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4,243 results for “SEASONALITY”
Figure 4 in Consistent seasonal polyphenism in male genitalia of three Leptidea butterfly species (Lepidoptera: Pieridae)
Figure 4. Two-dimensional principal components scatterplots of genital structures in form space.
Diurnal variations of brown carbon during two distinct seasons in a megacity in Northeast China
<p>Diurnal variations of brown carbon during two distinct seasons in a megacity in Northeast China</p>
EAPBSdata: Energy consumption and bee dataset of five connected beehives of one high season in France
<p><strong>1- Introduction</strong></p> <p>This dataset was developed in the context of our work [1] that focuses on the orchestration of tasks between edge devices and cloud servers, and the energy optimization of orchestration scenarios applied to smart beehives. [1] introduces an energy-aware precision beekeeping system capable of collecting bee-related data and its own energy consumption. This system was deployed during the 2022 beekeeping high season in 5 beehives, 3 of which located in Lyon, France, and the 2 others in Cachan, France.</p> <p><strong>2- Description</strong></p> <p><strong>Hardware</strong></p> <p>The energy-aware precision beekeeping system relies on the Raspberry Pis:</p> <ul> <li>One Raspberry Pi 3b+ that periodically wakes up, collects bee-related data, transfers them and shuts down.</li> <li>One Raspberry Pi Zero WH that constantly collects the energy consumption of itself, the Raspberry Pi 3b+ and the photovoltaic intake.</li> </ul> <p>The energy node of our system relies on a solar panel, current converter, and battery. The solar panel is a monocrystalline 30 W panel. The battery is a 20 000 mAh power bank. Between the two, the current is converted to 5 volts (the adequate voltage for the battery) with a DC/DC Step-Down 5 volts/3 amperes converter.</p> <p>The current sensors connected to the Raspberry Pi Zero are Grove ±5A DC/AC current sensors based on the Allegro ACS70331 current sensor, which is based on giant magneto-resistivity sensing technology. This sensor is selected because of its adapted sensing range (0 to 5 A) and its wide temperature operating range (-40°C to 85°C), as the later-introduced setup operates in a sealed case in an outdoor environment. The current collection script is written in Python 3 and relies on Grove’s package functions.</p> <p><strong>Data collection routines</strong></p> <p>The Raspberry Pi Zero, which records the current, is always switched on. Every twelve minutes (except for beehive 5, where this frequency varies), it sends a signal through GPIO to wake up the other Raspberry Pi, the beehive data recorder. Once the Raspberry Pi 3b+ wakes up, it collects data from all its sensors, including three 10-second audio samples collected at the same time, five 800×600 pixels images spread over five seconds and temperature and humidity measurements of the inside of a beehive, and it transfers the data through Wi-Fi to a remote data storage cloud server. In addition, at regular intervals, the Raspberry Pi Zero WH transfers the latest consumption data.</p> <p>For now, we share the current data and the temperature/humidity. Later, we will share the audio and the image datasets.</p> <p>The date of the beehive data files from beehives 1, 2 and 3 do not match the current data due to the lack of network during the acquisition process. It prevented the Raspberry Pi 3b+ to update its internal clock during wake-up.</p> <p><strong>File Structure:</strong></p> <p>Beehives 1, 2 and 3 are located on the roof of a university (ENS de Lyon) in Lyon, whereas beehives 4 and 5 are located on the roof a university (aivancity Paris-Cachan) in Cachan. Beehives 1 and 4 met technical difficulties which lowered the amount of collected data, whereas beehives 2, 3 and 5 gathered data from April to July 2022</p> <table> <tbody> <tr> <td> <p><strong>Folder</strong></p> </td> <td> <p><strong>Number of files</strong></p> </td> </tr> <tr> <td> <p>beehive1/consumption</p> </td> <td> <p>6</p> </td> </tr> <tr> <td> <p>beehive1/temphumi</p> </td> <td> <p>37</p> </td> </tr> <tr> <td> <p>beehive2/consumption</p> </td> <td> <p>4158</p> </td> </tr> <tr> <td> <p>beehive2/temphumi</p> </td> <td> <p>2598</p> </td> </tr> <tr> <td> <p>beehive3/consumption</p> </td> <td> <p>3503</p> </td> </tr> <tr> <td> <p>beehive3/temphumi</p> </td> <td> <p>3120</p> </td> </tr> <tr> <td> <p>beehive4/consumption</p> </td> <td> <p>182</p> </td> </tr> <tr> <td> <p>beehive4/temphumi</p> </td> <td> <p>261</p> </td> </tr> <tr> <td> <p>beehive5/consumption</p> </td> <td> <p>1915</p> </td> </tr> <tr> <td> <p>beehive5/temphumi</p> </td> <td> <p>1736</p> </td> </tr> </tbody> </table> <p><strong>3- Citation</strong></p> <p>Works citing this dataset should also refer to [1] as the article that introduces the system used to collect the data.</p> <p><strong>4- Acknowledgment </strong></p> <p>The research that led to this dataset was made possible thanks to the funding from aivancity School for Technology, Business & Society Paris Cachan, and the contribution of École Normale Supérieure for the hardware.</p> <p> </p> <p>[1] H. Hadjur, D. Ammar, and L. Lefèvre, “Services Orchestration at the Edge and in the Cloud for Energy-Aware Precision Beekeeping Systems” 5th Workshop on Parallel AI and Systems for the Edge (PAISE 2023), an IPDPS workshop, 2023.</p> <p> </p>
High resolution aerial imagery of barley over a growing season
<p><span class="TextRun SCXW237352169 BCX0"><span class="NormalTextRun CommentStart SCXW237352169 BCX0">This dataset consists of </span><span class="NormalTextRun SCXW237352169 BCX0">unprocessed</span><span class="NormalTextRun SCXW237352169 BCX0"> images</span><span class="NormalTextRun SCXW237352169 BCX0"> and </span><span class="NormalTextRun SCXW237352169 BCX0">orthomosaic imagery</span><span class="NormalTextRun SCXW237352169 BCX0"> </span><span class="NormalTextRun SCXW237352169 BCX0">of a barley field in Bozeman</span><span class="NormalTextRun SCXW237352169 BCX0">, </span><span class="NormalTextRun SCXW237352169 BCX0">Montana</span><span class="NormalTextRun SCXW237352169 BCX0">,</span><span class="NormalTextRun SCXW237352169 BCX0"> collected throughout the growing season from emergence to maturity. The </span><span class="NormalTextRun SpellingErrorV2Themed SCXW237352169 BCX0">orthomosaics</span><span class="NormalTextRun SCXW237352169 BCX0"> w</span><span class="NormalTextRun SCXW237352169 BCX0">ere</span><span class="NormalTextRun SCXW237352169 BCX0"> used to develop an open-source workflow for extracting </span><span class="NormalTextRun SCXW237352169 BCX0">quantitative</span><span class="NormalTextRun SCXW237352169 BCX0"> values from individual plots for downstream analysis of plant traits. This field exemplifies a challenge for plot extraction, as plots were planted with no border rows or alleys.</span></span><span class="EOP SCXW237352169 BCX0"> </span></p>
Cross-scale assessment of the impacts of chronic seasonal drought and episodic typhoons on subtropical montane cloud forests
<p>The data that support the findings of this study.</p>
Seasons and Milk Production in the Andean Highlands
<p>This database contains seasonal milk production data in the Andean Highlands (2008-2016).</p>
Data for: Functional, sexual and seasonal variation in the chemical constituents from feces of adult Iberian wolves
<p><span>Chemical signals play an important role in intraspecific and interspecific communication of many mammals, We described the chemicals found in fresh feces of adult wolves by means of analyses using gas chromatography-mass spectrometry (GC-MS) of samples collected from wild breeding groups. We identified 56 compounds in the feces, mainly heterocyclic aromatic organic compounds such as indole or phenol, but also steroids, such as cholesterol, carboxylic acids and their esters between n-C<sub>4</sub> and n-C<sub>18</sub>, aldehydes, alcohols and significant quantities of squalene and α-tocopherol, which would increase the chemical stability of feces on humid substrates. </span><span>All samples visually identified as wolves were subsequently identified to species level by sequencing a small fragment of mtDNA and sexed typing DBX6 and DBY7 sex markers. There is variability in the number and proportions of compounds between sexes, which could be indicative of their function as chemical signals. We also found variability in different reproductive states, especially in odorous compounds, steroids and α-tocopherol. Feces with a presumed marking function had higher proportions of α-tocopherol and steroids than feces with non-marking function. Therefore, these compounds could be involved in intragroup and intergroup communication of wolves and their levels could be directly related with the wolf's physiological status.</span></p>
Fig. 2 in Genotype diversity, phylogenetic analysis and seasonality of isolates of Acanthamoeba spp. in swimming pools in Kafrelsheikh, Egypt
Fig. 2. The maximum-likelihood constructed phylogenetic tree of Acanthamoeba isolates inferred from the small subunit ribosomal RNA gene sequences from GenBank showing the phylogenetic position of three strains detected in the present study; T4 (blue), T9 (green) and T11 (red). The tree with the highest log likelihood (-1201.92) is shown.
Fig. 7. Relative ATPase activity treated with 21 in Seasonal variation of phenolic compounds in Zostera marina (Zosteraceae) from the Baltic Sea
Fig. 7. Relative ATPase activity treated with 21 in micromolar range.
Fig. 3 in Seasonal variation of phenolic compounds in Zostera marina (Zosteraceae) from the Baltic Sea
Fig. 3. Chemical structure and HMBC key correlations of 7′′,8′′ -didehydrosalvianolic acid B.
A Global Perspective of the Functional Trait Responses of Graminoids to the Seasonality of Fire
<p>The dataset is data used for meta-analysis of graminoid functional trait responses to different seasons of fire</p>
Data from: Seasonal and daily xylem radius variations in Scots pine are closely linked to environmental factors affecting transpiration
<p>The data file (<strong>Dendrometer_data_Pisy.xlsx</strong>) contains environmental records and dendrometer data, i.e., raw data as well as calculated mean daily radial variations of the xylem and inner bark, detrended radial variations of the xylem and inner bark, and daily xylem amplitudes.</p> <p><strong>Data are documented in the following article</strong>:</p> <p>Oberhuber W, A Gruber, G Wieser (2023) Seasonal and daily xylem radius variations in Scots pine are closely linked to environmental factors affecting transpiration<strong>. </strong>Biology, 12, 1251; doi: 10.3390/ biology12091251</p> <p><strong>Summary</strong></p> <p>Diurnal and seasonal radius variations of xylem and inner bark of mature Scots pine trees (<em>Pinus sylvestris</em>; stem diameter 27±2 cm) were determined at a drought prone inner alpine site (<em>c</em>. 750 m asl; Tyrol, Austria). Point dendrometers were mounted on xylem and on living phloem (i.e., inner bark) of four trees, and xylem radius variations (XRV) were continuously recorded throughout three growing seasons (April 2019–October 2021) and related to environmental factors including air temperature (T), vapour pressure deficit of the air (VPD), relative air humidity (RH), solar radiation (SR), precipitation (P) and soil water content (SWC). While inner bark width increased due to radial stem growth (<em>c</em>. 500 µm per year), radial xylem width consistently decreased by <em>c</em>. 50 µm during the growing seasons. Results of our study revealed that daily and seasonal radial variations of the xylem and inner bark were closely linked, indicating intensive water exchange between these tissues. Comparison of XRV with environmental variables affecting transpiration revealed a close coupling of XRV to VPD, T and SR, pointing to a strong dependence of stem water status on changes in atmospheric conditions. Because VPD increases exponentially with the increase in temperature and thus causes the water content in the stem to decrease especially under drought, tree growth and mortality will be increasingly affected by climate warming.</p> <p> </p>
Figure 8 in Comparison of feeding apparatus and feeding intensity of the European sardine (Sardina pilchardus) off Morocco in two Atlantic upwelling areas with different seasonal activity
Figure 8. – Linear regression between the length of ceratohypobranchial arch and the total length of Sardina pilchardus in the Atlantic area B and area C off Morocco.
Figure 6 in Comparison of feeding apparatus and feeding intensity of the European sardine (Sardina pilchardus) off Morocco in two Atlantic upwelling areas with different seasonal activity
Figure 6. – Linear regression between length and number of gill rakers of Sardina pilchardus adult from the Atlantic the areas B and C off Morocco.
Figure 2 in Comparison of feeding apparatus and feeding intensity of the European sardine (Sardina pilchardus) off Morocco in two Atlantic upwelling areas with different seasonal activity
Figure 2. –Monthly variation of condition factor of Sardina pilchardus from the areas B and C during 2003.
Lingdancao Granules in the Treatment of Seasonal Influenza
ClinicalTrials.gov study NCT02662426. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Immunogenicity and Reactogenicity of GlaxoSmithKline (GSK) Biologicals' Quadrivalent Split Virion Influenza Vaccine Fluarix Tetra (2015 Season Southern Hemisphere) in Adults 18 Years of Age and Above
ClinicalTrials.gov study NCT02369341. IPD Sharing: YES. Countries: 1. Publications: 0.
Bepotastine Besilate-corticosteroid Nasal Spray Combination Compared to Placebo, Bepotastine Besilate Nasal Spray, and Corticosteroid Nasal Spray in the Treatment of Patients With Seasonal Allergic Rh
ClinicalTrials.gov study NCT01578278. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predicting Effectiveness of Light Treatment for Winter Seasonal Affective Disorder
ClinicalTrials.gov study NCT00742365. IPD Sharing: Not stated. Countries: 0. Publications: 4.
Seasonal Variation of Ambulatory Blood Pressure
ClinicalTrials.gov study NCT01119755. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.