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.
38
datasets available to search
ShareScore release 0.9.0
Dataset results
38 results for “phenology models”
Modeling dataset: Long-term Change in Metabolism Phenology across North-Temperate Lakes, Wisconsin, USA 1979-2019
This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.
Data for: Growing faster, longer or both? Modelling plastic response of Juniperus communis growth phenology to climate change
<p>Aim: Plant growth and phenology plastically respond to changing climatic conditions both in space and time. Species-specific levels of growth plasticity determine biogeographical patterns and the adaptive capacity of species to climate change. However, a direct assessment of spatial and temporal variability in radial-growth dynamics is complicated, as long records of cambial phenology do not exist.</p> <p>Location: 16 sites across European distribution margins of <em>Juniperus communis</em> L. (the Mediterranean, the Arctic, the Alps and the Urals).</p> <p>Time period: 1940-2016</p> <p>Major taxa studied: <em>Juniperus communis</em></p> <p>Methods: We applied the Vaganov-Shashkin process-based model of wood formation to estimate trends in growing season duration and growth kinetics since 1940. We assumed that <em>J. communis</em> would exhibit spatially and temporally variable growth patterns reflecting local climatic conditions.</p> <p>Results: Our simulations indicate regional differences in growth dynamics and plastic responses to climate warming. Mean growing season duration is the longest at Mediterranean sites and, recently, there is a significant trend towards its extension of up to 0.44 days per year. However, this stimulating effect of longer growing season is counteracted by declining summer growth rates caused by amplified drought stress. Consequently, overall trends in simulated ring-widths are marginal in the Mediterranean. By contrast, durations of growing seasons in the Arctic show lower and mostly non-significant trends. However, spring and summer growth rates follow increasing temperatures, leading to a growth increase of up to 0.32 % per year.</p> <p>Main conclusions: This study highlights the plasticity in growth phenology of widely distributed shrubs to climate warming–an earlier onset of cambial activity that offsets the negative effects of summer droughts in the Mediterranean and, conversely, an intensification of growth rates during the short growing seasons in the Arctic. Such plastic growth responsiveness allows woody plants to adapt to the local pace of climate change.</p>
Data and model code for article "Wildflower phenological escape differs by continent and spring temperature"
<p>This dataset contains seven spreadsheets and one R script. The spreadsheet "NC_Lee_etal_data.xls" contains the master data file associated with the article "Wildflower phenological escape differs by continent and spring temperature", along with a metadata sheet that describes the column names. The other six spreadsheets contain the continent (EA = East Asia, ENA = Eastern North America, and EU = Europe) x lifeform (forbs or trees) specific datasets used in the supplementary models containing spatial autocorrelation terms.</p> <p>The script contains annotated example code to run the models used in the final analysis.</p> <p>Questions about the code or analysis should be directed to the lead author (Benjamin R Lee).</p>
Data from: Alternative forms of brook trout nest site selection alter modeled offspring thermal experience and emergence phenology in groundwater-influenced streambeds
Open the record for dataset details and reuse information.
Data for: Growing faster, longer or both? Modelling plastic response of Juniperus communis growth phenology to climate change
Open the record for dataset details and reuse information.
Modeling phenological and physiological responses to climate warming in a hypothetical migratory songbird-mosquito system
Open the record for dataset details and reuse information.
Modelled past autumn leaf phenology of deciduous trees
<p> </p> <p><span>Autumn leaf phenology (i.e. leaf colouring or leaf senescence) marks the end of the growing season, during which trees assimilate atmospheric CO<sub>2</sub>. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn phenology is often modelled to assess possible climate change effects on future CO<sub>2</sub> mitigating capacities and species compositions of forests.</span></p> <p><span>Here, we give access to the entire dataset of modelled autumn phenology analyzed in Meier and Bigler (2023). The data was derived from >2.3 million model calibration runs according to 21 such models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for common beech (Fagus sylvatica L.), pedunculate oak (Quercus robur L.), and European larch (Larix decidua Mill.) from 500 Central European sites each.</span></p> <p><span>Cite as </span><span>Meier, M., & Bigler, C. (2023). Process-oriented models of autumn leaf phenology: Ways to sound calibration and implications of uncertain projections. <em>Geoscientific Model Development</em>, <em>16</em>(23), 7171–7201. https://doi.org/10.5194/gmd-16-7171-2023</span></p>
Incorporating plant phenological responses into species distribution models (SDMs) reduces estimates of future species loss and turnover
<p>Anthropogenetic climate change has caused distribution shifts of many species, and species distribution models (SDMs) are central for documenting this relationship. However, most SDMs rarely consider the evolution of climate-sensitive functional traits, such as phenology, which strongly affect species fitness. Using >120,000 herbarium specimens representing 360 plant species across the eastern United States, we developed a novel "phenology-informed" SDM that integrates dynamic phenological responses to changing climates. Compared to standard SDMs, our phenology-informed SDMs forecast lower species habitat loss and less species turnover under climate change. These results suggest that phenotypic plasticity or local adaptation in phenology may help species adjust their ecological niches and persist in their habitats under rapid environmental change. Our findings reveal how phenology variation mediates species distributions and affects regional biodiversity patterns. Our newly developed model also circumvents the need for mechanistic models, facilitating the deployment of trait-based SDMs across unprecedented spatial and taxonomic scales.</p>
Data and code: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize
<p>Data and code supporting the research article: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize - M. Viswanathan, A. Scheidegger, T. Streck, S. Gayler, T.K.D. Weber. This includes R code for the implementation of the SPASS phenology maize model; Jags in R to implement the Bayesian multi-level models .</p>
The niche through time: Considering phenology and demographic stages in plant distribution models
<p>Species distribution models (SDMs) are widely used to infer species-environment relationships, predict spatial distributions, and characterise species' environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors, and the calibration of stage-specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on the coincidence of range and phenological shifts under climate change. The proliferation of digitally available biodiversity and citizen science data increasingly allows considering time explicitly in SDMs. This offers a more mechanistic understanding of plant distributions, and more robust predictions under global change, especially if the reporting of phenological stages and age is facilitated and promoted by relevant data portals.</p>
Temperature data corresponding to "Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy"
<p>MERRA2 Temperature data corresponding to "Hybrid Phenology Modeling for Predicting Temperature Effects on Tree Dormancy"</p>
Incorporating plant phenological responses into species distribution models (SDMs) reduces estimates of future species loss and turnover
Open the record for dataset details and reuse information.
Modelled past autumn leaf phenology of deciduous trees
Open the record for dataset details and reuse information.
The niche through time: Considering phenology and demographic stages in plant distribution models
Open the record for dataset details and reuse information.
Figure 4 in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 4. Best models of each general structure. Model 6 (M6) for linear and 13 (M13) for sinusoidal structure. I = relative calling intensity, t = time. The solid lines represent the predictions of the models; the points represent the data acquired from the ARS.
Figure 3 in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 3. Rose-diagram of anuran assemblage relative calling intensity. The length and colour (light = low, dark = high) of the bars from the centre indicate the intensity of vocalisation for the temporal section. A Rayleigh z test for unimodal orientation was performed, resulting in significant evidence of non-uniform distribution of vocalisations across the year (P = 0.0303).
Figure 1 in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 1. Study site location and land use, natural protected area of Nahá, Ocosingo, Chiapas, México.
Figure 2. a in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 2. a) Calling anuran species recorded at temporary sections (7 months). The horizontal axis corresponds to temporary sections and the vertical to species. b) Average temperature T � (° C) (continuous line) and accumulated rainfall Ra (dashed line) by temporary section (32 in 7 months). Both graphs share the horizontal axis, where breaks indicate recording gaps, and the numbers are the days of the months covered.
Wood Brook catchment: A coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics
<p>This folder contains data, model input files, model executable and source code required to reproduce results in the paper:"Evaluating a coupled phenology – surface energy balance model to understand stream – subsurface temperature dynamics in a mixed-use farmland catchment" by Han Qiu, Phillip Blaen, Sophie Comer-Warner, David M. Hannah, Stefan Krause and Mantha S. Phanikumar (Water Resources Research).</p> <p>The catchment (referred to herein as Wood Brook, Mill Brook, Mill Haft, BIFOR) is a mixed-use farmland catchment in central England. The "Data" folder contains information needed to create model input files. The "Figures" folder contains MS excel files with observed data (streamflows, groundwater heads, stream, streambed and groundwater temperatures etc) and simulation results to recreate the figures in the WRR paper. The other three folders contain model inputs, outputs and source code.</p>
Data from: Estimating phenology and phenological shifts with hierarchical modeling
<p class="MsoNormal">This dataset contains daily counts of juvenile chum salmon (<em>Oncorhynchus keta</em>) from the Skagit River, WA between 1990–2019. The analyzed dataset contains 30 years and 4,636 monitoring days in which 2,358,284 migrating chum salmon were counted. This dataset is the companion dataset for phenomix R package.</p>
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.