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1,662 results for “Phenology”
Plant Phenology Forecasts
<p>These are forecasts of plant phenology for 66 species of plants in North America. The forecasts models are made using data from the National Phenology Network, and climate drivers from the NOAA CFSv2 forecast model and the PRISM Climate Group. Maps from the data are also available at the site http://phenology.naturecast.org.</p> <p> </p> <p> </p>
Test Phenology Annotations From Herbarium Specimen Dataset - Prunus serotina
<p>This is a test dataset of phenology annotations for the Black Cherry, P. serotina, used in the submitted manuscript:</p> <p>Brenskelle, L., B. Stucky, J. Deck, R. Walls, R. P. Guralnick [submitted]. Integrating herbarium specimen observations into global phenology data systems. Applications in Plant Sciences.</p>
Phenology metric layers and their classification layers for the NDVI approximated phenological cycle of Donana from 01/12/2015 to 31/11/2016.
<p>Analysis of changes in the phenological cycle of different plant species provide important information that may be used to assess the impact of seasonal and inter-annual climate variations on terrestrial vegetation. Phenex software has been used for estimating phenology related layers for Donana marshes relying on NDVI time series covering one year period from 01/12/2015 to 31/11/2016.</p> <p>“Phenology_metrics_layer_Dec2015_Nov2016.tif” includes the following layers: (i) green up day, (ii) senescence day, (iii) day of max NDVI value, and (iv) total number of NDVI peaks. These layers are also provided separately with the names: “Greenup_day_Dec2015_Nov2016.tif”, “Senescence_day_Dec2015_Nov2016.tif”, “Max_day_Dec2015_Nov2016.tif”, “Number_of_peaks_Dec2015_Nov2016.tif”.</p> <p>Classification layers based on these layers have been also generated. In particular, "ISODATA_classification_all_input_layers_Dec2015_Nov2016.tif" layer contains the classes generated when providing all phenology related layers as input to the ISODATA algorithm, while "ISODATA_classification_three_input_layers_Dec2015_Nov2016.tif" layer contains contains the classes generated when providing three penology related layers (i.e. greenup day, day of max NDVI value, senescence day layers) as input to the ISODATA algorithm.</p> <p>The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p> <p> </p>
Maps related to the detection of abrupt changes in NDVI approximated phenological cycles of Donana marshes for 2007-2016
<p>Monitoring of abrupt changes among annual vegetation cycles of consequent years in Protected Areas is valuable for the recognition of patterns, which represent the reaction of the biomes to external factors, such as changes in the meteorological conditions (e.g. the precipitation regime), human intervention or extreme events (e.g. fire). It is an indicator of the primary production of the area and other relevant functions of the ecosystem. The BFAST, Breaks For Additive Seasonal and Trend, approach can be used for monitoring changes, since it is globally applicable and able to analyze each pixel individually without the need to set thresholds for detecting changes within time series. Thus, BFAST is applied for the detection of abrupt trend changes in NDVI time series in the case of Doñana marshes, as a proxy to phenological metrics per pixel.</p> <p>BFAST outputs are used to generate: (i) a raster with the time of all detected abrupt changes per pixel (filename: “All_break_times_2007_to_2016.tif”), (ii) a raster with the total number of detected abrupt changes per pixel (filename: “Marshes_maximum_number_of_breaks_2007_to_2016.tif”), (iv) a raster with the time for which the biggest change is detected per pixel has the (filename: “Marshes_maximum_break_time_2007_to_2016.tif”).</p> <p>The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.docx” included in the zip containing the dataset.</p>
Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code
<p>Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)</p>
Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction
<p>This is the data set supporting the analyses performed in the article entitled "Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction", by Natasha de Manincor, Alessandro Fisogni, and Nicole E. Rafferty, published in Ecology Letters (2023, 26:323-334, <a href="https://doi.org/10.1111/ele.14158">https://doi.org/10.1111/ele.14158</a>).</p> <p>The experiment has been performed in the greenhouse facilities at the University of California, Riverside, in 2021.</p> <p>The two treatments analyzed are ambient vs warmed (+ 4 °C), the focal pollinator species is <em>Osmia lignaria</em>, and the three focal plant species are <em>Collinsia heterophylla</em>, <em>Nemophila menziesii</em>, and <em>Phacelia campanularia</em>.</p> <p>Data are tab separated .txt files.</p>
PixelCropRobot dataset: images of vegetables crops in different phenological stages taken in greenhouses
<p><em>Dataset created under the PixelCropRobot project, developed by FCUP, INESC TEC and FEUP.</em></p> <p><strong>Dataset folder:</strong></p> <blockquote> <p>This folder contains the images of each species in two formats (3456 × 4608 pixels and 864 × 1152 pixels), the annotations of the 864 × 1152 px. images, in Pascal VOC (.xml) and YOLO (.txt) formats and also a set of Python scripts useful for managing the dataset.</p> </blockquote> <p>The aim was to capture images of eight crops selected taking into account the length of the crop cycle (annual), the intensity of agricultural practices (mainly weed removal) and the low impact of pests and diseases.</p> <p>The images were captured using a smartphone (Huawei Mate 10 Lite), with 16 megapixels (MP) resolution (3456 × 4608 px.), in Professional mode (no flash, continuous autofocus, automatic ISO and shutter speed). Image collection took place at different hours of the day, with variable lighting conditions.</p> <p>The images are divided as follows (in parenthesis are the classes):</p> <ul> <li>Arugula - 312 (coty, minus9, plus9)</li> <li>Carrot - 533 (coty, smallleaves, carrot)</li> <li>Coriander - 321 (coty, smallleaves, coriander)</li> <li>Lettuce - 1426 (coty, minus9, plus9, ready)</li> <li>Radish - 494 (coty, smallleaves, bigleaves, root)</li> <li>Spinach - 270 (spinach, big)</li> <li>Swiss chard - 454 (coty, chard)</li> <li>Turnip - 313 (coty, smallleaves, turnip)</li> </ul> <p>To standardise the dataset, each image was renamed according to the corresponding EPPO (European and Mediterranean Plant Protection Organization) code and the date of creation of that image. The size of each image was also reduced four times (to 864 × 1152 pixels) to facilitate processing. For example, an image of lettuce captured on June 22 presents the name as follows: LACSA_Jun_22_x_864_1152.jpg.</p>
Dataset of cotton phenology images
<p>The dataset contains labels about the the phenological stages of cotton at the parcel level, generated by in-situ inspections in the region of Orchomenos, Greece, during the cotton cultivation period of 2021 (May-October). We collected 1,285 crop growth ground<br> observations. We introduce a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. More information about the annotation procedure can be found in the relevant publication "<a href="https://arxiv.org/abs/2211.14099">Fuzzy clustering for the within-season estimation of cotton phenology</a>". </p> <p>Along with the annotations we are publishing a unique dataset of 3,142 images. Each image is named in the following format:</p> <p> {date}_{id}-{type}.jpg, where "<strong>date"</strong> is in the format {year}{month}{day} and refer to the exact date the image was captured, "<strong>id</strong><em><strong>"</strong></em> is the unique id of a parcel and "<strong>type</strong>" is one of "O", "A" or "B". "O" refers to a panoramic photo of the entire field, "A" to a photo that is representative of the majority of the plants in the field and "B" to one representative of a minority of plants in the field. For each date and each unique parcel the first two exists always while the latter close-up photo had be captured only when the percentage of the minority class, in terms of area, was deemed significant. </p> <p>If you use this dataset, please cite our <a href="https://arxiv.org/abs/2211.14099">paper</a>:</p> <blockquote> <pre>@article{sitokonstantinou2022fuzzy, title={Fuzzy clustering for the within-season estimation of cotton phenology}, author={Sitokonstantinou, Vasileios and Koukos, Alkiviadis and Tsoumas, Ilias and Bartsotas, Nikolaos S and Kontoes, Charalampos and Karathanassi, Vassilia}, journal={PLOS ONE}, year={2023}, doi={10.1371/journal.pone.0282364} }</pre> </blockquote>
Data from: Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk
<p>This dataset comprises crowdsourced data using digitized herbarium specimen images from two comprehensively digitized regional floras; the Consortium of Northeastern Herbaria (CNH; <a href="http://portal.neherbaria.org/portal/">http://portal.neherbaria.org/portal/</a>) and Southeast Regional Network of Expertise and Collections (SERNEC; <a href="http://sernecportal.org/portal/index.php">http://sernecportal.org/portal/index.php</a>) for 200 plant species in the eastern United States, and four reproductive phenophases (i.e., flowering, peak flowering, fruiting, and peak fruiting) extracted from the herbarium specimens with associated climate data from PRISM and human population density from US Census Bureau.</p>
CARDAMOM Phenology Study: Dataset and Analysis Code
<p>This repository stores the input data, output data and analysis code for the following publication:</p> <p>Norton, A. J., Bloom, A. A., Parazoo, N. C., Levine, P. A., Ma, S., Braghiere, R. K., and Smallman, L. T.: Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1265, 2022.</p>
Copernicus High Resolution Vegetation Phenology and Productivity for Doñana Natural Space
<p>GeoTIFF rasters with the following phenometrics obtained from Sentinel 2 Data:.</p> <p> </p> <p>* Start of the season Day of the Year (SOSD)</p> <p>* Maximun of the Season Day of the Year (MAXD)</p> <p>* End of the Season Day of the Year (EOSD)</p> <p>* Start of the season Value (SOSV)</p> <p>* Maximun of the Season Value (MAXV)</p> <p>* End of the Season Value (EOSV)</p> <p> </p> <p>These rasters have been downloaded, mosaicked and croped with Doñana Natural Space DEIMS.ID through Pyvpp python package. </p>
Ice Phenology Lake Auburn, Maine, USA, 1836-2022
This dataset contains ice phenology for Lake Auburn, Maine, USA from the ice season of 1835-1836 through 2021-2022. This dataset is maintained by the Auburn Water District/Lewiston Water Division. Ice-on data begin in the early 1950s.
Mohonk Preserve Phenology Monitoring 1912-present
Phenology records around Mohonk Lake date back to the early 1900s, when Daniel and Keith Smiley started noting the date of spring bird arrivals. The Preserve’s Daniel Smiley Research Center continues this tradition of natural history observation and has expanded the scope of the Smileys’ phenology records to monitor seasonal changes in plants and other fauna at Mohonk Preserve on local, regional, and national levels. Alongside our long-term weather data, these data provide insight into the changing communities in the Mohonk Preserve in response to changing climate. These data include first occurrence dates for each of the taxa and associated life stages in the vicinity of Mohonk Preserve and were collected by staff at the Daniel Smiley Research Center.
Rainforest phenology: flower, fruit and seed production from biweekly collections of 200 traps in the Yasuní Forest Dynamics Plot, Ecuador, 2000-2018
We provide data on flowering and fruiting phenology from an equatorial, ever-wet rainforest in eastern Ecuador, in Yasuni National Park. This is the first long-term study (18 years) of phenology in a diverse equatorial neotropical forest. Although the site is ever-wet, there is some seasonal variation in rainfall and irradiance. One major question was to determine whether the seasonal variation in climate was sufficient to drive seasonality in reproduction in this hyper-diverse forest. The study began in 2000 with various funding, and became an LTREB-funded project in 2006. We used twice monthly censuses of 200 traps to document phenology. Parts of >1000 species were identified in the traps in the 18 year period (ending early in 2018), including trees, shrubs, lianas and epiphytes. Parts identified included buds, flowers, mature fruits and mature seeds, and aborted, damaged and immature fruits and seeds. The project is on-going, and additional data will be added as it is processed.
Data from Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus
These data include annotations for life stage, mating behavior, and plant part occupancy of large milkweed bug observations in North America as well as information about climate and environment.
Dataset from a study of aquatic macrophyte phenology across three sites in the Coeur d'Alene Lake Basin.
This repository includes data on aquatic macrophytes, water temperature, water clarity, and other water quality measures, such as pH, dissolved oxygen, and specific conductance relative to understanding the growth cycle (i.e., phenology) of aquatic plants. The data was generated during a project to study the phenology of macrophytes across the growing season of 2017, in three temperate lakes of the Coeur d’Alene Basin, USA. The project’s aim was to demarcate the timing of initial macrophyte growth, peak biomass, and senescence. The project also sought to compare traditional biomass measures with sonar biovolume (percent water column occupied by vegetation), which is relatively easier to collect. Macrophytes were measured twice per month with biomass collection via rake twirl, sonar biovolume, and species identification. Hourly water temperatures were measured over the course of the growing season at each site in shallow water near the macrophyte collection sites. The hourly water temperature data was used to calculate cumulative growing degree days over the growing season using minimum and maximum growth thresholds for Myriophyllum spicatum. Water temperature was also measured with a multi-parameter sonde at 0.5 meter increments from the water surface to the lake sediment and collected twice per month. Photosynthetically active radiation was measured during these vertical profiles and light extinction was calculated from this data. Water clarity was also measured with a standard black and white 20 centimeter Secchi disk. The data in this repository was originally reported in the following publication: Torso, K., Scofield, B.D. and Chess, D.W., 2020. Variations in aquatic macrophyte phenology across three temperate lakes in the Coeur d’Alene Basin. Aquatic Botany, 162, p.103209. https://doi.org/10.1016/j.aquabot.2020.103209
Walleye spawning, ice phenology, and covariate data for Upper Midwestern Lakes: 1939-2019
The phenology of critical biological events in aquatic ecosystems are rapidly shifting due to climate change. Growing variability in phenological cues can increase the likelihood of trophic mismatches, causing recruitment failures in commercially, culturally, and recreationally important fisheries. We tested for changes in spawning phenology of regionally important walleye (Sander vitreus) populations in 194 Midwest US lakes in Minnesota, Michigan, and Wisconsin spanning 1939-2019 to investigate factors influencing walleye phenological responses to climate change and associated climate variability, including ice-off timing, lake physical characteristics, and population stocking history. Data from Wisconsin and Michigan lakes (185 and 5 out of 194 total lakes, respectively) were collected by the Wisconsin Department of Natural Resources (WDNR) and the Great Lakes Indian Fish and Wildlife Commission (GLIFWC) through standardized spring walleye mark-recapture surveys and spring tribal harvest season records. Standardized spring mark-recapture population estimates are performed shortly after ice-off, where following a marking event, a subsequent recapture sampling event is conducted using nighttime electrofishing (typically AC – WDNR, pulsed-DC – GLIFWC) of the entire shoreline including islands for small lakes and index stations for large lakes (Hansen et al. 2015) that is timed to coincide with peak walleye spawning activity (G. Hatzenbeler, WDNR, personal communication; M. Luehring, GLIFWC, personal communication; Beard et al. 1997). Data for four additional Minnesota lakes were collected by the Minnesota Department of Natural Resources (MNDNR) beginning in 1939 during annual collections of walleye eggs and broodstock (Schneider et al. 2010), where date of peak egg take was used to index peak spawning activity. For lakes where spawning location did not match the lake for which the ice-off data was collected, the spawning location either flowed into (Pike River) or wa
Cue the chorus: Canyon treefrog calling phenology on the falling limb of spring floods and warming nights
Phenology is the timing of life events tied to environmental or abiotic cues. We used autonomous recording units (ARUs) across spring-summer months in 2022 to capture breeding calls from canyon treefrog (Hyla arenicolor). ARUs were placed in perennial and intermittent stream reaches across five Wilderness Areas within the upper Verde River basin in Arizona. We monitored streams by installing stream flow gauges (water level recorders). Treefrogs call at relatively low flow after spring floods. This suggests that stream-dwelling anurans may breed in response to flooding followed by prolonged periods of base flows which could be important for tadpole metamorphosis. Implications for stream regulation suggest maintaining the magnitude and timing of flood pulse events can benefit recruitment of stream-breeding amphibians.
Early snowmelt and warming experiments to study plant phenology
Phenology - the timing of life events - determines how a species’ life cycle aligns with the abiotic and biotic environment, however, climate change has altered the environmental cues organisms use to track climate leading to shifts in phenology. In high latitude environments, phenological shifts in plants are associated with both temperature and the timing of snowmelt, but the mechanism underlying the effect of snowmelt on phenology remains unclear. Here we aim to disentangle the effects of experimental warming and earlier snowmelt on the phenology of three long lived perennial wildflowers. In the summer of 2019, we factorially crossed passive warming with early snowmelt timing within a subalpine plant community in the Colorado Rocky Mountains at the Rocky Mountain Biological Laboratory to understand the individual effects of these aspects of climate change.
Toolik Lake 2011 common garden leaf length phenology 2015-2016 Alaska
Data on Eriophorum vaginatum leaf length collected from a common garden established at Toolik Lake in 2011 with tussocks from No Name Creek, Coldfoot, Eagle Creek, Toolik Lake, Sagwon, and Prudhoe Bay. Data collected during the growing seasons of 2015 and 2016. Results published in Parker, T. C., J. Tang, M. B. Clark, M. M. Moody, and N. Fetcher. 2017. Ecotypic differences in the phenology of the tundra species Eriophorum vaginatum reflect sites of origin. Ecology and Evolution 7: 9775-9786. doi: 10.1002/ece3.3445
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.