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2,651 results for “habit”
Série de prises de vue d'habitations sur pilotis à Bangkok (Bangkok 2009)
<p>Corpus de photos servant à un projet de recherche sur les textscapes urbains et périurbains (street- et cityscape). </p>
NMNH Botany Plant Habit Data: NMNH Plant Growth Form Data
<p>Plant growth form data from specimen labels in the collections of the Smithsonian National Museum of Natural History Botany Department, revised draft.</p> <p> </p> <p>Excel version, revised March 2018. Recoded measurement types and values to FLOPO uris whenever possible.</p>
NMNH Botany Plant Habit Data: Habit (old version, obsolete)
<p>Plant growth form data from specimen labels in the collections of the Smithsonian National Museum of Natural History Botany Department, revised draft</p> <p> </p>
Raw and aggregated data for the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors". The study is based on the bibliographic and citation data contained in 729 articles published in 147 journals in 27 subject areas. The articles contained a total amount of 34,140 bibliographic references and 55,100 mentions and quotations overall.</p> <p>The dataset is composed of a series of files:</p> <ul> <li>the files "subject_area_<discipline-name>.csv" contain the raw data of the articles published in the journals of all the disciplines considered in the study;</li> <li>the file "article_data_summary.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article;</li> <li>the file "starred_metadata_set.csv" contains information about the most used subset of bibliographic metadata;</li> <li>the file "journals_selection.csv" contains information about all the journals selected for the study.</li> </ul>
Dataset for Nanoscale Growth Mechanisms of Gypsum: Implications for Environmental Control of Crystal Habits
<p>This dataset collected here has been used in the paper "Nanoscale Growth Mechanisms of Gypsum: Implications for Environmental Control of Crystal Habits".</p> <p>The files are in .dat format and each one corresponds to the images indicated in the file name.</p>
Anoline Lizard Food Habits
<p>The stomach contents of 10 adult individuals of each of three anole species (Anolis gundlachi, A. evermanni, and A. stratulus) were collected to determine the number, type, and volume of prey consumed following Hurricane Hugo. Gut contents were identified to the lowest taxon possible and were measured.</p>
Fig. 7. Begonia acetosella Craib. A. Plant habit. B – C. Leaf variation. D. Female bud. E. Female flower. F in A revision and one new species of Begonia L. (Begoniaceae, Cucurbitales) in Northeast India
Fig. 7. Begonia acetosella Craib. A. Plant habit. B – C. Leaf variation. D. Female bud. E. Female flower. F. Reverse of flower. G. Styles. Photographs by Rebecca Camfield of a plant in cultivation at the Royal Botanic Garden Edinburgh (accession 19980065).
Fig. 8. Astiella delicatula Jovet. A. Habit. B. Isostylous flower. C. Open isostylous flower. D in Description of 11 new Astiella (Spermacoceae, Rubiaceae) species endemic to Madagascar
Fig. 8. Astiella delicatula Jovet. A. Habit. B. Isostylous flower. C. Open isostylous flower. D. Ovary dissected to show placentation. E. Capsule. Drawn by Marijke Meersman. All from De Block et al. 2173 (BR).
Figure 1 in Review of sterlet (Acipenser ruthenus L. 1758) (Actinopterygii: Acipenseridae) feeding habits in the River Danube, 1694-852 river km
Figure 1. Map with nine locations along the River Danube (1694–852 river km) where sterlet diet was analysed.
FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family
FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B. Part of an old long shoot showing short shoots with their rosettes of foliage leaves (mainly) and bracts. C. Young, actively elongating long shoots with short shoots not yet fully developed in leaf axils; arrows indicate where a long shoot emerges from the apex of a short shoot. D. Long shoot densely covered with short shoots, the latter bearing flowers. Photographs: W. Swanepoel.
FIGURE 21. Miconia latidecurrens. A. Habit. B in Systematics of the Octopleura Clade of Miconia (Melastomataceae: Miconieae) in Tropical America
FIGURE 21. Miconia latidecurrens. A. Habit. B. Cauline leaf (abaxial view). C. Flower. D. Stamen, abaxial view (left), lateral view (right). E. Berry, top view (top), lateral view (bottom). F. Seed. Based on: A, B, E, F, McPherson 20808, MO; C, D, Aranda et al. 4226, CAS. Drawn by Sean V. Edgerton.
Lifestyle habits of adolescents before pandemic and one-year later
<p>The database includes variables related to lifestyle habits (i.e. free-time activities, maladaptive behaviors, sleep quality, screen use) and anxiety of a sample of students (N=153, 16 years mean age, 72% female) who were assessed before pandemic (T0) and one year later (T1).</p>
Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics
<p>Considering the global intensification of aridity in tropical biomes due to climate change, we need to understand what shapes the distribution of drought sensitivity in tropical plants. We conducted a pantropical data synthesis representing 1117 species to test whether xylem-specific hydraulic conductivity (K<sub>S</sub>), water potential at leaf turgor loss (Ψ<sub>TLP</sub>), and water potential at 50% loss of K<sub>S</sub> (ΨP50) varied along climate gradients. The Ψ<sub>TLP</sub> and ΨP<sub>50</sub> increased with climatic moisture only for evergreen species, but K<sub>S</sub> did not. Species with high Ψ<sub>TLP</sub> and Ψ<sub>P50</sub> values were associated with both dry and wet environments. However, drought-deciduous species showed high Ψ<sub>TLP</sub> and ΨP<sub>50</sub> values regardless of water availability whereas evergreen species only in wet environments. All three traits showed a weak phylogenetic signal and a short half-life. These results suggest that environmental controls on trait variance, which in turn is modulated by leaf habit along climatic moisture gradients in the tropics.</p>
Help Me study! Music Listening Habits While Studying (Dataset)
<p>This repository contains the raw data used for a research study that examined university students' music listening habits while studying. There are two experiments in this research study. Experiment 1 is a retrospective survey, and Experiment 2 is a mobile experience sampling research study.</p><p>This repository contains five Microsoft Excel files with data obtained from both experiments. The files are as follows:</p><ul><li><i>onlineSurvey_raw_data.xlsx</i></li><li><i>esm_raw_data.xlsx</i></li><li><i>esm_music_features_analysis.xlsx</i></li><li><i>esm_demographics.xlsx</i></li><li><i>index.xlsx</i></li></ul><p><strong>Files Description</strong></p><p><i><strong>File: onlineSurvey_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 1, including the (anonymised) demographic information of the sample. The sample characteristics recorded are:</p><ul><li>studentship</li><li>area of study</li><li>country of study</li><li>type of accommodation a participant was living in</li><li>age</li><li>self-identified gender</li><li>language ability (mono- or bi-/multilingual)</li><li>(various) personality traits</li><li>(various) musicianship</li><li>(various) everyday music uses</li><li>(various) music capacity</li></ul><p>The file also contains raw data of responses to the questions about participants' music listening habits while studying in real life. These pieces of data are:</p><ul><li>likelihood of listening to specific (rated across 23) music genres while studying and during everyday listening.</li><li>likelihood of listening to music with specific acoustic features (e.g., with/without lyrics, loud/soft, fast/slow) music genres while studying and during everyday listening.</li><li>general likelihood of listening to music while studying in real life.</li><li>(verbatim) responses to participants' written responses to the open-ended questions about their real-life music listening habits while studying.</li></ul><p><i><strong>File: esm_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 2, including the following variables:</p><ul><li>information of the music tracks (track name, artist name, and if available, Spotify ID of those tracks) each participant was listening to during each music episode (both while studying and during everyday-listening)</li><li>level of arousal at the onset of music playing and the end of the 30-minute study period</li><li>level of valence at the onset of music playing and the end of the 30-minute study period</li><li>specific mood at the onset of music playing and the end of the 30-minute study period</li><li>whether participants were studying</li><li>their location at that moment</li><li>(if studying) whether they were studying alone</li><li>(if studying) the types of study tasks</li><li>(if studying) the perceived level of difficulty of the study task</li><li>whether participants were planning to listen to music while studying</li><li>(various) reasons for music listening</li><li>(various) perceived positive and negative impacts of studying with music</li></ul><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated data indicate that the participant did not respond to the questionnaire (i.e., missing data).</p><p><i><strong>File: esm_music_features_analysis.xlsx</strong></i></p><p>This file presents the music features of each recorded music track during both the study-episodes and the everyday-episodes (retrieved from Spotify's "Get Track's Audio Features" API). These features are:</p><ul><li>energy level</li><li>loudness</li><li>valence</li><li>tempo</li><li>mode</li></ul><p>The contextual details of the moments each track was being played are also presented here, which include:</p><ul><li>whether the participant was studying</li><li>their location (e.g., at home, cafe, university)</li><li>whether they were studying alone</li><li>the type of study tasks they were engaging with (e.g., reading, writing)</li><li>the perceived difficulty level of the task</li></ul><p><i><strong>File: esm_demographics.xlsx</strong></i></p><p>This file contains the demographics of the sample in Experiment 2 (N = 10), which are the same as in Experiment 1 (see above).</p><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated demographic data indicate that the participant did not respond to the questionnaire (i.e., missing data). </p><p><i><strong>File: index.xlsx</strong></i></p><p>Finally, this file contains all the abbreviations used in each document as well as their explanations.</p>
Classification of obesity levels based on eating habits and physical condition
<p>This dataset encompasses information intended for the assessment of obesity levels among individuals in the nations of Mexico, Peru, and Colombia.</p><p>The main dataset is prepared by other authors in the article (https://doi.org/10.1016/j.dib.2019.104344) I have only used this dataset to perform my final project related to the Homework Assignment 6: Machine Learning Application in Project Dataset. <br>Here is some detaied explanation about the dataset:<br> </p><p><strong>The attributes related with eating habits are:</strong></p><ol><li>Frequent consumption of high caloric food (FAVC)</li><li>Frequency of consumption of vegetables (FCVC)</li><li>Number of main meals (NCP)</li><li>Consumption of food between meals (CAEC)</li><li>Consumption of water daily (CH20)</li><li>Consumption of alcohol (CALC)</li></ol><p><strong>The attributes related with the physical condition are:</strong></p><ol><li>Calories consumption monitoring (SCC)</li><li>Physical activity frequency (FAF)</li><li>Time using technology devices (TUE)</li><li>Transportation used (MTRANS)</li></ol><p><strong>other variables obtained were:</strong></p><ol><li>Gender</li><li>Age</li><li>Height</li><li>Weight</li><li>family history with overweight</li><li>SMOKE activity</li></ol><p>Finally, all data was labeled and the class variable NObesity was created with the values of:</p><p>a) Insufficient Weight</p><p>b) Normal Weight</p><p>c) Overweight Level I</p><p>d) Overweight Level II</p><p>e) Obesity Type I</p><p>f) Obesity Type II</p><p>g) Obesity Type III</p>
Data and code for "Carbon cycle instability for high-CO2 exoplanets: implications for habitability"
<p>Data supporting "Carbon cycle instability for high-CO2 exoplanets: implications for habitability," Graham and Pierrehumbert, 2024, <em>Astrophysical Journal </em>(under review at time of upload)</p> <ul> <li>Global climate model (GCM) simulation output necessary to reproduce figures in paper. Netcdf (.nc) format. <ul> <li>For simulations receiving instellation of 1250 W m-2, the format is atmos_monthly_[co2 mixing ratio]ppmv_S1250.nc</li> <li>For other simulations, the format is: atmos_monthly_[co2 partial pressure]bar_S[675, 750, 800, or 1000].nc</li> </ul> </li> <li>Continental configuration land mask used in GCM simulations. Netcdf (.nc) format. <ul> <li>land.nc</li> </ul> </li> <li>Python scripts to<br> <ul> <li>post-process climate simulation data to calculate global weathering rates according to WHAK or MAC weathering <ul> <li>isca_weathering_calc.py</li> </ul> </li> <li>reproduce all model-based figures <ul> <li>figures 1-8: isca_plot_maker.py</li> <li>figure 9: isca_weathering_calc.py</li> </ul> </li> </ul> </li> </ul>
Dataset for Polarized Signatures of the Earth Through Time: An Outlook for the Habitable Worlds Observatory
<p>The search for life beyond the Solar System remains a primary goal of current and near-future missions, including NASA's upcoming Habitable Worlds Observatory (HWO). However, research into determining the habitability of terrestrial exoplanets has been primarily focused on comparisons to modern-day Earth. Additionally, current characterization strategies focus on the unpolarized flux from these worlds, taking into account only a fraction of the informational content of the reflected light. Better understanding the changes in the reflected light spectrum of the Earth throughout its evolution, as well as analyzing its polarization, will be crucial for mapping its habitability and providing comparison templates to potentially habitable exoplanets. Here we present spectropolarimetric models of the reflected light from the Earth at six epochs across all four geologic eons. We find that the changing surface albedos and atmospheric gas concentrations across the different epochs allow the habitable and non-habitable scenarios to be distinguished, and diagnostic features of clouds and hazes are more noticeable in the polarized signals. We show that common simplifications for exoplanet modeling, including Mie scattering for fractal particles, affect the resulting planetary signals and can lead to non-physical features. Finally, our results suggest that pushing the HWO planet-to-star flux contrast limit down to 1 $\times$ 10$^{-13}$ could allow for the characterization in both unpolarized and polarized light of an Earth-like planet at any stage in its history.</p> <p>This dataset contains all modeled spectropolarimetric signatures used to conduct this work. Each .zip file contains the output folders for one of the four eons: Hadean, Archean, Proterozoic, and Phanerozoic (i.e., Modern). Each of these top-level folders then contains individual folders for each specific model run. The names for each of these individual folders describes the specific model. Finally, each individual folder then contains files for the spectropolarimetric signatures, where each file contains four columns of data representing the spectra for that specific model at that specific planetary phase angle. Please refer to the README.txt as well as the accompanying manuscript for more information.</p> <p>Version 2 of this dataset includes the updated models for the Archean Earth with hazy atmospheres that now extend their spectra from the original 1.2um limit to 1.8um.</p>
Data from: Evolution of dispersal, habit, and pollination in Africa pushed Apocynaceae diversification after the Eocene-Oligocene climate transition
<p>Apocynaceae (the dogbane and milkweed family) is one of the ten largest flowering plant families, with approximately 5,350 species and diverse morphology and ecology, ranging from large trees and lianas that are emblematic of tropical rainforests, to herbs in temperate grasslands, to succulents in dry, open landscapes, and to vines in a wide variety of habitats. Despite a specialized and conservative basic floral architecture, Apocynaceae are hyperdiverse in flower size, corolla shape, and especially derived floral morphological features. These are mainly associated with the development of corolline and/or staminal coronas and a spectrum of integration of floral structures culminating with the formation of a gynostegium and pollinaria—specialized pollen dispersal units. To date, no detailed analysis has been conducted to estimate the origin and diversification of this lineage in space and time. Here, we use the most comprehensive time-calibrated phylogeny of Apocynaceae, which includes approximately 20% of the species covering all major lineages, and information on species number and distributions obtained from the most up-to-date monograph of the family to investigate the biogeographical history of the lineage and its diversification dynamics. South America, Africa, and Southeast Asia (potentially including Oceania), were recovered as the most likely ancestral area of extant Apocynaceae diversity; this tropical climatic belt in the equatorial region retained the oldest extant lineages and these three tropical regions likely represent museums of the family. Africa was confirmed as the cradle of pollinia-bearing lineages and the main source of Apocynaceae intercontinental dispersals. We detected 12 shifts toward accelerated species diversification, of which 11 were in the APSA clade (apocynoids, Periplocoideae, Secamonoideae, and Asclepiadoideae), eight of these in the pollinia-bearing lineages and six within Asclepiadoideae. Wind-dispersed comose seeds, climbing growth form, and pollinia appeared sequentially within the APSA clade and probably work synergistically in the occupation of drier and cooler habitats. Overall, we hypothesize that temporal patterns in diversification of Apocynaceae was mainly shaped by a sequence of morphological innovations that conferred higher capacity to disperse and establish in seasonal, unstable, and open habitats, which have expanded since the Eocene-Oligocene climate transition.</p>
Figure 8 – Streptocarpus schaijesii. A, C, E. Habit. B, D in Five new species of Streptocarpus (Gesneriaceae) from Katanga, D.R. Congo
Figure 8 – Streptocarpus schaijesii. A, C, E. Habit. B, D. Flower. Photographs taken by Michel Schaijes at Musokatanda (A–C, Schaijes 1695) and Biano, Temke (D, E, Schaijes 2282). © Michel Schaijes, all rights reserved.
Figure 7 – Streptocarpus schaijesii. A–B. Habit. C–D. Partial inflorescence. E. Flower, lateral view. F in Five new species of Streptocarpus (Gesneriaceae) from Katanga, D.R. Congo
Figure 7 – Streptocarpus schaijesii. A–B. Habit. C–D. Partial inflorescence. E. Flower, lateral view. F. Corolla dissected, showing position of stamens and staminodes. G. Stamens. H. Staminode. A, C–H from Schaijes 1695 and B from Malaisse 13809. Illustration by Eberhard Fischer.
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.