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334 results for “temporal variation”
Data from: Empirical evidence for the extent of spatial and temporal thermal variation on sea turtle nesting beaches
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Spatial and temporal variation in phenotypes and fitness in response to developmental thermal environments
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Data from: environmental DNA reveals temporal variation in mesophotic reefs of the Humboldt upwelling ecosystems of central Chile: towards a baseline for biodiversity monitoring of unexplored marine habitats
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Individual and temporal variation in movement patterns of wild alpine reindeer and implications for disease management
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Temporal variation in maternal nest choice and its consequences for lizard embryos
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Geographical and temporal variation of multiple paternity in invasive mosquitofish (Gambusia holbrooki, Gambusia affinis)
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Spatial-temporal gradient variation patterns of fish trophic guilds in a freshwater river wetland ecosystem of northeastern China
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The degree of spatial variation relative to temporal variation influences evolution of dispersal
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Temporal variation in early-life conditions impacts on later-life levels of infection in sex specific ways. Associated data and code
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Predictability of temporal variation in climate and the evolution of seasonal polyphenism in tropical butterflies
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Data from: Plant attributes interact with fungal pathogens and nitrogen addition to drive soil enzymatic activities and their temporal variation
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Temporal variations of the multifaceted biodiversity and assembly mechanisms in lake fish assemblages
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Spatial and temporal variation in toxicity and inorganic composition of hydraulic fracturing flowback and produced water
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Spatial and temporal heterogeneity in pollinator communities maintains within-species floral odour variation
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Temporal variation in floral scent emission of a woody plant and flower visiting behaviour of male and female flies
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Abiotic conditions shape spatial and temporal morphological variation in North American birds
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Temporal dynamics of migration-linked genetic variation are driven by streamflows and riverscape permeability
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Spatial and temporal variation in the value of solar power across United States electricity markets
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States Electricity Markets". Renewable & Sustainable Energy Reviews 2019. <a href="https://doi.org/10.1016/j.rser.2019.109594">https://doi.org/10.1016/j.rser.2019.109594</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Many of the scripts, data, and descriptive text in this repository are shared with the following publication:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles". Applied Energy 2019, 256, 113734. <a href="https://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm/*.py : Python module with functions for modeling PV generation and calculating PV energy revenue, capacity value, and emissions offset.</li> <li>notebooks/*.ipynb : Jupyter notebooks, including: <ul> <li>pvvm-vos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, assign nodes to capacity zones, download NSRDB input data, and reproduce some figures in [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the use of the PV generation model and a sensitivity analysis of PV generator assumptions</li> <li>pvvm-example-plots.ipynb: Example scripts demonstrating different plotting functions</li> <li>validate-pv-monthly-eia.ipynb: Scripts and plots for comparing modeled PV generation with monthly generation reported in EIA forms 860 and 923, as discussed in SI Note 3 of [1]</li> <li>validate-pv-hourly-pvdaq.ipynb: Scripts and plots for comparing modeled PV generation with hourly generation reported in NREL PVDAQ database, as discussed in SI Note 3 of [1]</li> <li>pvvm-energyvalue.ipynb: Scripts for calculating the wholesale energy market revenues of PV and reproducing some figures in [1]</li> <li>pvvm-capacityvalue.ipynb: Scripts for calculating the capacity credit and capacity revenues of PV and reproducing some figures in [1]</li> <li>pvvm-emissionsvalue.ipynb: Scripts for calculating the emissions offset of PV and reproducing some figures in [1]</li> <li>pvvm-breakeven.ipynb: Scripts for calculating the breakeven upfront cost and carbon price for PV and reproducing some figures in [1]</li> </ul> </li> <li>html/*.html : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data/lmp/*.gz : Day-ahead nodal locational marginal prices (LMPs) and marginal costs of energy (MCE), congestion (MCC), and losses (MCL) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will be linked here with the complete dataset.</li> </ul> </li> <li>results/*.csv.gz : Simulation results associated with [1], including modeled energy revenue, capacity credit and revenue, emissions offsets, and breakeven costs for PV systems at all LMP nodes</li> </ul> <p><strong>Data notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="https://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes on LMP data: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-vos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Usage notes</strong></p> <ul> <li>Code is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file and at the top of each *.py file. </li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_vos">https://github.com/patrickbrown4/pvvm_vos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, you will need to modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing (latitude, longitude) floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, you will need to request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul> <p> </p>
Data for: Wind drives temporal variation in pollinator visitation in a fragmented tropical forest
<p>Data for "Wind drives temporal variation in pollinator visitation in a fragmented tropical forest." Files include:</p> <p>1. Data on orchid bee abundance and species identification.</p> <p>2. Data for collection sessions, including forest cover at each site, as well as wind data for the specific time period of the collection, and overall abundance counts, etc.</p> <p>3. R scripts to recreate statistical presented in the paper, using the two datasets described above.</p>
Data from: Temporal variation in spatial genetic structure during population outbreaks: distinguishing among different potential drivers of spatial synchrony
Spatial synchrony is a common characteristic of spatio-temporal population dynamics across many taxa. While it is known that both dispersal and spatially autocorrelated environmental variation (i.e., the Moran effect) can synchronize populations, the relative contributions of each, and how they interact, is generally unknown. Distinguishing these mechanisms and their effects on synchrony can help us to better understand spatial population dynamics, design conservation and management strategies, and predict climate change impacts. Population genetic data can be used to tease apart these two processes as the spatio-temporal genetic patterns they create are expected to be different. A challenge, however, is that genetic data are often collected at a single point in time, which may introduce context-specific bias. Spatio-temporal sampling strategies can be used to reduce bias and to improve our characterization of the drivers of spatial synchrony. Using spatio-temporal analyses of genotypic data, our objective was to identify the relative support for these two mechanisms to the spatial synchrony in population dynamics of the irruptive forest insect pest, the spruce budworm (Choristoneura fumiferana), in Quebec (Canada). AMOVA, cluster analysis, isolation by distance and sPCA were used to characterize spatio-temporal genomic variation using 1370 SBW larvae sampled over four years (2012-2015) and genotyped at 3,562 SNP loci. We found evidence of overall weak spatial genetic structure that decreased from 2012 to 2015 and a genetic diversity homogenization among the sites. We also found genetic evidence of a long-distance dispersal event over > 140 km. These results indicate that dispersal is the key mechanism involved in driving population synchrony of the outbreak. Early intervention management strategies that aim to control source populations have the potential to be effective through limiting dispersal. However, the timing of such interventions relative to outbreak progression is likely to influence their probability of success.
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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)
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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.