be reused by researchers who want to compare our data with similar data collected at other points in historical time as well as in other countries than the eight European ones included. Likewise, stakeholders may be interested in reanalyzing our data particularly with a focus on descriptive survey-type information of European citizenship among youth across Europe. Earlier research on these issues is far more restricted in terms of the substantive scope, namely concerning insights into identity and behavioral aspects of participation on the European level and relevant processes, as well as in terms of the cross-national dimension including a larger number of countries from different European regions.</p> <p>File specifics</p> <p>This final working dataset is a cleaned version of raw data. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30 in both waves, or indicated age under 14 or over 30 in one wave and had a missing value on age in the other.</p> <p>Two filter variables based on participants’ age are present in the dataset:</p> <ul> <li>B_Filter_Age1 – selects participants who indicated age between 14-30 or did not indicate age at wave 2</li> <li>B_Filter_Age2 – selects participants who indicated age between 14-30 at wave 2</li> </ul> <p>Other technical variables refer to participants’ unique identification code (ID), country of data collection (Country), and cohort (Cohort; younger versus older). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Longitudinal (Wave 1 and 2) Questionnaires
<p>Abstract</p> <p>WP7 was designed to address questions concerning young Europeans’ EU citizenship, in particular to shed light on factors relevant in processes of construction of citizenship and their joint workings by way of the quantitative analysis of questionnaire data provided by adolescents and young adults in various situations of life across eight EU countries representing variations in, e.g., their economic situation/crisis, political conditions, and their history as EU member state.</p> <p>In the overall design of CATCH-EyoU, WP7 figures as a major link building on the initial theoretical work of WP2 by providing information that speaks to the assumptions based on the literature reviewed and the conceptual ideas developed at that point. Moreover, WP7 draws on results yielded by other work packages as far as available at a given stage of WP7 progress feeding into the construction of questionnaires, hypotheses to be tested, and interpretation of findings, respectively. The goal is to, in turn, contribute to an evidence-based finalization of WP2 conceptual efforts meant to integrate the empirical contributions of the different strands of CATCH-EyoU.</p> <p>At the core of WP7 is a longitudinal assessment using a two wave questionnaire data including a large sample of young people in all countries of the Consortium. After piloting instruments, the first wave of data collection took place in winter 2016/17. Questionnaires, details of the procedure, sample characteristics, and results of preliminary cross-sectional analyses were documented in D7.2 (Findings of wave1: A cross-national report. Noack, Macek 2017). A first set of substantive WP7 wave 1 findings have been presented in a special issue of the European Journal of Developmental Psychology (<a href="https://www.tandfonline.com/toc/pedp20/15/3">Volume 15, 2018 - Issue 3</a>; <a href="https://www.tandfonline.com/doi/full/10.1080/17405629.2017.1391087">Being both – A European and a national citizen? Comparing young people’s identification with Europe and their home country across eight European countrie</a>s. Landberg et.al 2017) as well as in various presentations addressing scientific and broader public audiences. The major reason for establishing a longitudinal data set is the study of processes of the development of major aspects of EU citizenship among youth which entails analyzing the influence of factors such as individual characteristics or conditions met in the family and in school on, e.g., young people’s identification with Europe and political participation. This implies to run analyses that capture the direction of effects as postulated on theoretical grounds which is not possible with a cross-sectional data set but requires sophisticated analyses of longitudinal data.</p> <p>The dataset contains more than 12600 questionnaires submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>The data can be reused by researchers who want to compare our data with similar data focusing on other aspects of attitudinal and identity development in youths. Likewise, stakeholders may be interested in reanalyzing our data particularly with a focus on descriptive survey-type information on the development of European citizenship in adolescence and early adulthood.</p> <p>File specifics</p> <p>This final working dataset is a cleaned version of raw data. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30 in both waves, or indicated age under 14 or over 30 in one wave and had a missing value on age in the other.</p> <p>Three general filter variables based on participants’ age are present in the dataset:</p> <ul> <li>Fltr_age1 – selects participants who indicated age between 14-30 at least in one wave (i.e. filters out all participants with unknown age)</li> <li>Fltr_age2 – selects participants who never indicated age beyond 14-30 (the age might be unknown)</li> <li>Fltr_age3 – selects participants who indicated age between 14-30 at least in one wave and never indicated age beyond 14-30</li> </ul> <p>The last filter variable is the strictest one (it combines the previous two) and it is recommended for use if it is essential that only people from the given age group are included in the analyses.</p> <p>Two additional filter variables based on participants’ age are provided for each wave (A_Filter_Age1, A_Filter_Age2, B_Filter_Age1, B_Filter_Age2). These filters can be used if data from wave 1 or wave 2 are analyzed separately.</p> <p>There are four general variables in the dataset: participants’ unique identification code (ID), country of data collection (Country), participant’s attendance in the study (Attn; indicating his or her participation in wave 1 only, wave 2 only, or both waves), and cohort (Cohort; younger versus older). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Names of variables from wave 1 start with letter “A_”, while names of variables from wave 2 start with letter “B_”.</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Wave 1 Questionnaires: Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia
<p>Abstract</p> <p>This work package (WP7) sets out to address open questions concerning factors associated with youth’s active EU citizenship. In particular, assumed (directions of) influences of relevant factors and their joint workings will be examined among adolescents and young adults in various situations of life, across different EU countries representing variations in, e.g., economic situation/crisis, political conditions, and history as an EU member state. This dataset presents the first wave of data collection. All teams collected data from a quite diverse sample of young people from their respective country.</p> <p>We achieved the targeted sample sizes due to our improved recruitment strategies (based on our experiences from the pilot assessment). More precisely, we could attract more than 10,400 young people to participate in our study (concrete numbers depend on sample selection). Since we initially set out to reach at least 6,400 young people, we were quite successful in our recruitment. Paper-and pencil as well as online modes of assessment proved to be equally effective.</p> <p>The data set integrates data resulting from the first submission wave (wave 1) of Catch-EyoU Questionnaire. The questionnaire was submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>Catch-EyoU Questionnaire was focused on attitudinal, identity, and behavioral aspects of European citizenship, on youth engagement and participation on the regional and national levels as well as on fostering and limiting conditions affecting these. Earlier research is far more restricted in terms of insights into identity and behavioral aspects of participation on the European level and relevant processes, as well as in terms of cross-national comparisons based on a substantial number of countries from different European regions.</p> <p>The data can be reused by researchers who want to compare our data with similar data collected at other points in historical time as well as in other countries than the eight European ones included. Likewise, stakeholders may be interested in reanalyzing our data particularly with a focus on descriptive survey-type information of European citizenship among youth across Europe.</p> <p>File specifics</p> <p>This final working dataset is a cleaned version of raw data. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30 in both waves, or indicated age under 14 or over 30 in one wave and had a missing value on age in the other.</p> <p>Two filter variables based on participants’ age are present in the dataset:</p> <ul> <li>A_Filter_Age1 – selects participants who indicated age between 14-30 or did not indicate age at wave 1</li> <li>A_Filter_Age2 – selects participants who indicated age between 14-30 at wave 1</li> </ul> <p>Other technical variables refer to participants’ unique identification code (ID), country of data collection (Country), and cohort (Cohort; younger versus older). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Cross-national Pilot Questionnaires
<p>Abstract</p> <p>This final working dataset is a cleaned version of raw data from our pilot study. The cleaning procedure involved (1) removing participants who left the survey on first pages of the questionnaire; (2) removing participants with clearly unreliable responses (i.e. choosing the same response option through the questionnaire); (3) removing participants who indicated age under 14 or over 30.</p> <p>The data set integrates the data resulting from 1390 Catch-EyoU Pilot Questionnaires submitted to adolescents and young adults from Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia.</p> <p>The purpose of creating this cross-national data set is to test and compare the quality of items and scales across countries. Given the nature of items and scales a comparable data base does not exist elsewhere.</p> <p>The data can be reused by researchers who want to run more in-depth analyses of the items and scales as well as to a limited extent for substantive analyses.</p> <p>File specifics</p> <p>Technical variables refer to participants’ unique identification code (ID), country of data collection (Country), cohort (Cohort; younger versus older), age group computed based on participants’ age (Agegroup_computed), type of assessment (Assess; paper versus online), and questionnaire version (Version; items were ordered differently between versions A and B). Please note that the cohort variable does not strictly refer to participants’ actual age, but it indicates whether he or she completed the questionnaire intended for adolescents (younger) or young adults (older).</p> <p>Data file is in a proprietary format (SPSS .sav format) but it can be opened through “R”, a free software environment for statistical computing and graphics, using different “R” libraries (such as “foreign”).</p>
dataset for: A Distance-Based Boolean Applicability Domain for category Quantitative Structure-Activity Relationship
<p>Dataset for category QSAR benchmarking</p> <p> </p>
CATCH-EyoU WP3: D3.2 Perspectives of Policy Makers on EU and on Youth Active Citizenship: a Cross-National Report
<p>Abstract</p> <p>The data set includes data from the analysis of semi-structured interviews collected with politicians, public officials and representatives of youth umbrella organizations at national and regional/local level.</p> <p>The aim of the interviews is to explore policy makers’ views on whether and to what extent European Youth policies have been implemented at national, regional and local levels in various member states. Moreover, policy makers are asked to identify actual and future challenges in the field of youth policies. Previous research has only partially answered to these questions; moreover, comparing different policy makers’ perspectives allows researchers to identify tensions and contradictions in policies implementation, providing new insight for improving policy implementation.</p> <p>The data can be reused by researchers who want to compare our data with similar data collected in different countries. Also, other stakeholders may be interested in reanalyzing our data for comparative aims.</p>
NIH BREATHE mHealth pediatric activity classification dataset
<p>The Biomedical REAl-Time Health Evaluation (BREATHE) platform is part of the Los Angeles PRISMS (Pediatric Research with Integrated Monitoring Systems) NIH-funded Center, which focuses on informatics platforms for mHealth (NIH/NIBIB U54 EB022002; PI: Alex Bui). This dataset was developed to better establish models for child activity classification based on smartwatch sensors. Specifically, a Motorola 360 smartwatch was worn by 21 subjects (age ranged 11-18), generating annotated data across different sensors (3-axis accelerometry, gyroscope, heart rate) for 6 different types of physical activity:three static postures (standing, sitting, lying) and three dynamic activities (walking, walking downstairs and walking upstairs). The study was motivated by the fact that current activity classifiers are typically geared towards <em>adult </em>bodies and patterns of motion; children exhibit different motion patterns, particularly as they grow. This dataset is not intended to be comprehensive, but rather a starting point for exploring differences in motion patterns across children (and relative to adults).</p>
CATCH-EyoU: Representation of the EU and Youth Active Citizenship in Educational Contexts: Cross-national Textbook Analysis
<p>The data set contains the results of the quantitative and qualitative content analysis of local textbooks from different subjects (e.g. ESL, EFL, History, Social Sciences, Civic Education) at ISCED 3 level (upper secondary schools as well as vocational schools). Data collection was carried out using a specifically designed analytical grid (Ribeiro; Ferreira & Menezes, 2016). The grid was tested and applied by national research teams (Portugal, Czechia, Estonia, Germany, Italy and Sweden) participating to Catch-EyoU project – each textbook analysis implied filling one grid. In total, 34 textbooks were analysed across the participating countries. All grids include excerpts of the textbooks (text or images, whenever legally possible). Quantitative analysis data and bibliographical metadata are also included.</p> <p>Data collection and analysis was aimed at getting an overall picture of school texts, including the analysis of number of paragraphs, pages and exercises about topics such as the EU, active citizenship, intercultural awareness, political involvement.</p> <p>Although textbook analysis is relatively common and there are other European projects that include it, the data analysis grid is original (and relatively innovative in the area) as it directly relates to the project topics.</p> <p>The potential users can be other researchers who might be interested not only in the grid but also in the analysis itself, either to use it with other textbooks or compare it with existing research or with similar data collected in different countries; teachers might also find it useful to explore the analysis as a basis for their practice. Also other stakeholders may be interested in reanalyzing our data for comparative aims.</p>
CODEX: Oscillatory brain activity during acute exercise: Tonic and transient neural response to an Oddball task.
<p><strong>2. Method</strong></p> <p><em>2.1.</em> <em>Participants</em></p> <p>We recruited 20 young males with a high level of aerobic fitness (age between 18-31 years old, average age 23.9 years old) from the University of Granada (Spain). All participants met the inclusion criteria of reporting at least 8 hours of cycling or triathlon training per week, normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders and were taking no medication. Note that high-fit cyclists and triathletes were selected because they are capable of maintaining a pedalling cadence at moderate-to-high intensity during long periods of time. Furthermore, they are able to keep a fixed posture over time, which reduces EEG movement artifacts considerably. Their fitness level was verified by an incremental effort test (see below). Participants were required to maintain a regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense physical activity 24 hours before each session. All subjects gave written informed consent before the study. The protocol was in accordance with both, the ethical guidelines of the University of Granada, and the Declaration of Helsinki.</p> <p> </p> <p><em>2.2. Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Oddball stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 100 cm between the head of participants and the centre of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p> </p> <p><em>2.3. Fitness Assessments</em></p> <p>Participants came to the laboratory at least one week before the first experimental session to provide the informed consent, complete an anthropometric evaluation (height, weight and body mass index [BMI]) and to familiarize with the oddball task. Subsequently, they performed an incremental cycle-ergometer test to obtain their VO<sub>2max</sub> that was used in the following experimental sessions to adjust the exercise intensity individually. The incremental effort test started with a 3 minutes warm-up at 30 Watts (W), with the power output increasing 10 W every minute. Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) during the warm-up period and was asked to maintain this cadence during the entire protocol. The test began at 60 W and was followed by an incremental protocol of 30 W every 3 minutes. Each step of the incremental protocol consisted of 2 minutes of stabilized load and 1 minute of progressive load increase (5 W every 10 seconds). The oxygen uptake (VO<sub>2</sub> ml • min<sup>-1 </sup>• kg<sup>-1</sup>), respiratory exchange ratio (RER; i.e., CO<sub>2</sub> production • O<sub>2</sub> consumption<sup>-1</sup>), relative power output (W • Kg<sup>-1</sup>) and heart rate (bpm) were continuously recorded throughout the test.</p> <p> </p> <p><em>2.4. Experimental sessions</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 100 min each. To avoid possible fatigue and/or training effects, visits to the laboratory were scheduled on different days allowing 48–72 hours between sessions. On each experimental session, after 10’ warm-up on a cycle-ergometer at a power load of 30% of their individual VO<sub>2max</sub>, participants performed an oddball task for 20’ while pedalling either at 30% (Light intensity exercise session) or 80% (Moderate-intensity exercise session) of their VO<sub>2max</sub>. Upon completion of the oddball task, a 10’ cool down period at 30% of intensity followed (see Table 1). Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions, as much as possible, in terms of dual-task demands.</p> <p> </p> <p><em>2.5. Oddball task</em></p> <p>The visual oddball task was based on that reported in Sawaki and Katayama (Sawaki & Katayama, 2007). It consisted of a random presentation of three visual stimuli: a frequent small blue circle (approximately 1.15º x 1.15º), a rare big blue circle (approximately 1.30º x 1.30º), and a rare red square (approximately 2.00º x 2.00º). Small blue circles were considered as standard stimuli (non-target), while big blue circles (target 1) and red squares (target 2) were considered as target stimuli. Stimuli were displayed sequentially on the centre of the screen on a black background. Each trial started with the presentation of a blank screen in a black background for 1200 ms. Then, the stimulus was presented at a random time interval (between 0 and 800 ms) during 150 ms. Participants were instructed to respond to both targets by pressing a button connected to the cycle-ergometer handlebar with the thumb of their dominant hand and to not respond when standard stimuli were shown. Participants were encouraged to respond as accurately as possible. The target stimuli were randomly presented in 20% of trials (10% of target 1, 10% target 2) and the non-target stimulus in the remaining 80% of trials. A total of 600 stimuli were presented. The task lasted for 20 minutes approximately. No breaks were allowed.</p> <p> </p> <p><em>2.6. EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ) to optimize signal transduction. Participants were instructed to avoid postural movements as much as possible, and to keep their gaze on the centre of the screen during the task. Electrode impedances were kept below 10 kΩ.</p>
De novo design of a fluorescence-activating β-barrel
<p>This dataset contains the supplementary materials for "<em>De novo</em> design of a fluorescence-activating beta barrel" (doi: 10.1038/s41586-018-0509-0). Detailed description is provided in the "List.docx" file.</p>
Target set-dependent activity cliffs
<p>Two databases comprising a total of 13,886<em> (i)sdMMP-cliffs</em> (93 target sets) and 8140 <em>(i)sdRMMP- cliffs</em> (73 target sets) are provided. For each of 93 and 73 target sets with sdMMP- and sdRMMP-cliffs, respectively, ChEMBL ID, target name, and corresponding PubChem assay IDs (AIDs) are reported and all ACs are provided including isdMMP-cliffs (21 of 93 sets) and isdRMMP-cliffs (eight of 73 sets). For each active AC compound, the ChEMBL ID, NonStereoAromaticSMILES (in-house version) representation, and potency value are given and for each inactive AC compound, the PubChem ID (CID) and SMILES representation.</p>
Sentinel-2 reference cloud masks generated by an active learning method
<p> <strong>Reference classifications generated with Active Learning for Cloud Detection (ALCD)</strong></p> <p>This data set provides a reference cloud mask data set for 38 Sentinel-2 scenes. These reference masks have been created with the ALCD tool, developed by Louis Baetens, under the direction of Olivier Hagolle at CESBIO/CNES[1]. They were created to validate the cloud masks generated by the MAJA software [2].</p> <p>- The `Reference_dataset` directory contains 31 scenes selected in 2017 or 2018.<br> - The `Hollstein` directory contains 7 scenes that were used to validate the ALCD tool by comparison to manually generated reference images kindlyprovided by Hollstein et al[3]<br> One of these scenes is present in both directories. For the validation of MAJA, the "Hollstein" scenes were not used because of their acquisition at a time period when Sentinel-2 was not yet operational, with a degraded repetitivity of observations.</p> <p><strong># Description of the data structure</strong><br> The name of each scene directory is the name of the corresponding Sentinel-2 L1C product.<br> In the scene directory, three sub-directories can be found.<br> - `Classification`<br> - `Samples`<br> - `Statistics`</p> <p><strong># Description of the files</strong><br> - `Classification/classification_map.tif` --- the main product, which is the classified scene. 7 classes are available. Each one is represented with a different integer.<br> 0: no_data.<br> 1: not used.<br> 2: low clouds.<br> 3: high clouds.<br> 4: clouds shadows.<br> 5: land.<br> 6: water.<br> 7: snow.</p> <p>- `Classification/confidence_enhanced.tif` --- enhanced confidence map of the classification. The values are between 0 and 255 (coded on 1 bit).<br> The original confidence map is, for each pixel, the proportion of votes for the majority class as the classification map has been created via a Random Forest algorithm.<br> A median filter has been applied to this confidence map. Finally, the value was saved on 1 bit, leading to the value being between 0 and 255.</p> <p>- `Classification/contours.png` --- the contours of the classes from the classification map, overlayed on the scene. The color code depends on each class.<br> Green: low and high clouds. Yellow: cloud shadows. Blue: water. Purple: snow.</p> <p>- `Classification/used_parameters.json` --- the parameters that were used to classify the scene. It includes the tile code, the cloudy and clear dates, along with their product reference.</p> <p>- `Samples/` --- this directory contains all the shapefiles, one per class.</p> <p>- `Statistics/k_fold_summary.json` --- results of the 10-fold cross-validation on the scene.<br> 5 metrics are computed, in the order given in the "metrics_names". "all_metrics" is a list of the 10 folds, with the 5 metrics in the correct order for each fold.<br> "means" and "stds" are the means and standard deviations of the 10 folds.</p> <p><br> <strong># References</strong></p> <p>[1] Baetens, L.; Desjardins, C.; Hagolle, O. Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. <em>Remote Sens.</em> <strong>2019</strong>, <em>11</em>, 433.</p> <p>[2] A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images, O Hagolle, M Huc, D. Villa Pascual, G Dedieu, Remote Sensing of Environment 114 (8), 1747-1755, 2010</p> <p>[3] Hollstein, A.; Segl, K.; Guanter, L.; Brell, M.; Enesco, M. Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images. Remote Sens. 2016, 8, 666</p>
Investigating Attitudinal Ambivalence via Sequential Priming: Evidence for the Simultaneous and Unintentional Activation of Opposite Evaluations
<p>This research tested a central assumption of attitudinal ambivalence research: ambivalent attitude objects simultaneously trigger positive and negative evaluations. It further specifies at which stage this activation is likely to produce an evaluative conflict. Experiments 1 to 3 involved two evaluative priming paradigms, in which ambivalent stimuli served either as primes or as targets. The <em>Ambivalent Primes Paradigm</em> tested the degree to which the concurrent and unintentional activation of positivity and negativity influences responding to univalent targets. The <em>Ambivalent Targets Paradigm</em> tested the degree to which ambivalent targets entail an evaluative response conflict irrespective of prime valence. Experiments 1 and 2 revealed slower responses for ambivalent trials compared to congruent trials in both paradigms. Employing a longer stimulus onset asynchrony, Experiment 3 attested to the short-lived nature of the joint activation of opposite evaluations triggered by ambivalent primes. In contrast, the deliberate categorization of ambivalent targets was not affected by this procedural variation. Finally, by relying on a valent/neutral categorization task, Experiments 4 and 5 indicate that conflicts triggered by ambivalent stimuli occur at the response selection rather than the exposure stage. Our findings lend original empirical support to the assumption that positivity and negativity are activated simultaneously and unintentionally in ambivalent attitude objects. Moreover, the present research suggests that ambivalence generates a conflict only if the task at hand requires a univalent categorization. We discuss the extent to which the activation of ambivalent attitudes may be automatic and the implications of our findings for dual-process models of attitudes.</p>
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.