Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
141
datasets available to search
ShareScore release 0.9.0
Dataset results
141 results for “Behavioral assessment”
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
A QUESTIONNAIRE FOR THE ASSESSMENT OF VIOLENT BEHAVIORS IN YOUNG COUPLES: THE ITALIAN VERSION OF DATING VIOLENCE QUESTIONNAIRE (DVQ)
<p> In the last years, intimate partner violence (IPV) became a relevant problem for community and for social life, particularly in young people. Its correct assessment and evaluation in the population is mandatory. Our objectives were: Confirm factor structure of Dating Violence Questionnaire (DVQ) and investigate its convergent and divergent validity. The DVQ along with other personality measures were filled by a sample of 418 university students (Females = 310) of average age of 23 y.o. (SD = 4.71). A subsample of participants (223 students) consented in being involved also in retest and filled also the Revised Eysenck Personality Questionnaire (short form) and a brief scale for describing the behavior of the (past) partner after the breaking of the relationship (BRS). The 8-factor structure, with respect to the two other competing models, reported better fit indexes and showed significant correlations with other personality measures. Personality traits, both Neuroticism and Psychoticism, correlated with Sexual Violence, while Detachment correlated only with Neuroticism and Coercion, Humiliation and Physical Violence correlated with only Psychoticism. Extraversion did not report significant relationships with any of the 8 DVQ factors. Also the predictive validity of DVQ was satisfactory with the partner violent reaction to the break of relationship predicted positively predicted by Coercion (b = 0.22) and by Humiliation (b = 0.20) and negatively by Emotional Punishment (b = -0.18). The present results indicate a good factor structure of the questionnaire, and interesting correlations with personality traits, allowing to identify psychological aspects with a predisposing role for anti-social aggressive behaviors. Further studies will be aimed at ascertaining other possible determinants of intimate partner violence and the weight of cultural aspects.</p>
Predictors of medical staff's knowledge, attitudes, and behavior of dysphagia assessment: A cross-sectional study
<p>This study aimed to develop training resources and standardize the assessment of dysphagia in patients with stroke. This study was a cross-sectional study. A total of 430 nurses and doctors from four provinces(Guangdong Province, Hunan Province, Guangxi Province, and Shaanxi Province) who were selected by convenience sampling were invited to complete the questionnaire through WeChat, DingTalk, and Tencent QQ from May 23 to 31, 2022. A self-reported questionnaire was used to assess participants' Knowledge, Attitude, and Behavior regarding dysphagia. Participants' sociodemographic, training, and nursing experience were measured using the general information sheet and assessed as potential predictors of medical staff's Knowledge, Attitudes, and Behavior of dysphagia assessment. A multiple linear regression model was used to identify the factors predicting medical staff's Knowledge, Attitudes, and Behavior regarding dysphagia assessment. The mean scores for Knowledge, Attitudes, and Behavior of dysphagia assessments were 92.654(SD 17.519). Multiple linear regression results indicated that experience in dysphagia patients' nursing, related training for dysphagia, working years in the field of dysphagia-related diseases, specialized training in geriatric, swallowing & rehabilitation, and department related to neurology, rehabilitation & elderly were significant predictors, accounting for 35.1% of the variance in scores of medical staff's Knowledge, Attitudes and Behavior of dysphagia assessment. Our findings imply that nursing experience, training, and work for patients with swallowing disorders could have positive effects on the Knowledge, Attitudes, and Behavior of medical staff regarding dysphagia assessment. Hospital administrators should provide relevant resources, such as videos of dysphagia assessment, training centers for the assessment of dysphagia, and swallowing specialist nurses. It is important that health policies fully recognize the role of training and support systems in caring for people with dysphagia.</p>
Data for "Re-weighing the 5% tagging recommendation: assessing the potential impacts of tags on the behavior and body condition of bats"
<p>Database as tab-delimited (.csv) associated with the publication: </p> <p>Meierhofer M.B., et al. (2024) Re-weighing the 5% tagging recommendation: assessing the potential impacts of tags on the behavior and body condition of bats. <em>Mammal Review.</em></p> <p>Please refer to the main publication for a detailed description. An explanation of the database is available in the Metadata file uploaded alongside the database. R code to reproduce the analysis pipeline is available on GitHub:</p> <p>https://github.com/melissameierhofer/Meta-5-Rule.git</p>
Fig. 5 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)
Fig. 5. Spodoptera frugiperda moths prefer to oviposit on uninfested Zea mays plants. To test the effect of prior infestation with S. frugiperda (FAW) compared to a non-treated (NT) control plant on S. frugiperda oviposition preference, a pair-wise oviposition assay was performed using 3 independent experiments (Experiments 1–3). In each experiment, 6 uninfested plants and 6 infested plant treatments were used, and egg masses on each plant counted (Table). The total number of egg masses on each treatment was determined and from these data the percent total oviposition (%NT and %infested plant) calculated. The graph shows the mean (± SE) percent parasitism for each treatment,and the treatments were statistically significantly different using a pair-wise t-test: P ≤ 0.05; n = 3.
Fig. 6. Cotesia marginiventris wasps have a in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)
Fig. 6. Cotesia marginiventris wasps have a marginal preference to oviposit on Spodoptera frugiperda on W22 compared to B104 Zea mays inbred plants. To test the effect of different Z. mays varieties on oviposition preference of C. marginiventris, a pair-wise oviposition assay was performed using 13 independent experiments (Experiments 1–13). In each experiment, the number of S. frugiperda larvae recovered from B104 or W22 genotypes that were parasitized by C. marginiventris (P), not-parasitized (NP), or had died shortly afer collection (D), and the percentage of larvae parasitized was calculated by (P/[P + NP]) × 100 for each plant variety (Table). Experiments that had less than 5 parasitized larvae or more than 15 dead (bold) were discarded. The graph shows mean (± SE) percentage parasitism for each treatment. The treatments were not significantly different using a pair-wise t-test with P ≤ 0.05 and n = 8.
Fig. 3 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)
Fig. 3. Stages of Cotesia marginiventris development. Representative images of C. marginiventris adult (A), larva emerging from S. frugiperda host (B) and pupae (C) are shown. Size bars are 20 µm.
Fig. 4 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)
Fig. 4. Larval growth assays of Spodoptera frugiperda. Two independent larval growth assays for S. frugiperda on the Zea mays inbred line B104. Graph shows mean larval weights (± SE) from 3 to 7 d afer infestation, n = (35–100). Because these growth assays were done at different times, they were not statistically compared.
Fig. 2 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)
Fig. 2. Spodoptera frugiperda developmental stages. Line drawing of differences between male and female S. frugiperda pupae (A). Representative images of S. frugiperda larva (B), pupae (C), adults (D), and egg masses (E).
Data from: Novel approaches for assessing acclimatization in birds reveal seasonal changes in peripheral heat exchange and thermoregulatory behaviors
<p>Using thermography and behavioral analyses, we found that heat exchange and thermoregulatory behaviors changed seasonally in chipping sparrows (<em>Spizella passerina</em>). Studies on seasonal acclimatization in birds have primarily involved metabolic measurements, few of which have investigated behaviors, and none have investigated changes in peripheral heat exchange. We captured chipping sparrows in the winter and summer of 2022 in Wilmington, North Carolina, and we collected thermal images of these birds at 15.0°, 27.5°, and 40.0°C. We found that heat dissipation through the bill and legs changed seasonally, but surprisingly both were higher in winter than in summer. We found that heat dissipating behaviors were more common in winter, whereas heat conserving behaviors were more common in summer, and that behaviors associated with resource costs (e.g., panting) or predation risk (e.g., bill tucking) showed the most distinct differences between seasons. Meanwhile, low-cost and low-risk postural adjustments (e.g., feather adjustments and tarsus exposure) did not vary as strongly between seasons but followed similar trends. The seasonal adjustments to behaviors suggest that non-acclimatized birds must use costly thermoregulatory behaviors more frequently than acclimatized birds. The use of thermography catalyzed the discovery of one completely novel behavior, and the first detection of a known behavior in a new species. Both novel behaviors aided in evaporative heat loss and occurred more commonly in winter, supporting the presence of seasonal acclimatization as evidenced by behavioral adjustments. These results provide novel insights to the process of acclimatization and suggest a role of behavioral adjustments in seasonal acclimatization.</p>
Data from: Assessing the association between animal color and behavior: A meta-analysis of experimental studies
Open the record for dataset details and reuse information.
Predictors of medical staff’s knowledge, attitudes, and behavior of dysphagia assessment: A cross-sectional study
Open the record for dataset details and reuse information.
Data from: Novel approaches for assessing acclimatization in birds reveal seasonal changes in peripheral heat exchange and thermoregulatory behaviors
Open the record for dataset details and reuse information.
Ultimate strength assessment of stiffened panel using non-linear mechanical behavior of an equivalent single layer: grillage FE model used for analysis
<p>This example shows how the ESL can be applied in the ultimate strength structural analysis in Abaqus finite element software. In other words, ESL methodology is applied only in some parts of the structure while larger structural supporting components like girders and webframes are still modeled explicitly. FIles include also the Full_3D_FEM model used for validating the ESL model.</p> <p>Dataset includes following files:</p> <p>1. ESL_nonlinear_grillage.inp - this is Abaqus input file for running the ESL nonlinear grillage model.</p> <p>2. ugensFINALv_master.for - this defines the nonlinear stiffness or ABD matrix. This is called by input file (ESL_nonlinear_grillage.inp ).</p> <p>3. Full_3D_FEM.inp - Full_3D_FEM model used for validating the ESL model.</p> <p> </p>
Accuracy of online survey assessment of mental disorders and suicidal thoughts and behaviors in Spanish university students. Results of the WHO World Mental Health-International College Student initiative.
<p>This dataset contains clinical data about 287 university students that participated in a clinical reappraisal study with the objective of examining the accuracy of WMH-ICS online screening scales for evaluating four common mental disorders (Major Depressive Episode, Mania/Hypomania, Panic Disorder, Generalized Anxiety Disorder) and suicidal thoughts and behaviors used in a survey of Spanish university students(UNIVERSAL project).</p>
Fig. 1 in Approaches for assessing the impact of Zea mays (Poaceae) on the behavior of Spodoptera frugiperda (Lepidoptera: Noctuidae) and its parasitoid Cotesia marginiventris (Hymenoptera: Braconidae)
Fig. 1. Spodopera frugiperda rearing for use in oviposition assays.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.