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242 results for “Temperature change”
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios
<p>This repository contains the data and results from the paper <strong>Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities</strong> published in <em>Nature Medicine</em> (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>).</p> <p>It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals. </p> <p>The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>.</p> <h3>Content</h3> <p>This repository contains three zip files, each with an internal codebook:</p> <ul> <li><em>data.zip</em>: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li><em>results_csv.zip</em>: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li><em>results_parquet.zip</em>: contains the same information as the <em>results_csv.zip</em> but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> <p>It is recommended to only download <em>results_csv.zip</em> for a quick exploration of the results, or only <em>results_parquet.zip</em> when the results are to be loaded into a software for deeper analysis.</p> <p> </p>
Raw data for High Temperature Photochromism of Fe-Doped SrTiO3 Caused by UV Induced Bulk Stoichiometry Changes
<p>In the following the raw data lying the foundation of the paper High Temperature Photochromism of Fe-Doped SrTiO3 Caused by UV Induced Bulk Stoichiometry Changes (Viernstein et al.) published in Advanced Functional Materials Vo. 29 Issue 23 (WILEY-VCH Verlag GmbH & Co. KGaA, Germany) in 2019 are described. They were obtained under the funding provided by Austrian Science Fund (FWF) (project F4509-N16, FOXSI) and the European Union`s Horizon 2020 research and innovation program under the grant agreement No. 824072 and consist of UV/VIS spectra, van der Pauw measurements, electrochemical impedance spectra, and laser ablation ICP-MS data.</p> <p>The UV/VIS measurements were carried out in air, at 440 °C using a deuterium and a tungsten lamp (Edmund Optics Inc., Germany) as light source and an Ocean Optics QE6500 (Halma plc, England) as spectrometer. The data include background, I<sub>0</sub> and I absorption measurements of Fe doped SrTiO<sub>3</sub> (STO) single crystals before, during, and after illumination with UV light (365 nm). The data files are labeled for example as “UVvis_FeSTO_background_1” or ”UVvis_FeSTO_I_440C_UVon_90s”, to state the type auf measurement, temperature, and status of the experiment. In each of them the average of 30 spectra is given and each exhibits two columns, namely wavelength, and intensity.</p> <p>The van der Pauw measurements were performed on two Keithley 20 multimeter and a 2410 1100 V source meter (Keithley Instruments, USA). They are labeled in the following way: “date_applied voltage_atmosphere_sample identification_temperature cycle_status of the measurement”. Each file consists out of a header giving time, cycle number, temperature (real and set) and six columns, t[s], (applied) U[V], (measured) I[A], R [Ohm], and two unnamed columns ((applied) U[V] and (measured) U [V]).</p> <p>The electrochemical impedance spectra were obtained before, during, and after UV exposure, using an Electrochemical Test Station POT/GAL 30 V/2 A or a Novocontrol Alpha‐A high‐performance frequency analyzer, respectively (both Novocontrol Technologies GmbH & Co. KG, Germany). Each spectrum is named after the following description: “date_sample name_UVonoff_real temperature_atmosphere_spectra number”. A header with date, time, cycle number, and temperature followed by four columns, namely Freq [Hz], Re (real part of the impedance spectra), Im (imaginary part), Amp (amplitude), and Pha (phase) are given.</p> <p>Laser ablation ICP-MS measurements were performed on a NWR213 laser ablation system (ESI; USA) and an iCAP Q ICP-MS (Thermo Fisher Scientific, Germany). The obtained data file is labeled as Iaser_ablation_ICP_MS_FeSTO and exhibits sample names, names of the measured masses (isotopes) and the obtained counts.</p>
Effect of changing ocean circulation on deep ocean temperature in the last millennium: simulation output data
<ul> <li>This dataset contains the output of model simulations used in the paper:<br> Scheen, Jeemijn and Stocker, Thomas F., "Effect of changing ocean circulation on deep ocean temperature in the last millennium", Earth System Dynamics Discussions, https://doi.org/10.5194/esd-11-925-2020, 2020 </li> <li>All figures can be reproduced when combining this dataset with the published analysis code. <br> </li> <li>In addition this dataset contains the data behind Fig. 2 of the paper:<br> Gebbie, G. and Huybers, P. : "The Little Ice Age and 20th-century deep Pacific cooling", Science, 363, 70-74, https://doi.org/10.1126/science.aar8413, 2019<br> </li> <li>Download either the small (unzipped 5 Gb) or large (unzipped 22 Gb) version of the dataset. <strong>Warning: this needs to be loaded into memory when running the notebook.</strong> You only need the small version to run the github notebook and reproduce the figures, but you are free to explore additional variables in the large version.</li> </ul> <p>Overview of doi's:</p> <ul> <li>paper: <a href="https://doi.org/10.5194/esd-11-925-2020">https://doi.org/10.5194/esd-11-925-2020</a></li> <li>code (analysis and figures): <a href="https://doi.org/10.5281/zenodo.4022947">https://doi.org/10.5281/zenodo.4022947</a></li> <li>data (simulation output): <a href="https://doi.org/10.5281/zenodo.4022927">https://doi.org/10.5281/zenodo.4022927</a></li> </ul>
Future monthly discharge and water temperature simulations under global change (CMIP6)
<p>Monthly discharge (m3 s-1) and water temperature (K) simulated by a global hydrological model coupled to a surface water quality model (<i>PCR-GLOBWB2-DynQual)</i> for the time period 2005 - 2100, for an ensemble of 15 projections based on three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and five general circulation models (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0)</p><p>Output data are provided at 10km resolution and are averaged at monthly temporal resolution.</p><p>These datasets were generated as part of the work presented in: Jones, E.R., Bierkens, M.F.P., van Puijenbroek, P.J.T.M. <i>et al.</i> Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. <i>Nat Water</i> <strong>1</strong>, 602–613 (2023). <a href="https://www.nature.com/articles/s44221-023-00105-5#citeas">https://doi.org/10.1038/s44221-023-00105-5</a></p><p>Relevant model description papers can be found at the following links:</p><ul><li><i>PCR-GLOBWB2</i>: Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, <i>Geoscientific Model Development</i>, 11, 2429–2453, <a href="https://gmd.copernicus.org/articles/11/2429/2018/gmd-11-2429-2018.html">https://doi.org/10.5194/gmd-11-2429-2018</a>, 2018.</li><li><i>DynQual</i>: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.: DynQual v1.0: a high-resolution global surface water quality model, <i>Geoscientific Model Development</i>, 16, 4481–4500, <a href="https://gmd.copernicus.org/articles/16/4481/2023/gmd-16-4481-2023.html">https://doi.org/10.5194/gmd-16-4481-2023</a>, 2023.</li></ul><p>Additional information on the water temperature modelling can also be found at:</p><ul><li>Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (Rens): High-resolution global water temperature modeling. <i>Water Resources Research</i>, 55, 2760–2778, <a href="https://doi.org/10.1029/2018WR023250">https://doi.org/10.1029/2018WR023250</a>, 2019</li><li>van Beek, L. P. H., Eikelboom, T., van Vliet, M. T. H., and Bierkens, M. F. P.: A physically based model of global freshwater surface temperature, <i>Water Resources. Research</i>, 48, W09530, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2012WR011819">https://doi.org/10.1029/2012WR011819</a> , 2012.</li></ul>
Code and data to "Climate change contribution to the 2023 autumn temperature records in Vienna"
<p>The dataset consists of the code and data used for the preprint "Climate change contribution to the 2023 autumn temperature records in Vienna". </p> <p>It contains two objects:</p> <ul> <li>The station data of mean monthly temperature for Vienna Hohe-Warte from 1750 to 2023 (vienna_hohe-warte.csv), which also can be downloaded here: http://www.zamg.ac.at/histalp/dataset/station/csv.php. </li> <li>The code for modeling and producing the figures of the preprint (autumn_temperature.R).</li> </ul>
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes
<p>Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".</p>
Global Surface Temperature Changes over Land Dataset
<p>Annual averages of global surface temperature changes for land only based on Berkeley Earth monthly dataset above the 1951-1980 baseline. The dataset is from 1750 in °C, 3 decimal places.</p>
Global Surface Temperature Changes Datasets Converted to 1850-1900 Baseline
<p>Global warming datasets converted to the uniform baseline. NASA, NOAA and Berkeley Earth datasets of global surface temperature changes in the period 1850-2021 for land+ocean, 1750-2021 for land only and 1880-2021 for ocean only, converted to the 1850-1900 baseline.</p>
Changes in soil moisture and temperature modify the toxicity of sodium selenite and sodium selenate for Folsomia candida (Collembola) Willem 1902
<p>Effects of sublethal concentrations of selenite and selenate were tested on parameters of mortality, reproduction, growth, and oxidative stress parameters of <em>Folsomia candida</em> (Collembola) in case of different climate scenarios. The standard 20°C and the increased 25°C temperatures were combined with three different soil moisture conditions: drought, standard water content and increased water content.</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Raw Data - 3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer
<p>This Data set contains the raw data of the article:</p> <p>3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer, Small, 2021, 17, 2101233.</p> <p>C. Iffelsberger, C. W. Jellett, and M. Pumera*,</p> <p>https://doi.org/10.1002/smll.202101233</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Increased egg shell temperature during incubation leads to changes in transcriptional and epigenetic profiles in chicken lungs
<p>These RDS files contain <strong>DESeqDataSet </strong>objects subsets per broiler age and treatment. These objects are the result of DESeq2::DESeq( … ,betaPrior=FALSE).The .txt-objects contain the normalized sequencing counts per broiler age and treatment group. These objects are the result of DESeq2::counts( … , normalized=TRUE). Data was generated using STAR v2.7.10a and DESeq2 v1.36. Metadata is included as Excel file.</p> <p>Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA949139. </p> <p> </p> <p><strong>Study abstract</strong></p> <p>D. Schokker, J. de Vos, P.B. Stege, O. Madsen, H.J. Wijnen, S.K. Kar, and J.M.J. Rebel</p> <p>Health and resilience against respiratory diseases are important features for broiler chicken. In this study, epigenetic and transcriptomic changes in the lungs of broiler chickens of different ages during rearing that were either exposed to elevated egg shell temperature (HIGH) of 38.9°C during mid-incubation or normal egg shell temperature (control; CON). The objective was to better understand how environmental challenges, such as heat stress during egg incubation, affect the development of the immune system and health of broiler chicken at later age. To this end we generated both epigenetic and transcriptomic data of lung tissue of elevated HIGH and CON chicken, furthermore these chicken were challenged by introducing either an infectious E. coli or an IBV vaccination to monitor the respiratory response. Thousands of differential methylated sites were observed at days 15 and 33, when comparing HIGH vs. CON. Pathway enrichment analysis of HIGH vs. CON showed that differentially expressed genes were mainly involved in cilium, cytoskeleton, and immune processes. These findings provide insight into the underlying biological mechanisms of early life conditions, like elevated EST, and their potential role in health of broilers.</p>
Macrosystems EDDIE Module 1: Climate Change Effects on Lake Temperatures
Climate change is modifying the thermal structure of lakes around the globe. Because it is difficult to predict how lakes will respond to the many different aspects of climate change (e.g., altered temperature, precipitation, wind, etc.), many researchers are using models to manipulate climate scenarios and simulate lake responses. Lake simulation models provide a powerful tool for exploring the sensitivity of lake thermal structure characteristics to weather. In this module, students will learn how to set up a lake model (General Lake Model; GLM) and "force" the model with climate scenarios of their own design to test hypotheses about how lakes may change in the future. Once students have mastered running one climate scenario for their lake, they will learn how to use distributed computing tools to scale up and run hundreds of different climate scenarios for their lakes. The overarching goal of this module is for students to explore new modeling and computing tools while learning fundamental concepts about how climate change will affect lakes. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This dataset contains instructional materials and the files necessary to run the complete module. Readers are referred to the GLM science manual (Hipsey et al. 2014) for further details on model configuration.
Site environmental, climate, water levels and temperatures, vegetation cover, and GIS change detection for assessing permafrost change in fens on the Tanana Flats, central Alaska
This data package provides data used to assess the roles of climate extremes, ecological succession, and hydrology in repeated permafrost aggradation and degradation in fens on the Tanana Flats, central Alaska. The package provides data on site environmental information, Fairbanks climate, vegetation cover, water levels and temperatures, as well as GIS files for fen change detection. The Site data include information on observers, locations, geomorphology, hydrology, soils, vegetation, and disturbance. The table has numerous fields that uses coding for class characteristics and these codes are described in the metadata as well as compiled in the ELS_Arctic_Boreal_Site_Soil_Veg_Code_Sheet_2020.docx. Alaska Climate records for Fairbanks (UAF Experiment Station) from 1904 to 2019 were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/). Additional data were obtained for the Nenana station (about 70 km southwest of Fairbanks), to fill in small data gaps (particularly precipitation/snow depth ruler measurements) in the Fairbanks record. We attributed the data with fields for summer (May-September) and winter periods (November-March) and hydrologic year (October-September) and calculated mean air temperature, precipitation, and snow depth by seasonal period (average of daily values) and year. The broad summer and winter periods were of interest because warmer and wetter summers increase soil heat input and warmer and snowier winters reduce soil heat loss. Fen hydrology data include information on fen water level/pressure and temperatures collected every two hours at seven sites within fens from 2011 to 2014. Vegetation composition and cover of fens, scrub, and forests was sampled to assess effects of thermokarst on vegetation change. Plant cover was determined by point-sampling at 100 points (including repetitive “hits” for all layers) distributed along 5 equally spaced rows (4-m long, 20 points per row) across the 10-m l
Changes in water temperature in streams with progressive hemlock mortality at 8 Coweeta Hydrologic Laboratory study sites from 2004 to 2013
The purpose of this study was to document changes in water temperature in 8 streams in areas affected by hemlock mortaity. Beginning in August 2004, one temperature logger was submerged at the downstream end of each of 8 small stream sites. Temperature was recorded a minimum of every 4 h. This study was conducted at Coweeta Hydrologic Laboratory, Otto, North Carolina. Stream sites were located on 1st and 2nd order streams reaches affected by hemlock death. Six sites were located in areas that have not been logged since the area became National Forest in the late 1920s and where streams passed through or were adjacent to permanent vegetation plots in which trees were measured in 1934-35, 1969-73 and 1988-93 (Elliott and Swank, 2008). Also included are data from two sites on WS 7 (Big Hurricane Branch), which was logged in 1977.
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: growth and enzyme activity traits of soil fungi isolated from CCASE in July 2017, grown under a common garden experiment in the laboratory that mimicked CCASE soil temperature treatments
Projections for the northeastern U.S. indicate that mean air temperatures will rise and snowfall will become less frequent, causing more frequent soil freezing. To test fungal responses to these combined chronic and extreme soil temperature changes, we conducted a laboratory-based common garden experiment with soil fungi that had been subjected to different combinations of growing season soil warming, winter soil freeze/thaw cycles, and ambient conditions for four years in the field. We found that fungi originating from field plots experiencing a combination of growing season warming and winter freeze/thaw cycles had inherently lower activity of acid phosphatase, but higher cellulase activity, that could not be reversed in the lab. In addition, fungi quickly adjusted their physiology to freeze/thaw cycles in the laboratory, reducing growth rate and potentially reducing their carbon use efficiency. Our findings suggest that less than four years of new soil temperature conditions in the field can lead to physiological shifts by some soil fungi, as well as irreversible loss or acquisition of extracellular enzyme activity traits by other fungi. These findings could explain field observations of shifting soil carbon and nutrient cycling under simulated climate change. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Soil Temperature, Soil Frost, and Snow Depth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025
Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes soil temperature (winter 2021-2022) and snow depth and frost depth (winter 2022-2023) at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data for: Long-term body size change in multiple landbird species, long-term change in temperature and precipitation as well as associations between temperature, precipitation, and morphological change in multiple landbird species, 2004 – 2019, 2021 - 2022.
<p>Six data sets used to look for long-term change in precipitation and temperature, body size change and possible environmental drivers of morphological change in birds captured during spring or fall migration in and around Lackawanna State Park, northeastern Pennsylvania, USA.</p> <p>The file labeled daily_temp_precip.csv contains daily precipitation and average daily temperature data from the Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA) and the file called daily_temp_precip_1400 contains daily precipitation and daily temperature data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude.</p> <p>The file called band_data_final.csv contains data collected from the first capture of individuals of multiple species during spring or fall migration, the file called all_hy_env_morph.csv contains temperature and precipitation anomaly data from Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA), as well as morphological data from the first capture of all fall migrating young of the year.</p> <p>The file called all_hy_env_morph_1400.csv contains temperature and precipitation anomaly data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude as well as morphological data from the first capture of all fall migrating young of the year while the file called local_hy_env_morph.csv contains temperature and precipitation anomaly data as well as first capture of local young of the year.</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.